{"paper_id":"2763e6db-bf84-4831-ba6d-b7d37e75771e","body_text":"Spatiotemporal Patterns and Drivers of Population–Transport Coordination in the Pearl River Delta | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatiotemporal Patterns and Drivers of Population–Transport Coordination in the Pearl River Delta Di Lyu, Weiwang Zhu, Libin Ouyang, Zhaoya Gong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7168229/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The demographic–transport nexus is central to regional integration, but remains insufficiently studied in rapidly urbanizing contexts. Taking China’s Pearl River Delta (PRD) as a representative megaregion, this study uses panel data from nine PRD cities spanning 1990 to 2020. We construct an entropy-weighted indicator system and apply a coupling–coordination model in combination with spatial Durbin regressions to trace the co-evolution of population and transport systems and identify their driving forces. Findings reveal that: (1) the regional coupling-coordination index rose from 0.21 to 0.54 but still shows a clear core–periphery gradient—Guangzhou and Shenzhen already display high coordination, whereas ZhaoQing and Jiangmen lag behind; (2) economic growth, a consumption-oriented economic structure and technological progress significantly enhance coordination; (3) the 2009 PRD Master Plan mainly benefits core cities, with limited policy spill-overs; (4) medical-service provision improves coordination, while basic-education supply is not significant, highlighting service-level disparities. We recommend strengthening peripheral inter-city corridors, building 30- to 60-minute commuting rings, and linking transport investment to real-time coupling metrics and coordinated industry relocation to advance the region toward higher-level integration. Humanities/Complex networks Social science/Complex networks Social science/Development studies Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Coupling coordination Transport infrastructure Population dynamics Pearl River Delta Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction The coordinated development of population and transportation systems is of profound significance for the sustainable growth of cities and regions worldwide (Shen et al., 2024 ). The coupling and coordination between transportation systems and population dynamics has been widely recognized as a key indicator of urban agglomeration competitiveness and governance effectiveness (Ma et al., 2019 ; Tao, 2019 ). Transportation infrastructure not only determines the speed and cost of the movement of people and goods, but also acts as an amplifier in resource reallocation, industrial spatial layout, labor market integration, and regional innovation diffusion (Q. Du et al., 2022a ). Simultaneously, population size, structure, and spatial distribution critically shape travel demand, infrastructure supply pressures, and transportation-related energy consumption patterns (J. Liu et al., 2023 ; S. Wang et al., 2017 ). Achieving a dynamic match between population change and transportation supply-demand is essential for metropolitan areas to enhance economic competitiveness while ensuring environmental resilience and social equity (Q. Wang et al., 2021 ). Therefore, deconstructing the co-evolution mechanism between population and transportation systems carries significant strategic value for balancing the “resources–environment–society” triad. The Pearl River Delta (PRD), as one of China’s most economically vibrant and structurally complex polycentric urban agglomerations (Tong et al., 2014 ), presents a critical case where the spatial coupling between transportation and population not only influences intra-regional industrial division and functional hierarchy but also shapes the integration of the Guangdong–Hong Kong–Macau Greater Bay Area and the configuration of outward-oriented economic corridors. A deeper examination of the coordination mechanism and spatial heterogeneity between transportation and population in the PRD (T. Li et al., 2024 ) would provide scientific evidence for clarifying investment priorities, optimizing the provision of services to resident and floating populations, and informing the strategic planning of multilevel transport hubs—thereby yielding both theoretical value and policy significance (Z. Liao & Liang, 2024 ). Extant literature has explored the coordinated evolution of population and transportation from multiple dimensions. First, many macro-statistical studies have highlighted the siphon effect between transport investment and urbanization, whereby the expansion of transport corridors accelerates population concentration in core cities and reshapes spatial structures, forming a virtuous cycle of “greater transport–greater cities” (Han et al., 2023 ; Song et al., 2023 ). Second, the increasing availability of big data from mobile signaling, GPS trajectories, and social media has enabled scholars to investigate individual migration and commuting behaviors at the micro level, confirming that marginal improvements in accessibility can alter job-housing matching patterns and subsequently affect population mobility and urban functional differentiation (Y. Liao et al., 2019 ; Peng et al., 2019 ; R. Wang et al., 2022 ). Furthermore, researchers have introduced coupling coordination models, system dynamics frameworks, and spatial econometric techniques to quantitatively assess the interaction intensity, coupling elasticity, and spatial spillover effects between population and transportation systems (J. He et al., 2024a ; Wei et al., 2025 ; C. Wu et al., 2020 ). Despite these advances, several critical limitations remain in the study of population–transportation coupling (S. Huang & Lin, 2025 ; Wan et al., 2023 ). First, in terms of spatiotemporal scale, most existing studies are limited to short-term cross-sectional or single-city analyses and lack systematic investigation of long-term coupling trajectories across administrative boundaries in polycentric urban agglomerations, hindering the identification of turning points and stage-specific patterns. Second, in terms of mechanism explanation, current research frameworks still focus heavily on the linear relationship between transportation investment and population agglomeration, with insufficient integration of multi-dimensional drivers such as industrial upgrading, technological diffusion, and policy shocks—thus failing to fully reveal potential synergistic or suppressive effects. Third, regarding generalization and scalability, existing findings often remain at the level of single-case or local policy summaries, with inadequate discussion on regional differences and spatial spillover mechanisms, limiting their utility in guiding inter-regional collaborative governance. To address these gaps, this study focuses on the PRD urban agglomeration—China’s most economically dynamic region with pronounced cross-jurisdictional characteristics—and constructs a long-term panel dataset (1990–2020, five-year intervals) across nine cities. It develops a comprehensive index system encompassing five dimensions: population, transportation, economy, environment, and policy. The study aims to achieve the following objectives: (1) quantify the spatiotemporal evolution trajectory of population–transportation coupling coordination in the PRD, identifying inflection points and regional divergence across development stages; (2) assess the marginal contribution and elasticity of key drivers—including GDP level, industrial structure, technological innovation, urban attractiveness, regional planning, hub transport strategies, and population policies—in determining the level of coordination; (3) extract generalizable strategies for promoting integrated and resilient regional development, offering operational insights for other polycentric urban areas. In terms of methodology, the study first applies the entropy weight method to objectively assign indicator weights and eliminate subjective bias. It then calculates coupling coordination degrees to quantify system-level interactions between population and transportation. On this basis, a fixed-effects panel regression model is employed to identify the marginal effects of economic, technological, and institutional variables. Robustness and heterogeneity are tested using spatial visualization and quantile regression. Compared to existing literature, this study contributes in three main areas:First, through long-term cross-regional data integration, it establishes a comprehensive indicator system spanning five dimensions and systematically tracks the coupling evolution across nine cities in the PRD from 1990 to 2020, addressing gaps in macro-scale and longitudinal studies of cross-jurisdictional urban agglomerations.Second, it integrates coupling assessment with econometric modeling, combining entropy weighting and coordination index calculations with fixed-effects and quantile regressions to form a replicable methodological framework for evaluating coordination levels, identifying mechanisms, and testing robustness.Third, by explicitly incorporating institutional variables, such as regional planning, transport hub designations, and population policies, into the analytical framework, it quantifies their marginal contributions and elasticity, offering empirical evidence and policy references for differentiated investment, spatial reconfiguration of population, and integrated governance—thereby informing transferable strategies for transportation–population coordination in polycentric regions globally. The remainder of this paper is organized as follows: Section 2 builds the theoretical framework and research hypotheses for transportation–population interaction. Section 3 introduces data sources, indicator system, and methodological design, including entropy weighting, coordination index calculation, and panel model construction. Section 4 presents empirical results, revealing the spatiotemporal patterns and typologies of coordination in the PRD. Section 5 analyzes the marginal elasticity of economic, technological, service-related, and policy variables. Section 6 concludes by summarizing theoretical and practical contributions, proposing policy implications for network optimization, spatial restructuring, and institutional collaboration, and outlining future research directions. This study ultimately deepens the understanding of regional transportation–population dynamics and supports high-quality, integrated development strategies. 2 Literature review The evolution of population in the process of urbanization is essentially driven by the interaction of three forces: natural growth, spatial migration, and socioeconomic restructuring (H. Wu et al., 2024 ; Zou & Deng, 2022 ). Classical demographic transition theory suggests that as a country or region enters the mid-to-late stages of industrialization, birth and death rates tend to converge, and the growth of urban populations becomes increasingly dependent on migration flows rather than natural increase (Skeldon, 2012 ). With improvements in transportation accessibility, widening urban–rural income disparities, and the spillover effects of information networks, labor forces tend to migrate along gradients from capital to employment and then to infrastructure, concentrating progressively in urban centers and secondary nodes. This process displays cyclical fluctuations characterized by concentration, dispersion, and re-concentration (Franconi et al., 2024 ; Y. Liu et al., 2024 ). Migration not only alters the quantity and density of urban populations but also profoundly reshapes employment structures and the spatial distribution of industries (Q. He et al., 2023 ). During the rapid urbanization phase dominated by manufacturing, the secondary sector had a significant labor-absorbing effect, drawing large numbers of surplus rural laborers into industrial clusters. In the subsequent post-industrial stage, dominated by the service economy, the expansion of the tertiary sector and knowledge-intensive industries has redirected population flows toward core business districts and innovation clusters (X. Zhao, 2024 ). In North America and Western Europe, the mid-20th century witnessed the rise of suburbanization fueled by the expansion of interstate highways and mortgage finance, leading to classic patterns of decentralization as urban populations dispersed to the suburbs, followed by reurbanization-induced returns (Garcia-López et al., 2024 ; Weiner, 2008 ). In Japan, during the formation of the industrial triangle, the development of the Shinkansen and national highway networks prompted massive rural youth migration toward the Tokyo–Nagoya–Osaka corridor, resulting in a highly centralized monocentric pattern (Masahiko, 2022 ). In China, the Pearl River Delta and Yangtze River Delta regions have exhibited a more complex cycle of population concentration, dispersion, and re-concentration since the Reform and Opening-Up. In the 1990s, export-oriented manufacturing triggered explosive growth in secondary core cities such as Shenzhen and Suzhou, while improvements in high-speed rail and industrial upgrading have since driven population reflux toward primary cores such as Guangzhou–Shenzhen and Shanghai–Hangzhou, reflecting the strong shaping influence of transport accessibility on cyclical population agglomeration (Zhu, 2021 ). Structural transformation and quality upgrading constitute the deep-seated drivers behind dynamic population changes. The concentration of surplus labor from non-urban areas into the secondary sector has accelerated the industrial gradient upgrading process (S. Wu et al., 2023 ). Meanwhile, the expansion of the service economy and knowledge-intensive industries has guided population redistribution toward multifunctional urban nodes, thereby restructuring employment structures and land-use patterns (C. Tang & Dou, 2022 ). The Silicon Valley phenomenon in the United States demonstrates how the tertiary sector and high-tech clusters reshape employment structures by attracting global migrants through high-skill jobs, thereby enhancing regional educational attainment and income distribution (Adams, 2021 ; Erkel, 2023 ). Cross-national population tracking data reveal that the inflow of high-skilled labor into major cities not only improves individual education levels but also generates strong knowledge spillover effects that significantly boost local innovation output and wage levels (R. Guo et al., 2022 ; C. Wang et al., 2022 ). At the same time, institutional constraints—such as household registration and land systems—exert significant moderating effects on migration decisions. In China, the gradual relaxation of household registration entry policies after 2014 led to a cumulative five-year increase in the resident population of typical second-tier cities (with populations between one and five million) that was approximately 9–11% higher than in the equivalent period prior to reform. This effect was most pronounced in cities with a high share of labor-intensive manufacturing and relatively moderate housing prices (X. Guo & Xu, 2025 ). Overall, population evolution during urbanization follows a progressive chain of scale expansion, structural differentiation, and quality upgrading. However, variations in transportation investment timing, the pace of industrial upgrading, and the strength of institutional regulation across different countries have jointly shaped the diverse feedback mechanisms of population dynamics on transport demand, spatial structure, and social stratification (Seifollahi-Aghmiuni et al., 2022 ). Although the existing literature has systematically revealed these patterns, insufficient attention has been paid to the temporal feedback mechanisms by which population changes affect transportation demand, and to the elasticity differences among urban concentric zones. Further exploration of these issues is of significant theoretical and practical value for understanding the co-evolution of population and transport systems in polycentric urban agglomerations, optimizing regional resource allocation, and formulating fine-grained governance strategies (H. Wang et al., 2024 ). 2.2 Supply and demand for transportation infrastructure The supply system of transport infrastructure defines the material boundaries for the movement of people and goods. Its hierarchical network, nodal connectivity, and overall accessibility are widely recognized as key preconditions shaping the spatial structure of regions and the evolution of growth poles (Colon et al., 2021 ). Cross-national econometric studies have shown that increased infrastructure density can enhance labor productivity in growth regions, with the effect declining along a clear gradient from hub cities to their peripheries (Dinlersoz & Fu, 2022 ; Nkemgha et al., 2023 ). In China, the rapid rollout of high-speed rail and expressway networks under the “Eight Vertical and Eight Horizontal” framework has reduced average commuting time by 34.5% in key economic corridors, forming one-hour commuting circles in the Pearl River Delta, Yangtze River Delta, and Beijing-Tianjin-Hebei regions—thus validating infrastructure’s accelerating role in urban network formation (Jin et al., 2017 ; L. Liu & Zhang, 2021 ). In contrast, Sub-Saharan Africa and parts of South Asia have long faced challenges of underinvestment, poor maintenance, and limited accessibility. According to World Bank reports, the average serviceable mileage of trunk and secondary roads in these regions is only about 50% of the global average, with less than 30% rated as being in good condition. This restricts market hinterlands, locks peripheral areas into constraints of time and distance, and significantly raises logistics costs (Calderón et al., 2018 ; Muzira & Qiao, 2022 ). Inadequate transport supply weakens peripheral regions’ accessibility to core markets, exacerbating center-periphery developmental divides and forming classic patterns of spatial exclusion and path-dependent poverty traps (Jaramillo Lizana, 2025 ). A review of diverse developmental trajectories reveals that a hierarchically complete and spatiotemporally balanced transport supply system is a crucial precondition for supporting regional economic spatial restructuring and the formation of polycentric networks (Singer et al., 2022 ; Yoo et al., 2024 ). In contrast to the grand narratives of supply-side planning, demand-side transport analysis focuses more on behavioral mechanisms and the evolution of predictive methods (H. Wang et al., 2023 ). Traditional regional-scale models, such as gravity models or four-step models, have been widely used for static predictions of passenger and freight flows. However, they often oversimplify dynamics in polycentric and time-varying networks (Yao & Sun, 2013 ; Y. Zhao et al., 2018 ). In the era of big data, mobile signaling, GPS trajectories, ride-hailing order data, and social media check-ins have provided high spatiotemporal resolution flow maps. These enable fine-grained quantification of demand differences, latent bottlenecks, and network resilience—forming a robust data foundation for multi-scale decisions on strategic planning and real-time dispatching (W. Chen et al., 2024 ; S. Guo et al., 2024 ; Y. Liu, Jia, et al., 2022 ). For instance, researchers in Shenzhen integrated ride-hailing orders, taxi GPS trajectories, and smart subway card data to construct a mixed geographically weighted regression model that reveals a power-law decay relationship between CBD vitality and 15-minute accessibility (J. Tang et al., 2021 ). Simultaneously, machine learning and graph neural networks have been increasingly applied to demand forecasting. These tools integrate multi-source real-time data, capture nonlinear and topological dependencies in both time and space, and enable continuous learning and adaptive updating of network states. They offer new paradigms for analyzing demand elasticity and propagation mechanisms under sudden disruptions by dynamically correcting traffic forecasts and quantifying network resilience (F. Huang et al., 2022 ; Jiang & Luo, 2022 ). Supply-demand mismatches remain a shared challenge in most rapidly urbanizing regions. On one hand, large-scale infrastructure investment often lags behind fast-growing demand, leading to congestion and negative externalities; on the other hand, premature investments risk asset underutilization and fiscal burdens (Persyn et al., 2023 ). Meanwhile, rapidly evolving demand, coupled with technological transitions, imposes structural adjustment pressures on existing networks (Ercan et al., 2017 ). Empirical experience from Europe and the U.S. shows that synchronizing land use with network evolution through accessibility analysis and transit-oriented development (TOD) strategies can smooth supply-demand curves over a decadal scale (Staricco & Vitale Brovarone, 2018 ). The Bus Rapid Transit (BRT) systems of Latin America offer an alternative path—demonstrating that commuting efficiency can be significantly improved even at relatively low cost (Vergel-Tovar & Rodriguez, 2018 ). Overall, research on transport infrastructure supply and demand has shifted from static scale expansion to dynamic network optimization and behavior-sensitive modeling. However, comprehensive empirical exploration remains urgently needed in areas such as cross-jurisdictional coordination in polycentric urban agglomerations, tiered service balancing, and the application of digital twin platforms. 2.3 Interaction between transportation and demographic At the macro level, numerous cross-national studies have confirmed the leading and feedback relationships between transport infrastructure and the spatial redistribution of population (Gutiérrez et al., 2010 ; Pokharel et al., 2023 ). First, transport infrastructure such as trunk highways, intercity railways, and multimodal transport corridors compress intercity time–space distances, reduce migration and commuting thresholds, and enhance core cities’ attraction to labor and capital (Duan et al., 2021 ; Z. Li et al., 2024 ). Conversely, the resulting agglomeration-induced increase in travel demand stimulates the expansion and hierarchical differentiation of transport networks, forming a cumulative cycle of transport investment, population concentration, and subsequent network upgrading (Y. Yang et al., 2022 ). For instance, empirical studies based on the Minneapolis–St. Paul metropolitan area reveal significant spatial heterogeneity in job accessibility along rail corridors. The difference in accessibility between urban centers and competing employment clusters in surrounding areas is identified as a key factor affecting job acquisition and commuting efficiency for low-income groups (Mitropoulos et al., 2023 ). High-speed rail travel surveys in the Chengdu–Chongqing city cluster show that station accessibility significantly influences travel behavior in smaller cities. As station distance decreases, the probability of high-speed rail travel increases markedly, along with a notable rise in travel frequency (Cao et al., 2025 ). Micro- and meso-level perspectives further illuminate the real-time feedback mechanisms between travel behavior and job–housing relationships within the coupled transport–population system (Macias et al., 2025 ; Zhong et al., n.d.). Multi-agent simulations based on mobile signaling and public transit smart card data suggest that improved transport accessibility expands individuals’ employment opportunity radii (Zhou & Yang, 2021 ). In terms of quantitative methods, spatial panel models, coupling coordination indicator systems, and PVAR (panel vector autoregression) models are widely applied to measure interaction intensity and lag elasticity (R. Yang et al., 2023 ; X. Zhang & Zhang, 2022 ). Overall, the transport–population relationship exhibits bidirectional coupling across multiple scales and pathways: at the macro scale as a cumulative cycle between network density and population agglomeration, at the meso scale as dynamic restructuring of the job–housing spatial structure, and at the micro scale as real-time behavioral feedback to transport supply changes in individual travel choices. In sum, the transport and population systems are characterized by multiscale and multipath bidirectional coupling. 2.4 Factors influencing the coordination of transportation and demographic Within existing research frameworks, scholars generally regard economic and industrial fundamentals as the primary exogenous forces driving the coordinated evolution of transport and population systems (Y. Wang, Zou, et al., 2022 ). First, robust economic growth expands the local government’s tax base, enhances multilateral financing capacity and capital returns, and thereby triggers larger-scale and higher-quality transport investments (L. Zhao & Jia, 2021 ). Empirical evidence from the Egnatia Odos Motorway suggests a significant long-term bidirectional causal relationship between transport efficiency and regional GDP, with the elasticity of infrastructure’s contribution to economic growth increasing alongside industrial upgrading (Magoutas et al., 2023 ). Second, shifts in industrial structure reshape the spatial distribution of labor and production factors. High-tech industries and modern services demand greater travel efficiency for business and commuting, thereby driving demand-led expansion of high-speed railways and multimodal corridors (Y. Wang, 2022 ). In China’s Yangtze River Delta, the shift of manufacturing toward R&D and design has significantly intensified reliance on the Shanghai–Nanjing intercity railway, where the development of high-speed rail has improved rail accessibility by approximately 50% (Sun et al., 2021 ). Meanwhile, the digital economy and automation technologies have reduced the marginal cost of transportation and enhanced network resilience, further unlocking mobility potential within metropolitan areas (Vieira et al., 2022 ). These trends underscore the roles of macroeconomic vitality, industrial specialization, and technological innovation as core drivers of synchronized advancement in transport capacity and population concentration (Magazzino & Mele, 2021 ). Further studies highlight the mediating and moderating roles of institutional environments and urban attractiveness in shaping transport–population coupling (Chen J. & Pan, 2024 ; T. Zhang et al., 2023 ). First, spatial planning and investment–financing mechanisms determine the sequencing and layout of transport infrastructure (Komornicki & Szejgiec-Kolenda, 2023 ). Second, public service accessibility, ecological quality, and cultural and recreational amenities together form a multidimensional index system of urban attractiveness (X. Li et al., 2022 ). Governance heterogeneity also generates institutional effects in the transport–population interaction system. In Chinese prefecture-level cities with higher degrees of fiscal decentralization, a stronger coupling coordination is observed between rail transit development and resident population growth (Z. Liu et al., 2024 ; Lu et al., 2021 ). Moreover, environmental constraints are reshaping investment priorities. For example, the EU’s Green Corridors Initiative incorporates carbon reduction performance as a threshold for funding allocation. The resulting low-carbon capital orientation has altered the spatial configuration of infrastructure investments, influencing intercity population agglomeration and dispersion through differential accessibility and factor mobility (Kozera et al., 2024 ). Current research has preliminarily outlined the theoretical landscape of transport–population coupling from four perspectives: population dynamics, transport supply and demand, cross-scale interaction mechanisms, and exogenous driving forces. However, there remains a lack of systematic evidence regarding the long-term coordination trajectories and quantifiable institutional effects within cross-jurisdictional, polycentric urban agglomerations. This creates opportunities for integrated research based on long-term panel data and multi-source big data. Building on this gap, the present study aims to further explore influencing factors and provide theoretical support for urbanization strategies in developing countries. 3 Data and Methodology 3.1 Research Framework This study constructs an integrated research framework encompassing indicator development, dynamic evaluation, and spatial econometric diagnosis (Fig. 1 ), aiming to systematically analyze the coupling mechanisms between population and transportation. First, a hierarchical indicator system is established based on system interaction mechanisms, with indicator weights determined through a combination of the Analytic Hierarchy Process (AHP) and the entropy weight method, balancing expert judgment and objective data. Second, data from nine prefecture-level cities in the Pearl River Delta from 1990 to 2020 are range-standardized to compute comprehensive indices for both population and transportation systems. A coupling coordination degree model is then applied to jointly measure coupling intensity and synergistic performance. The results are categorized into five levels of coordination types at 0.2 intervals to depict their spatiotemporal patterns. Finally, a fixed effects panel regression model is employed to identify the direct effects of economic, environmental, and policy factors on the coupling degree. Subsequently, a Spatial Durbin Model (SDM) is introduced to capture the spatial spillover effects of natural, technological, and institutional variables. This framework integrates indicator assessment, temporal dynamics, and spatial econometric perspectives, offering precise and operational decision-making support for coordinated population–transport planning. 3.2 Data sources and processing Taking into account the accuracy, accessibility, and temporal continuity of the data, this study collected population, transportation, and economic data from 9 prefecture-level cities over a span of 7 years, from 1990 to 2020, with a 5-year interval. The population data was sourced from the Guangdong Provincial Census and the 1% population sampling survey data, while the transportation and economic data were obtained from the Guangdong Provincial Statistical Yearbook and the statistical yearbooks of the respective cities. In cases of missing data, interpolation was performed using the growth rate method. To mitigate the impact of dimensionality on data calculations and comparative analysis, the maximum-minimum method was employed to standardize the raw data prior to data analysis. Specifically, the equation is as follows: $$\\:{X}_{ij}=\\frac{{r}_{ij}-\\text{m}\\text{i}\\text{n}\\left({r}_{ij}\\right)}{\\text{max}\\left({r}_{ij}\\right)-\\text{m}\\text{i}\\text{n}\\left({r}_{ij}\\right)}$$ 1 Where \\(\\:{X}_{ij}\\) stands for the normalised value, while \\(\\:{r}_{ij}\\) represents the raw data. 3.3 The entropy method The entropy method was utilized in this study to determine the weights of different indicators associated with demographic and transportation. As an objective approach, it mitigates the influence of human factors and is extensively employed in system evaluation research. Grounded on the concept of information entropy, the entropy method calculates the entropy of indicator values to capture their significance and variances, consequently determining their weights(B. Wu et al., 2023 ). The fundamental principle of the entropy method lies in the notion that indicators with higher entropy values demonstrate more pronounced differences and impact on decision-making outcomes, indicating a higher weight. The calculation procedures of the entropy method are as follows: (1) Calculate the proportion of the standardize data: $$\\:{p}_{ij}=\\frac{{X}_{ij}}{\\sum\\:_{j}{X}_{ij}}$$ 2 (2) Calculate the entropy of each indicator: $$\\:{e}_{i}=-\\frac{1}{lnn}\\sum\\:_{j}{p}_{ij}ln{p}_{ij}$$ 3 (3) Calculate the weights of each indicator: $$\\:{w}_{i}=\\frac{1-{e}_{i}}{{\\sum\\:}_{i}(1-{e}_{i})}$$ 4 In the above formulas, i and j denote the ordinal numbers of indicators and observations, respectively, n is the number of observations and \\(\\:{X}_{ij}\\) represents the standardize data. 3.4 Coupling coordination degree model The widely adopted model for assessing the collaborative development level of different systems is the coupling coordination degree model(N. Li et al., 2023a ; Zhuang et al., 2024 ), which has been applied in this study to analyze demographics and transportation. Coupling refers to the interrelation and interaction between systems, while coordination refers to the degree of synergy and mutual development. The purpose of the coupling coordination degree model is to evaluate the overall operational status and level of coordinated development by quantifying and analyzing the degree of coupling and coordination between systems. In this study, the coupling analysis of demographic and transportation systems focuses on examining the relationship between changes in population size and structure and the construction of transportation infrastructure. The calculation procedures for the coupling coordination degree model are as follows: (1) Calculate coupling degree between demographic and transportation systems of city k in year t : $$\\:C(k,t)=2\\sqrt{\\frac{f\\left(P\\right)\\times\\:g\\left(T\\right)}{{(f\\left(P\\right)+g\\left(T\\right))}^{2}}}$$ 5 (2) Calculate coordination degree between demographic and transportation systems of city k in year t : $$\\:D(k,t)={\\alpha\\:}f\\left(P\\right)+\\beta\\:g\\left(T\\right)$$ 6 (3) Calculate coupling coordination degree between demographic and transportation systems of city k in year t : $$\\:T(k,t)=\\sqrt{C(k,t)\\times\\:D(k,t)}$$ 7 (4) Calculate coupling coordination degree between demographic and transportation systems of study area in year t : $$\\:T\\left(t\\right)=\\frac{1}{m}\\sum\\:_{k=1}^{m}T(k,t)$$ 8 In the above formulas, \\(\\:f\\left(P\\right)\\) and \\(\\:g\\left(T\\right)\\) represent the comprehensive evaluation index of demographic and transportation systems of city k in year t , respectively, and m is the number of cities. In order to assess the development and interaction level of demographic and transportation systems across various time periods and cities, the coupling coordination degree was graded according to previous research. This study categorized the coupling coordination degree into 5 main categories and 2 subcategories (Table 1 ). The main categories represented the overall level of coupling coordination, with each 0.2 level further divided into 5 levels. The subcategories compared the relative development levels of demographic and transportation, and were further divided into advanced transportation and lagging transportation types. Table 1 Classification of coordination types. T Coordination type g (T) > f (P) f (P) > g (T) 0.8 < T ≤ 1 High coupling Advanced transportation Lagging transportation 0.6 < T ≤ 0.8 Moderate coupling Advanced transportation Lagging transportation 0.4 < T ≤ 0.6 Low coupling Advanced transportation Lagging transportation 0.2 < T ≤ 0.4 Moderate uncoupling Advanced transportation Lagging transportation 0 < T ≤ 0.2 Severe uncoupling Advanced transportation Lagging transportation 3.5 Panel data regression Panel data regression analysis is a method that uses panel data to analyze the relationship between independent variables and dependent variables. It has the advantage of being able to control for the influence of unobserved variables that do not vary over time. Panel data regression methods mainly include fixed effects models and random effects models. The fixed effects model assumes the presence of unobserved fixed effects among individuals, while the random effects model allows for random effects among individuals. In this study, the fixed effects model was used to explore the factors influencing the degree of coupling coordination between demographics and transportation. The regression model is as follows: $$\\:{T}_{it}=\\alpha\\:+\\beta\\:{ECO}_{it}+\\gamma\\:{ENV}_{it}+\\gamma\\:{POL}_{it}+{FE}_{i}+ϵ$$ 9 In the equation, \\(\\:{T}_{it}\\) represents the coupling coordination degree between demographic and transportation, while \\(\\:{ECO}_{it}\\) , \\(\\:{ENV}_{it}\\) and \\(\\:{POL}_{it}\\) represent a series of variables related to economic, environmental and policy factors respectively. \\(\\:{FE}_{i}\\) denotes the fixed effects, which are related to the city and do not changing over time. 4 Model 4.1 The index system for demographic Population urbanization is reflected in various aspects, including spatial aggregation of the population, an increase in the proportion of non-agricultural population, and an improvement in residents’ living standards. Referring to existing studies on the selection of demographic indicators(Ren et al., 2022 ; Y. Zhang et al., 2023 ), this study selected six commonly used indicators to represent the demographic in terms of population size, population density, urban-rural structure, employment structure, population quality, and living standards. Table 2 provides a detailed explanation of the meanings associated with each indicator. To assess the rationality of the indicator selection, the average correlation coefficients among the indicators were calculated using 2020 data. The results, shown in Table 3 , indicate that the average correlation coefficient for each indicator within the population system does not exceed 0.7, suggesting that the selected indicators possess a certain level of representativeness. Table 2 Detailed description of demographic system indicators. Subsystem Indicator Description Population size Total population Permanent population size Population density Population density Permanent population divided by administrative area Urban-rural structure Urbanization rate Urbanization rate of permanent population Employment structure Industrial structure Proportion of population in secondary and tertiary industries Population quality Educational level Proportion of population with college education and above Living standard Disposable income Disposable income of permanent population Table 3 The average correlation coefficient for each indicator within demographic system. Indicator Total population Population density Urbanization rate Correlation 0.58 0.62 0.68 Indicator Industrial structure Educational level Disposable income Correlation 0.64 0.68 0.69 4.2 The index system for transportation In evaluating urban transportation levels, previous studies have comprehensively considered the infrastructure construction of various transportation modes, including highways, railways, waterways, and aviation(Dong et al., 2021 ; Pradhan et al., 2021 ). Different regions may exhibit structural differences in transportation modes. Referring to previous research, this study categorized the transportation system into five subsystems: highways, railways, waterways, aviation, and management. These subsystems encompass a total of 19 evaluation indicators (Table 4 ). By utilizing 2020 data, the average correlation coefficients among indicators within the five subsystems were calculated. As shown in Table 4 , all average correlation coefficients do not exceed 0.7, indicating that the selected indicators demonstrate good representativeness. Table 4 The average correlation coefficient of transportation system indicators. Subsystem Indicator Average correlation coefficient Highway Grade highway mileage 0.19 Grade highway network density 0.24 Highway mileage 0.53 Highway network density 0.42 Highway passenger volume 0.37 Highway passenger turnover 0.53 Highway freight volume 0.60 Highway freight turnover 0.62 Railway Railway passenger volume 0.60 Railway passenger turnover 0.57 Railway freight volume 0.50 Railway freight turnover 0.43 Water Water transport passenger volume 0.11 Water transport freight volume 0.57 Aviation Civil aviation passenger volume 0.59 Civil aviation freight volume 0.55 Number of airports 0.29 Management Proportion of transportation workers 0.33 Per capita transport fixed assets investment 0.34 4.3 The variables that affect coupling coordination The factors that influence the degree of coupling coordination between demographic and transportation systems mainly include economic factors, environmental factors, and policy factors(Y. Liu, Nath, et al., 2022 ; Sung & Eom, 2024 ). In terms of economic factors, GDP is the most direct variable for measuring urban economic development levels. Developed areas have a strong attraction for populations and possess sufficient funds to construct regional transportation infrastructure, which significantly promotes the development of both demographic and transportation systems. The industrial structure has a significant impact on regional population flow patterns, and an increase in the proportion of non-agricultural employment will bring more job opportunities. Differences in economic structure may create differentiated development dynamics for urban population and economic development. Fixed asset investment promotes urban economic development by expanding industrial scale, while fiscal expenditure and resident consumption enhance economic vitality by facilitating economic circulation. This study constructs an index that reflects urban economic structure by using the ratio of the sum of urban fiscal expenditure and resident consumption to fixed asset investment. Additionally, this study selects the number of patent authorizations per ten thousand people as a variable reflecting urban technological levels. Cities with higher technological levels will generate more high-tech jobs and attract more high-quality talent. At the same time, the rapid flow of factors also relies on efficient transportation infrastructure, which contributes to the coordinated development of demographic transportation systems. The urban environment represents the attractiveness of a city and is an important factor influencing population inflow(R. Du et al., 2024 ). Urban attractiveness encompasses various aspects, including healthcare, education, and greenery. This study selects four indicators to reflect these aspects: the number of doctors per 10,000 people, the number of medical beds per 10,000 people, the number of primary and secondary school teachers per 10,000 people, and the urban greening rate. Regarding policy factors, this study considers the impact of regional development policies, transportation development policies, and population policies on the coupling coordination of population and transportation systems. In 2009, the Outline of Reform and Development Plan for the Pearl River Delta Region was released, proposing regional development requirements and guidelines in terms of population carrying capacity, industrial development, and facility construction. The regional development policy variable uses a time dummy variable, set to 1 after 2009 and 0 before 2009. In the comprehensive transportation system plan for the Pearl River Delta, Guangzhou, Shenzhen, and Zhuhai were designated as regional transportation hubs, playing significant roles in the regional transportation system. The transportation development policy variable uses a regional dummy variable, with Guangzhou, Shenzhen, and Zhuhai set to 1 and other cities set to 0. Considering that Guangdong Province has fully relaxed the two-child policy since 2018, the population policy variable uses a time dummy variable, set to 1 after 2018 and 0 before 2018. Descriptive statistics of the variables are shown in Table 5 . To avoid multicollinearity among variables, those with VIF values greater than 10 were removed, including the industrial structure and healthcare service level_1. Since the model already incorporates individual fixed effects, this study replaced the original variables with interaction terms for transportation development policy and regional development policy. Table 5 Descriptive statistics of variables. Variable Description Observation Mean Standard deviation Minimum Maximum Coupling coordination degree Coupling coordination degree between population and transportation system 63 0.37 0.17 0.12 0.85 GDP Logarithm of GDP 63 7.06 1.65 3.72 10.23 Industrial structure Ratio of secondary and tertiary industries 63 90.94 10.67 52.49 99.96 Economic structure Ratio of the sum of urban fiscal expenditure and resident consumption to fixed asset investment 63 1.67 0.84 0.68 4.57 Level of technological development Number of patent authorizations per 10000 people 63 19.05 28.63 0.00 127.13 Level of medical services_1 Number of doctors per 10000 people 63 18.29 7.17 4.98 33.37 Level of medical services_2 Number of medical beds per 10000 people 63 30.67 12.19 8.69 60.75 Level of educational services Number of primary and secondary school teachers per 10000 people 63 77.58 18.70 23.49 115.91 Level of urban greening Urban greening rate 63 37.90 7.39 19.27 57.94 Transportation development policy = 1 if the city is Guangzhou, Shenzhen, or Zhuhai = 0 if the city is another city 63 0.33 0.48 0.00 1.00 Regional development policy = 1 if the year is after 2009 = 0 if the year is before 2009 63 0.43 0.50 0.00 1.00 Population policy = 1 if the year is after 2018 = 0 if the year is before 2018 63 0.14 0.35 0.00 1.00 5 Results 5.1 The spatiotemporal differentiation of demographic and transportation systems From 1990 to 2020, the population aggregation capacity of PRD steadily increased, with its demographic system evolving through three phases. Figure 2 and Fig. 3 illustrate the changes in the comprehensive evaluation indices of the demographic system in PRD and in individual cities, separately. Overall, the comprehensive evaluation index of the demographic system in PRD rose from 0.09 in 1990 to 0.54 in 2020, indicating that PRD played a significant role in accommodating population growth over the past 30 years. The growth trends of the comprehensive evaluation indices of demographic system in individual cities varied. From 1990 to 2000, the reform and opening-up policy prompted cities such as Guangzhou, Shenzhen, Dongguan, and Foshan to undergo industrial transformation, developing export-oriented industries and creating a large number of job opportunities. At the same time, social changes weakened controls over the floating population, thus accelerating urban population aggregation. In contrast, cities like Zhaoqing, Jiangmen, and Huizhou developed more slowly during this period, influenced by the siphoning effect of the more developed cities. Between 2000 and 2015, PRD continued to see population aggregation and urbanization, with steady development of demographic system in each city. The level of development remained relatively stable, forming a clear hierarchy. Shenzhen and Guangzhou were in the first tier, Zhaoqing, Jiangmen, and Huizhou were in the third tier, while the remaining cities were classified in the second tier. From 2015 to 2020, the demographic system in PRD underwent leapfrog development, with ongoing optimization of the population structure and significant improvements in residents’ living standards, and the comprehensive evaluation indices of demographic system in each city increased substantially. Compared to the demographic system, the comprehensive evaluation index of the transportation system in PRD has grown relatively slowly. As shown in Fig. 2 , the comprehensive evaluation index of transportation system was only 0.21 in 2020, an increase of 0.17 compared to 1990. The higher concentration of transportation system resources in PRD reflects the central characteristics of transportation planning. Figure 4 illustrates the changes in the comprehensive evaluation indices of transportation system in each city of PRD from 1990 to 2020. Guangzhou serves as the primary transportation hub, followed by Shenzhen and Zhuhai, with their transportation system indices significantly leading the other cities. As the capital of Guangdong Province, Guangzhou holds significant political, economic, and cultural importance within the region. Since 2000, Guangzhou’s transportation system has developed rapidly, consistently outpacing other cities in transportation infrastructure. Shenzhen and Zhuhai, located along the coast near Guangzhou, have benefited from their advantageous geographic positions, resulting in relatively higher allocation of transportation resources. Since 2005, the development level of the transportation system in Shenzhen and Zhuhai has seen significant improvements. The indices of transportation system in the remaining six cities show a fluctuating growth trend and are notably lagging behind the top three cities. 5.2 Spatial and temporal changes in coupling coordination between demographic and transportation systems Figure 2 and Fig. 5 illustrate the changes in coupling coordination of demographic and transportation systems in PRD and individual cities from 1990 to 2020. Overall, the coupling coordination of demographic and transportation systems in PRD shows a steady upward trend. From 1990 to 2020, the coupling coordination increased from 0.21 to 0.54, gradually shifting from moderate uncoupling to low coupling. In terms of individual cities, the dynamic trends of the coupling coordination in different cities align with the overall changes, but there are significant differences in relative levels. Guangzhou consistently maintained the highest coupling coordination across all years, rising from 0.41 to 0.85. Shenzhen followed closely, with its coupling coordination increasing from 0.22 to 0.75. Zhuhai’s coupling coordination is slightly above the overall level, while the remaining cities below the overall level. Based on the standards in Table 1 , the coordination types of demographic and transportation systems in various cities of PRD have been classified, as shown in Fig. 5 . In this figure, the surface elements represent the classification results of the primary categories, while the point elements represent those of the secondary categories. Overall, PRD's coordination type of demographic and transportation systems exhibits a circular and stepped development pattern. According to the dynamic changes in the coordination type of each city, the PRD can be divided into three circles. Guangzhou and Shenzhen constitute the core circle, Foshan, Zhuhai, Dongguan, Zhongshan, and Huizhou constitute the peripheral circle, while Zhaoqing and Jiangmen comprise the outer circle. The change in the coordination type of demographic and transportation systems follows the direction of the core, peripheral, and outer circles. In 1995, with the exception of the two cities in the outer circle, all other cities were classified as moderate uncoupling or higher. In 2000, the cities in the outer circle upgraded to moderate uncoupling. In 2015, the core circle upgraded to moderate coupling, while the peripheral circle transitioned to low coupling, and the outer circle remained at moderate uncoupling. In 2020, Guangzhou was the only city in PRD to achieve high coupling, while Shenzhen and Zhuhai were classified as moderate coupling. Other cities in peripheral circle were all classified as low coupling, and the two cities in the outer circle were categorized as moderate uncoupling. From the perspective of the relative development of demographic and transportation systems, since 2005, the comprehensive evaluation index of Guangzhou transportation system has consistently exceeded that of its demographic system. In contrast, other cities in PRD have been classified as lagging transportation type. This disparity can be attributed to the allocation of transportation system resources, which are primarily concentrated in the core circle and not evenly distributed across all cities. 5.3 Factors related the coupling coordination of demographic and transportation Table 6 presents the results of the panel regression analysis. The adjusted R-squared value of the model exceeds 0.95, and it passed the F-test at a significance level of 0.1%, indicating that economic, environmental, and policy factors significantly influence the coupling coordination between demographic and transportation systems. GDP, economic structure, technological level, urban services, regional policies, and transportation policies all impact the coordinated development of demographic and transportation systems at different significance levels. Specifically, the coefficient of GDP is 0.0569, which indicates that at the 0.1% significance level, a 1% increase in GDP will lead to an average increase of 0.0569 in the coupling coordination. The coefficient for economic structure is positive at the 0.1% significance level, suggesting that a consumption-oriented socio-economic structure significantly promotes the coupling coordination. Technological level also significantly enhances the coupling coordination at the 0.1% significance level, although its effect is relatively weak, with a coefficient of only 0.0009. The coefficient for urban medical services is positive, while that for educational services is negative, indicating a differential impact of medical and educational facilities on the coupling coordination. The interaction term between regional development policy and transportation development policy has a coefficient of 0.0575, with a significance level of 0.1%, suggesting that since 2009, the coupling coordination in Guangzhou, Shenzhen, and Zhuhai has increased by an average of 0.0575 due to policy dividends. The study could not identify significant relationships of other influencing factors, which may be attributed to the limited sample size or unclear sample differences. Specifically, only the data from 2020 was affected by the two-child policy, resulting in a limited number of time series observations, making it impossible to quantify the relationship at this time. Additionally, the green space rate in cities is generally high, and with the provision of ample green space, the impact of increasing urban greening rates on population mobility may no longer be significant. Table 6 Panel data regression results. Variable Coefficient Standard deviation P-value GDP 0.0569*** 0.0049 0.000 Economic structure 0.0219*** 0.0051 0.000 Level of technological development 0.0009*** 0.0003 0.001 Level of medical services 0.0010* 0.0005 0.050 Level of educational services -0.0008** 0.0003 0.010 Level of urban greening 0.0004 0.0007 0.565 Transportation * regional development policy 0.0575*** 0.0141 0.000 Population policy 0.0162 0.0149 0.282 Constant 0.0622 0.0380 0.109 Observation 63 Adjusted R squared 0.9815 F-value 206.38*** Fixed effect YES Note: * p < 0.05; ** p < 0.01; *** p < 0.001 6 Discussion Many studies define transportation lag as a development bottleneck caused by underinvestment, flawed decision-making, or imbalanced facility distribution, resulting in infrastructure falling behind the pace of urbanization (W. Wang & Xie, 2024 ). Based on panel data from nine cities between 1990 and 2020, this study finds no generalized absolute undersupply in the Pearl River Delta (PRD); instead, it exhibits internally unbalanced relative lag. Among them, Guangzhou and Shenzhen consistently show transportation indices exceeding population indices (transport/population ratio > 1.2 in 2020), while Zhaoqing and Jiangmen fall below 0.8. Consequently, although the region's overall coupling coordination degree increased from 0.21 to 0.54, a hierarchical pattern—core, periphery, and outer circle—remains. Consistent with previous research, this gradient stems from excessive transportation resource concentration in core cities and weak connectivity in peripheral nodes and intercity corridors. The result is spatial fragmentation of network connectivity and over-aggregation of infrastructure (Q. Du et al., 2022b ; J. He et al., 2024b ; N. Li et al., 2023b ). The persistent outperformance of transportation indices over population indices in core cities, contrasted with widespread transportation lag elsewhere, highlights internal resource allocation imbalances. Therefore, improvements should focus on intercity trunk corridors, secondary hub enhancements, and public fiscal rebalancing to mitigate core siphoning effects, enhance peripheral accessibility, and improve network resilience and regional equity. While prior studies emphasize the role of economic scale and investment-driven growth in population–transportation coordination, they often overlook the influence of consumption structure and technological spillovers (Lu & Li, 2025 ). Our findings also suggest that consumption upgrading expands the spatial domain of the service sector and increases everyday mobility demand. Meanwhile, technological advancement—through innovation activities—drives demand for efficient transport systems, in turn attracting population migration and urban expansion, thereby facilitating coordinated urbanization through positive feedback loops. These findings corroborate similar conclusions from the Yangtze River Delta, suggesting that the PRD should seize the current window of digital economy and consumption upgrading to accelerate multi-modal integration and intelligent transportation deployment, providing sustainable support for the diffusion of service industries and advanced manufacturing chains (Chang et al., 2024 ). The “Outline of the Reform and Development Plan for the Pearl River Delta” is a strategic policy document guiding industrial layout, urban development, infrastructure networks, technological innovation, and ecological protection in the region, with a strong emphasis on regional coordination and inter-city linkages (Cheshmehzangi & Tang, 2022 ). From 2010 to 2020, the coordinated development policies of the PRD yielded notable progress: the region's overall strength and coupling coordination steadily improved—permanent population increased by approximately 40%, per capita income doubled, expressway mileage rose by 20%, and total mileage of classified roads doubled. However, policy dividends have shown a “hub-lock-in” effect, with the three core cities clearly leading in population attractiveness, transport accessibility, and port throughput. In contrast, non-core cities suffer from lower policy gains and weaker accessibility, limiting their ability to absorb high-value-added industries. The next stage must maintain core competitiveness while linking transportation investment with industrial transfer and public service improvement through intercity rail express lines, integrated transit corridors, and intelligent freight hubs. This would support a “multi-core–multi-node–networked” layout to prevent the structural fragility caused by excessive monocentric concentration. Urban services such as healthcare and education can significantly enhance urban attractiveness, support the urbanization process, and increase demand for transport infrastructure. This study also validates the positive role of healthcare services: medical resources have a positive effect on the coupling coordination degree (0.0010, p = 0.05), whereas the supply of basic education shows a negative effect. High-quality healthcare sustainably attracts both permanent residents and cross-regional patients. In contrast, universal primary education, due to its numerical balance and shorter migration cycles, contributes less to long-term population agglomeration and cross-city transport demand. Its marginal attractiveness is more dependent on “quality” (H. Zhang et al., 2023 ). Educational services generally struggle to drive long-term or cross-regional transport demand. Migration for education tends to be temporary or phased, meaning that while education resources may attract short-term floating populations, they rarely provide lasting urban appeal or significant transportation load. In terms of limitations, this study uses five-year interval data, resulting in a limited sample size that may not fully capture short-cycle shocks such as the two-child policy. The lack of continuous time-series data may reduce the reliability of some conclusions. Moreover, the absence of micro-level mobility trajectory data constrains the granularity of commuting and logistics chain analysis. Future research should expand the sample scope to include more cities and longer time periods, in order to systematically evaluate the impact mechanisms of policy and economic factors on population–transport coupling coordination. Additionally, future studies should consider incorporating broader environmental and social dimensions—such as public safety and health services—to broaden analytical perspectives and enhance the explanatory power and practical relevance of the research framework. 7 Conclusion and policy implications Based on long-term panel data from nine cities in the Pearl River Delta (PRD) spanning 1990–2020, this study applies range standardization, an entropy-weighted coupling coordination model, and fixed-effect spatial Durbin regression to systematically reveal the spatiotemporal patterns and driving mechanisms of regional population–transport system co-evolution. The results show that: (1) The overall coupling coordination degree in the PRD rose from 0.21 to 0.54, indicating a gradual transition from \"moderate imbalance\" to \"low-level coupling.\" However, a significant “core–periphery–outer ring” gradient persists. Guangzhou (0.85) and Shenzhen (0.75) have entered a high coupling zone, while Zhaoqing and Jiangmen remain in the moderate imbalance category; (2) A pronounced spatial mismatch exists between population and transportation resources across the PRD. Since 2005, Guangzhou’s composite transport index has consistently exceeded its population index, whereas most other cities exhibit characteristics of transportation-lagged urban development; (3) Panel regression analysis reveals significant positive elasticities between coupling coordination and economic growth, consumption-led industrial structure, and technological progress. Specifically, the elasticity coefficients are 0.0569 for GDP, 0.0219 for consumption-oriented economy, and 0.0009 for patent density (all p < 0.001), confirming the \"consumption–technology–transport–population\" positive feedback hypothesis; (4) Public services and policy effects show clear differentiation: medical resources significantly promote coupling coordination (0.0010, p = 0.05), while basic education exhibits a weak negative effect (–0.0008, p = 0.01). Notably, the interaction term for regional development and transport policy reaches a coefficient of 0.0575 (p < 0.001), indicating that following the coordinated integration policy launched in 2009, core cities such as Guangzhou, Shenzhen, and Zhuhai experienced an annual additional increase of nearly 0.06 in their coupling degree. This confirms the magnifying effect of macro-level integration planning. However, excessive concentration of resources and functions in single hubs has objectively weakened the ability of peripheral cities to obtain transport investment and host high-value-added activities, thereby exacerbating regional development imbalances. Based on the above findings, this study proposes several policy recommendations for improving population–transport coupling coordination in the PRD: First, activate regional synergistic growth through a “multi-core–networked” transport structure. Core cities should continue to consolidate their roles as international-level integrated hubs, while establishing 30–60 minutes commuting zones and 1.5-hour industrial cooperation zones through intercity rail express, cross-river corridors, and trunk–branch highway systems to enhance accessibility and network resilience of peripheral nodes (Y. Wang, Cao, et al., 2022 ). Simultaneously, leverage special bonds, toll rebates, and land premium redistribution to align transport investments with the staged transfer of manufacturing and service industries, thereby avoiding further internal imbalance. By combining consumption upgrading and digital economy diffusion, accelerate deployment of 5G-V2X, vehicle–infrastructure coordination systems, and urban traffic control centers. Open transport data APIs to attract private sector participation in autonomous bus services, smart logistics hubs, and multimodal transport pilots—bridging network gaps in peripheral cities through technological spillovers. Second, enhance the quality of population flows through equitable public services and collaborative governance (Barrutia et al., 2022 ). At the provincial level, a “Greater Bay Area Population–Transport Coordination Monitoring Index Cluster” should be established. This would use mobile signaling, transit IC cards, and remote sensing imagery to track coupling dynamics in real time, linking monitoring outcomes with fiscal transfers and infrastructure investment quotas to create dynamic incentives. For peripheral cities with weak healthcare capacity, accessibility to quality medical care can be improved through branch hospital deployment, telemedicine centers, and intercity medical shuttle lines. In the education sector, efforts should be made to situate quality high schools and vocational–educational integration platforms in outer-ring cities, supported by school transit rail lines and regional public transit networks to enhance local educational magnetism and curb talent outflow from non-core cities. Through differentiated and refined coordination policies in transport and public services, the PRD could transition from “low-level coupling” to “high-quality coordination,” thus providing sustainable support for the Greater Bay Area's competitiveness among global city clusters. In summary, this paper develops an integrated analytical framework combining multidimensional indicators, coupling models, and spatial econometrics, with a specific focus on the nine cities of the Pearl River Delta. It is the first to systematically quantify the joint effects of core–periphery gradients, consumption–technology feedback loops, and policy coordination on population–transport coupling evolution. The findings offer actionable decision-making paths and empirical evidence for population and transport governance in rapidly urbanizing regions. However, the study faces limitations in data granularity and variable dimensions. First, five-year interval statistical data may not capture short-cycle shocks; second, micro-level travel behavior and real-time traffic flow data are not incorporated into the analysis. Future research could integrate mobile signaling, GPS trajectories, and multi-source remote sensing to deepen exploration of policy timeliness and individual travel response mechanisms, thereby refining the theory and practice of population–transport coordination under regional integration. Declarations Funding This work was supported by the National Natural Science Foundation of China (Grant numbers (42201183) ;2023 New Recruitment Talents Financial Subsidy for Scientific Research Initiatives (1270110343). Author Contribution Zhaoya Gong provided supervision and resources. Di Lyu led the writing and revision of the manuscript, with contributions from Weiwang Zhu. Weiwang Zhu and Libin Ouyang collected and analysed the research data. Data Availability The authors declare that the data supporting the conclusions are described in the paper. Due to privacy and security restrictions, certain datasets analyzed in this study are not publicly available. Interested researchers can request access by contacting the corresponding author, Zhaoya Gong, subject to evaluation and compliance with relevant policies. References Adams, S. B. (2021). From orchards to chips: Silicon Valley’s evolving entrepreneurial ecosystem. In The dynamics of entrepreneurial ecosystems (pp. 15–35). Routledge. Barrutia, J. M., Echebarria, C., Aguado-Moralejo, I., Apaolaza-Ibáñez, V., & Hartmann, P. (2022). Leading smart city projects: Government dynamic capabilities and public value creation. Technological Forecasting and Social Change, 179, 121679. https://doi.org/10.1016/j.techfore.2022.121679 Calderón, C., Cantu, C., & Chuhan-Pole, P. (2018). Infrastructure Development in Sub-Saharan Africa: A Scorecard (SSRN Scholarly Paper 3172503). Social Science Research Network. https://papers.ssrn.com/abstract=3172503 Cao, W., Shi, F., Zhu, Q., & Li, Q. (2025). How access distance to high-speed rail stations affects individual travel behavior in small cities: A case study of the Chengdu-Chongqing corridor. Journal of Public Transportation, 27, 100127. https://doi.org/10.1016/j.jpubtr.2025.100127 Chang, K., Zhang, H., & Li, B. (2024). The Impact of Digital Economy and Industrial Agglomeration on the Changes of Industrial Structure in the Yangtze River Delta. Journal of the Knowledge Economy, 15(2), 9207–9227. https://doi.org/10.1007/s13132-023-01448-w Chen J., & Pan L. (2024, October 15). Impact of the Coupling Coordination Degree of Human Capital and Infrastructure on High-Quality Economic Development: Empirical Evidence from Chinese Cities. | EBSCOhost. https://doi.org/10.3390/su16208905 Chen, W., Liang, Y., Zhu, Y., Chang, Y., Luo, K., Wen, H., Li, L., Yu, Y., Wen, Q., Chen, C., Zheng, K., Gao, Y., Zhou, X., & Zheng, Y. (2024). Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2403.14151 Cheshmehzangi, A., & Tang, T. (2022). Pearl River Delta City Cluster: From Dual-Core Structure Economic Development Strategies to Regional Economic Plans. In A. Cheshmehzangi & T. Tang (Eds.), China’s City Cluster Development in the Race to Carbon Neutrality (pp. 63–75). Springer Nature. https://doi.org/10.1007/978-981-19-7673-5_5 Colon, C., Hallegatte, S., & Rozenberg, J. (2021). Criticality analysis of a country’s transport network via an agent-based supply chain model. Nature Sustainability, 4(3), 209–215. Dinlersoz, E. M., & Fu, Z. (2022). Infrastructure investment and growth in China: A quantitative assessment. Journal of Development Economics, 158, 102916. https://doi.org/10.1016/j.jdeveco.2022.102916 Dong, L., Longwu, L., Zhenbo, W., Liangkan, C., & Faming, Z. (2021). Exploration of coupling effects in the Economy–Society–Environment system in urban areas: Case study of the Yangtze River Delta Urban Agglomeration. Ecological Indicators, 128, 107858. Du, Q., Wang, X., Li, Y., Zou, P. X. W., Han, X., & Meng, M. (2022a). An analysis of coupling coordination relationship between regional economy and transportation: Empirical evidence from China. Environmental Science and Pollution Research, 29(23), 34360–34378. https://doi.org/10.1007/s11356-022-18598-0 Du, Q., Wang, X., Li, Y., Zou, P. X. W., Han, X., & Meng, M. (2022b). An analysis of coupling coordination relationship between regional economy and transportation: Empirical evidence from China. Environmental Science and Pollution Research, 29(23), 34360–34378. https://doi.org/10.1007/s11356-022-18598-0 Du, R., Liu, K., Zhao, D., & Fang, Q. (2024). Urban amenity and urban economic resilience: Evidence from China. Frontiers in Public Health, 12, 1392908. Duan, L., Niu, D., Sun, W., & Zheng, S. (2021). Transportation infrastructure and capital mobility: Evidence from China’s high-speed railways. The Annals of Regional Science, 67(3), 617–648. https://doi.org/10.1007/s00168-021-01059-w Ercan, T., Onat, N. C., Tatari, O., & Mathias, J.-D. (2017). Public transportation adoption requires a paradigm shift in urban development structure. Journal of Cleaner Production, 142, 1789–1799. https://doi.org/10.1016/j.jclepro.2016.11.109 Erkel, B. (2023). Policy Appeal and Tech Talent Migration: A Comparative Case Study of Australia and the United States Assessing Policy Elements That Determine Each Country’s Attractiveness for High-Skilled Tech Migrants. Franconi, L., Mantuano, M., & Ichim, D. (2024). Population grid and location quotient of land cover to capture the urban-rural nature of labour market areas in Italy. GeoJournal, 89(1), 6. Garcia-López, M.-À., Pasidis, I., & Viladecans-Marsal, E. (2024). Suburbanization and transportation in European cities. Journal of Economic Geography, 24(6), 843–869. Guo, R., Ning, L., & Chen, K. (2022). How do human capital and R&D structure facilitate FDI knowledge spillovers to local firm innovation? A panel threshold approach. The Journal of Technology Transfer, 47(6), 1921–1947. Guo, S., Huang, Q., & Wen, C. (2024). Analysis of the Spatial Heterogeneity of Commuting Flows in Beijing: Perspectives from Mobile Phone Data. Sensors and Materials, 36(10), 4455. https://doi.org/10.18494/SAM5253 Guo, X., & Xu, J. (2025). The impact of China’s 2014 Hukou reform on economic growth. Economic Analysis and Policy, 85, 641–655. https://doi.org/10.1016/j.eap.2024.12.031 Gutiérrez, J., Condeço-Melhorado, A., & Martín, J. C. (2010). Using accessibility indicators and GIS to assess spatial spillovers of transport infrastructure investment. Journal of Transport Geography, 18(1), 141–152. https://doi.org/10.1016/j.jtrangeo.2008.12.003 Han, D., Attipoe, S. G., Han, D., & Cao, J. (2023). Does transportation infrastructure construction promote population agglomeration? Evidence from 1838 Chinese county-level administrative units. Cities, 140, 104409. He, J., Yang, S., Deng, S., Ye, J., & Chen, H. (2024a). Research on the Decoupling Relationship between Transportation Land and Population Growth: A Case of Guangdong Province in China. Land, 13(4), 484. He, J., Yang, S., Deng, S., Ye, J., & Chen, H. (2024b). Research on the Decoupling Relationship between Transportation Land and Population Growth: A Case of Guangdong Province in China. Land, 13(4), 484. https://doi.org/10.3390/land13040484 He, Q., Musterd, S., & Boterman, W. (2023). Geographical structure of the local segregation of migrants in (sub) urban China. GeoJournal, 88(2), 1449–1467. Huang, F., Yi, P., Wang, J., Li, M., Peng, J., & Xiong, X. (2022). A dynamical spatial-temporal graph neural network for traffic demand prediction. Information Sciences, 594, 286–304. https://doi.org/10.1016/j.ins.2022.02.031 Huang, S., & Lin, Y. (2025). Research on High-Quality Urbanization Development and Optimization Pathways Based on the Coupling Coordination Perspective of “Population–Land–Economy–Environment”: A Case Study of Jiangsu Province, China. Land, 14(2), 435. Jaramillo Lizana, J. (2025). Regional Inequality in Peru: Causes, Effects, and Strategies for Equitable Development. https://doi.org/10.2139/ssrn.5020164 Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207, 117921. https://doi.org/10.1016/j.eswa.2022.117921 Jin, F., Jiao, J., Qi, Y., & Yang, Y. (2017). Evolution and geographic effects of high-speed rail in East Asia: An accessibility approach. Journal of Geographical Sciences, 27(5), 515–532. https://doi.org/10.1007/s11442-017-1390-8 Komornicki, T., & Szejgiec-Kolenda, B. (2023). The development of transport infrastructure in Poland and the role of spatial planning and cohesion policy in investment processes. Planning Practice & Research, 38(5), 694–713. https://doi.org/10.1080/02697459.2020.1852677 Kozera, A., Satoła, Ł., & Standar, A. (2024). European Union co-funded investments in low-emission and green energy in urban public transport in Poland. Renewable and Sustainable Energy Reviews, 200, 114530. https://doi.org/10.1016/j.rser.2024.114530 Li, N., Song, Y., Xia, W., & Fu, S.-N. (2023a). Regional transportation integration and high-quality economic development, coupling coordination analysis, in the Yangtze River Delta, China. Systems, 11(6), 279. Li, N., Song, Y., Xia, W., & Fu, S.-N. (2023b). Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China. Systems, 11(6), 279. https://doi.org/10.3390/systems11060279 Li, T., Dong, Y., Wei, X., Zhou, H., & Li, Z. (2024). The rapid prosperity of China’s Pearl River Delta from the perspective of social–ecological coupling: Implications for sustainable management. Scientific Reports, 14(1), 19914. Li, X., Li, Y., Jia, T., Zhou, L., & Hijazi, I. H. (2022). The six dimensions of built environment on urban vitality: Fusion evidence from multi-source data. Cities, 121, 103482. https://doi.org/10.1016/j.cities.2021.103482 Li, Z., Tang, J., Feng, T., Liu, B., Cao, J., Yu, T., & Ji, Y. (2024). Investigating urban mobility through multi-source public transportation data: A multiplex network perspective. Applied Geography, 169, 103337. https://doi.org/10.1016/j.apgeog.2024.103337 Liao, Y., Yeh, S., & Jeuken, G. S. (2019). From individual to collective behaviours: Exploring population heterogeneity of human mobility based on social media data. EPJ Data Science, 8(1), 1–22. Liao, Z., & Liang, S. (2024). Spatiotemporal differences and influencing factors of urban vitality and urban expansion coupling coordination in the Pearl River Delta. Heliyon, 10(4). Liu, J., Meng, B., Xu, J., & Li, R. (2023). Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network. ISPRS International Journal of Geo-Information, 12(6), 213. https://doi.org/10.3390/ijgi12060213 Liu, L., & Zhang, M. (2021). The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region. Sustainability, 13(8), 4227. https://doi.org/10.3390/su13084227 Liu, Y., Jia, R., Ye, J., & Qu, X. (2022). How machine learning informs ride-hailing services: A survey. Communications in Transportation Research, 2, 100075. https://doi.org/10.1016/j.commtr.2022.100075 Liu, Y., Nath, N., Murayama, A., & Manabe, R. (2022). Transit-oriented development with urban sprawl? Four phases of urban growth and policy intervention in Tokyo. Land Use Policy, 112, 105854. Liu, Y., Tang, D., & Wang, F. (2024). Research on the spatial spillover effect of high-speed railway on the income of urban residents in China. Humanities and Social Sciences Communications, 11(1), 1–13. Liu, Z., Xia, H., & Zhang, T. (2024). A review of research methods on the coupling relationship between urban rail transit and urban space: Revealing spatiotemporal relationships through big data. International Journal of Digital Earth, 17(1), 2339363. https://doi.org/10.1080/17538947.2024.2339363 Lu, C., Hong, W., Wang, Y., & Zhao, D. (2021). Study on the Coupling Coordination of Urban Infrastructure and Population in the Perspective of Urban Integration. IEEE Access, 9, 124070–124086. https://doi.org/10.1109/ACCESS.2021.3110368 Lu, C., & Li, J. (2025). Influence of Population Agglomeration on Regional High-Quality Economic Development: Evidence from Guangdong Province in China (SSRN Scholarly Paper 5276411). Social Science Research Network. https://doi.org/10.2139/ssrn.5276411 Ma, F., Guo, Y., Yuen, K. F., Woo, S., & Shi, W. (2019). Association between New Urbanization and Sustainable Transportation: A Symmetrical Coupling Perspective. Symmetry, 11(2), 192. https://doi.org/10.3390/sym11020192 Macias, L. H., Ravalet, E., & Rérat, P. (2025). How does telework impact daily and residential mobilities: New geographies of working and living in Switzerland. Applied Geography, 178, 103591. https://doi.org/10.1016/j.apgeog.2025.103591 Magazzino, C., & Mele, M. (2021). On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics, 88, 100947. https://doi.org/10.1016/j.retrec.2020.100947 Magoutas, A., Manolopoulos, D., Tsoulfas, G. T., & Koudeli, M. (2023). Economic impact of road transportation infrastructure projects: The case of Egnatia Odos Motorway. European Planning Studies, 31(4), 780–801. https://doi.org/10.1080/09654313.2022.2082243 Masahiko, M. (2022). Why Do Firms Concentrate in Tokyo? An Economic Geography Perspective. Japan Labor Issues/The Japan Institute for Labour Policy and Training, International Research Exchange Section [編], 6(36–40), 43–54. Mitropoulos, L., Karolemeas, C., Tsigdinos, S., Vassi, A., & Bakogiannis, E. (2023). A composite index for assessing accessibility in urban areas: A case study in Central Athens, Greece. Journal of Transport Geography, 108, 103566. https://doi.org/10.1016/j.jtrangeo.2023.103566 Muzira, S., & Qiao, W. (2022). To Pave or Not to Pave: A Framework for Systematic Decision Making in the Choice of Paving Technologies for Rural Roads. Transportation Research Record: Journal of the Transportation Research Board, 2676(7), 46–54. https://doi.org/10.1177/03611981221076446 Nkemgha, G. Z., Nchofoung, T. N., & Sundjo, F. (2023). Financial development and human capital thresholds for the infrastructure development-industrialization nexus in Africa. Cities, 132, 104108. https://doi.org/10.1016/j.cities.2022.104108 Peng, H., Du, Y., Liu, Z., Yi, J., Kang, Y., & Fei, T. (2019). Uncovering patterns of ties among regions within metropolitan areas using data from mobile phones and online mass media. GeoJournal, 84, 685–701. Persyn, D., Barbero, J., Díaz‐Lanchas, J., Lecca, P., Mandras, G., & Salotti, S. (2023). The ripple effects of large‐scale transport infrastructure investment. Journal of Regional Science, 63(4), 755–792. https://doi.org/10.1111/jors.12639 Pokharel, R., Bertolini, L., & Te Brömmelstroet, M. (2023). How does transportation facilitate regional economic development? A heuristic mapping of the literature. Transportation Research Interdisciplinary Perspectives, 19, 100817. https://doi.org/10.1016/j.trip.2023.100817 Pradhan, R. P., Arvin, M. B., & Nair, M. (2021). Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities, 115, 103213. Ren, Y., Tian, Y., & Xiao, X. (2022). Spatial effects of transportation infrastructure on the development of urban agglomeration integration: Evidence from the Yangtze River Economic Belt. Journal of Transport Geography, 104, 103431. Seifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L., & Salvati, L. (2022). Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio, 51(6), 1446–1458. Shen, J., Ren, X., Wu, H., & Feng, Z. (2024). The Relationship between the Construction of Transportation Infrastructure and the Development of New Urbanization. ISPRS International Journal of Geo-Information, 13(6), 194. Singer, M. E., Cohen-Zada, A. L., & Martens, K. (2022). Core versus periphery: Examining the spatial patterns of insufficient accessibility in U.S. metropolitan areas. Journal of Transport Geography, 100, 103321. https://doi.org/10.1016/j.jtrangeo.2022.103321 Skeldon, R. (2012). Migration transitions revisited: Their continued relevance for the development of migration theory. Population, Space and Place, 18(2), 154–166. Song, G., Cai, J., & Fu, Y. (2023). Regional development study how to develop a small city affected by siphoning: A case of a Chinese city. Prosperitas, 10(3), 1–13. Staricco, L., & Vitale Brovarone, E. (2018). Promoting TOD through regional planning. A comparative analysis of two European approaches. Journal of Transport Geography, 66, 45–52. https://doi.org/10.1016/j.jtrangeo.2017.11.011 Sun, W., Wang, C., Liu, C., & Wang, L. (2021). High-Speed Rail Network Expansion and Its Impact on Regional Economic Sustainability in the Yangtze River Delta, China, 2009–2018. Sustainability, 14(1), 155. https://doi.org/10.3390/su14010155 Sung, H., & Eom, S. (2024). Evaluating transit-oriented new town development: Insights from Seoul and Tokyo. Habitat International, 144, 102996. Tang, C., & Dou, J. (2022). Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network. Frontiers in Physics, 10. https://doi.org/10.3389/fphy.2022.855380 Tang, J., Gao, F., Han, C., Cen, X., & Li, Z. (2021). Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi. Journal of Transport Geography, 95, 103134. https://doi.org/10.1016/j.jtrangeo.2021.103134 Tao, Z. (2019). Research on the degree of coupling between the urban public infrastructure system and the urban economic, social, and environmental system: A case study in Beijing, China. Mathematical Problems in Engineering, 2019(1), 8206902. Tong, D., Liu, T., Li, G., & Yu, L. (2014). Empirical analysis of city contact in Zhujiang (Pearl) River Delta, China. Chinese Geographical Science, 24(3), 384–392. https://doi.org/10.1007/s11769-014-0667-4 Vergel-Tovar, C. E., & Rodriguez, D. A. (2018). The ridership performance of the built environment for BRT systems: Evidence from Latin America. Journal of Transport Geography, 73, 172–184. https://doi.org/10.1016/j.jtrangeo.2018.06.018 Vieira, J., Poças Martins, J., Marques De Almeida, N., Patrício, H., & Gomes Morgado, J. (2022). Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins. Sustainability, 14(12), 7060. https://doi.org/10.3390/su14127060 Wan, J., Wang, Z., Ma, C., Su, Y., Zhou, T., Wang, T., Zhao, Y., Sun, H., Li, Z., & Wang, Y. (2023). Spatial-temporal differentiation pattern and influencing factors of high-quality development in counties: A case of Sichuan, China. Ecological Indicators, 148, 110132. Wang, C., Wang, L., Xue, Y., & Li, R. (2022). Revealing spatial spillover effect in high-tech industry agglomeration from a high-skilled labor flow network perspective. Journal of Systems Science and Complexity, 35(3), 839–859. Wang, H., Li ,Jinyang, Wang ,Pengling, Teng ,Jing, & and Loo, B. P. Y. (2023). Adaptability analysis methods of demand responsive transit: A review and future directions. Transport Reviews, 43(4), 676–697. https://doi.org/10.1080/01441647.2023.2165574 Wang, H., Zhang, X., Zhang, X., Liu, R., & Ning, X. (2024). Understanding coordinated development through spatial structure and network robustness: A case study of the Beijing-Tianjin-Hebei region. Journal of Geographical Sciences, 34(5), 1007–1036. Wang, Q., Qian, Y., Zeng, J., Yin, F., & Zhu, L. (2021). Land Transportation Accessibility and Urbanization Spatial Pattern Based on Coupling Coordination—Taking Chengdu-Chongqing Urban Agglomeration as an Example. IOP Conference Series: Earth and Environmental Science, 696(1), 012035. https://doi.org/10.1088/1755-1315/696/1/012035 Wang, R., Zhang, X., & Li, N. (2022). Zooming into mobility to understand cities: A review of mobility-driven urban studies. Cities, 130, 103939. Wang, S., Liu, X., Zhou, C., Hu, J., & Ou, J. (2017). Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities. Applied Energy, 185, 189–200. Wang, W., & Xie, M. (2024). Can transportation infrastructure improve resource misallocation? Evidence from China. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32724 Wang, Y. (2022). The impacts of improvements in the unified economic and environmental efficiency of transportation infrastructure on industrial structure transformation and upgrade from the perspective of resource factors. PLOS ONE, 17(12), e0278722. https://doi.org/10.1371/journal.pone.0278722 Wang, Y., Cao, G., Yan, Y., & Wang, J. (2022). Does high-speed rail stimulate cross-city technological innovation collaboration? Evidence from China. Transport Policy, 116, 119–131. https://doi.org/10.1016/j.tranpol.2021.11.024 Wang, Y., Zou, H., Duan, X., & Wang, L. (2022). Coordinated Evolution and Influencing Factors of Population and Economy in the Yangtze River Economic Belt. International Journal of Environmental Research and Public Health, 19(21), 14395. https://doi.org/10.3390/ijerph192114395 Wei, W., Yuan-rui, M., Lei, G., & Liang, G. (2025). Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin. Ecological Indicators, 175, 113521. Weiner, E. (2008). Urban Transportation Planning in the United States: History, Policy, and Practice. Springer. https://doi.org/10.1007/978-0-387-77152-6 Wu, B., Jin, X., Li, D., & Wang, B. (2023). Spatial–temporal evolution of coupling coordination development between regional highway transportation and new urbanization: A case study of Heilongjiang, China. Sustainability, 15(23), 16365. Wu, C., Huang, X., & Chen, B. (2020). Telecoupling mechanism of urban land expansion based on transportation accessibility: A case study of transitional Yangtze River economic Belt, China. Land Use Policy, 96, 104687. Wu, H., Jiang, Z., Zhu, L., Lin, A., Zhou, H., & Cen, L. (2024). Analyzing multiscale associations and couplings between integrated development and eco-environmental systems: A case study of the central plains urban agglomeration, China. Applied Geography, 171, 103387. https://doi.org/10.1016/j.apgeog.2024.103387 Wu, S., Ma, L., Wang, L., Chen, X., & Shi, Z. (2023). Differences of Social Space of Rural Migrant Labor Force: The Influence of Local Quality. Land, 12(3), 644. Yang, R., An, X., Chen, Y., & Yang, X. (2023). The knowledge analysis of panel vector autoregression: A systematic review. Sage Open, 13(4), 21582440231215991. Yang, Y., Lu, X., Chen, J., & Li, N. (2022). Factor mobility, transportation network and green economic growth of the urban agglomeration. Scientific Reports, 12(1), 20094. https://doi.org/10.1038/s41598-022-24624-5 Yao, L., & Sun, L. (2013). Practical Methods in Traffic Demand Forecasting Model. In W. Wang & G. Wets (Eds.), Computational Intelligence for Traffic and Mobility (pp. 297–319). Atlantis Press. https://doi.org/10.2991/978-94-91216-80-0_15 Yoo, S., Kumagai, J., & Managi, S. (2024). Urban-rural gap induced by high-speed rail: 35 years of evidence from Japan. Research in Transportation Business & Management, 55, 101131. https://doi.org/10.1016/j.rtbm.2024.101131 Zhang, H., Zhou, B.-B., Liu, S., Hu, G., Meng, X., Liu, X., Shi, H., Gao, Y., Hou, H., & Li, X. (2023). Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China. Applied Geography, 152, 102892. https://doi.org/10.1016/j.apgeog.2023.102892 Zhang, T., Qiu, Y., Ding, R., Yin, J., Cao, Y., & Du, Y. (2023). Coupling coordination and influencing factors of urban spatial accessibility and economic spatial pattern in the New Western Land-Sea Corridor. Environmental Science and Pollution Research, 30(19), 54511–54535. https://doi.org/10.1007/s11356-023-26121-2 Zhang, X., & Zhang, Z. (2022). Interaction Effects of R&D Investment, Industrial Structure Rationalization, and Economic Growth in China Based on the PVAR Model. Sustainability, 15(1), 545. https://doi.org/10.3390/su15010545 Zhang, Y., Zhu, T., Guo, H., & Yang, X. (2023). Analysis of the coupling coordination degree of the Society-Economy-Resource-Environment system in urban areas: Case study of the Jingjinji urban agglomeration, China. Ecological Indicators, 146, 109851. Zhao, L., & Jia, Y. (2021). Interactive Correlation between Economy and Integrated Transportation Development of Metropolitan Areas in China: Quantitative Study. Journal of Urban Planning and Development, 147(4), 05021041. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000759 Zhao, X. (2024). The influence of knowledge-intensive service industry agglomeration in the Yangtze River Delta urban agglomeration on regional economy. Heliyon, 10(3). Zhao, Y., Zhang, H., An, L., & Liu, Q. (2018). Improving the approaches of traffic demand forecasting in the big data era. Cities, 82, 19–26. https://doi.org/10.1016/j.cities.2018.04.015 Zhong, Y., Wang, H., Jing, T., Yang, Y., Zou, H., & Jin, Y. (n.d.). Unveiling the Spatiotemporal Evolution Characteristics of Urban Residents’ Travel Patterns and Spillover Effects of Jobs-Housing Spaces in China Based on Multi-Source Data. Available at SSRN 5179218. Zhou, J., & Yang, Y. (2021). Transit-based accessibility and urban development: An exploratory study of Shenzhen based on big and/or open data. Cities, 110, 102990. https://doi.org/10.1016/j.cities.2020.102990 Zhu, P. (2021). Does high-speed rail stimulate urban land growth? Experience from China. Transportation Research Part D: Transport and Environment, 98, 102974. Zhuang, S., Xia, N., Gao, X., Zhao, X., Liang, J., Wang, Z., & Li, M. (2024). Coupling coordination analysis between railway transport accessibility and tourism economic connection during 2010–2019: A case study of the Yangtze River Delta. Research in Transportation Business & Management, 55, 101134. Zou, J., & Deng, X. (2022). Spatial differentiation and driving forces of migrants’ socio-economic integration in urban China: Evidence from CMDS. Social Indicators Research, 159(3), 1035–1056. Additional Declarations No competing interests reported. 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04:29:09\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":488408,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of the research methodology\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/928ff55c93fae148ea697ebd.png\"},{\"id\":92826695,\"identity\":\"1d2bf11e-ef5b-4b7c-9b6a-65fca2438a18\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 04:37:09\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":94342,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComprehensive evaluation indices of demographic and transportation systems and coupling coordination degree in PRD from 1990 to 2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/b28990b854abf322d8beefdd.png\"},{\"id\":92826311,\"identity\":\"22ddcbc9-6143-4629-8a60-b6354f760374\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 04:29:09\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":259015,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComprehensive evaluation indices of demographic system in each city from 1990 to 2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/8646b32a75484602d9a5e0be.png\"},{\"id\":92826317,\"identity\":\"1b9283c4-92a4-4c7d-9445-88bbe0ad1e62\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 04:29:09\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":234680,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eComprehensive evaluation indices of transportation system in each city from 1990 to 2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/982224ec9508afbe83457948.png\"},{\"id\":92826698,\"identity\":\"cf100629-0604-46c1-9002-280b760e9299\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 04:37:09\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":614297,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCoordination types of individual cities in PRD from 1990 to 2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/78262ba2413c515717b52d27.png\"},{\"id\":92826327,\"identity\":\"f3cd3da1-1681-4f84-85b8-b064d2f37d0d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-06 04:29:09\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":251398,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe coupling coordination of demographic and transportation systems from 1990 to 2020\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/51007b4ea37abee307835679.png\"},{\"id\":97723840,\"identity\":\"e5971a74-a32e-4002-90ff-e4825270b597\",\"added_by\":\"auto\",\"created_at\":\"2025-12-08 16:08:34\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3191220,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7168229/v1/0112edf9-6983-4270-bbfe-544aa3c3afb4.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Spatiotemporal Patterns and Drivers of Population–Transport Coordination in the Pearl River Delta\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eThe coordinated development of population and transportation systems is of profound significance for the sustainable growth of cities and regions worldwide (Shen et al., \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The coupling and coordination between transportation systems and population dynamics has been widely recognized as a key indicator of urban agglomeration competitiveness and governance effectiveness (Ma et al., \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tao, \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Transportation infrastructure not only determines the speed and cost of the movement of people and goods, but also acts as an amplifier in resource reallocation, industrial spatial layout, labor market integration, and regional innovation diffusion (Q. Du et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2022a\\u003c/span\\u003e). Simultaneously, population size, structure, and spatial distribution critically shape travel demand, infrastructure supply pressures, and transportation-related energy consumption patterns (J. Liu et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; S. Wang et al., \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Achieving a dynamic match between population change and transportation supply-demand is essential for metropolitan areas to enhance economic competitiveness while ensuring environmental resilience and social equity (Q. Wang et al., \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, deconstructing the co-evolution mechanism between population and transportation systems carries significant strategic value for balancing the \\u0026ldquo;resources\\u0026ndash;environment\\u0026ndash;society\\u0026rdquo; triad.\\u003c/p\\u003e\\u003cp\\u003eThe Pearl River Delta (PRD), as one of China\\u0026rsquo;s most economically vibrant and structurally complex polycentric urban agglomerations (Tong et al., \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), presents a critical case where the spatial coupling between transportation and population not only influences intra-regional industrial division and functional hierarchy but also shapes the integration of the Guangdong\\u0026ndash;Hong Kong\\u0026ndash;Macau Greater Bay Area and the configuration of outward-oriented economic corridors. A deeper examination of the coordination mechanism and spatial heterogeneity between transportation and population in the PRD (T. Li et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) would provide scientific evidence for clarifying investment priorities, optimizing the provision of services to resident and floating populations, and informing the strategic planning of multilevel transport hubs\\u0026mdash;thereby yielding both theoretical value and policy significance (Z. Liao \\u0026amp; Liang, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eExtant literature has explored the coordinated evolution of population and transportation from multiple dimensions. First, many macro-statistical studies have highlighted the siphon effect between transport investment and urbanization, whereby the expansion of transport corridors accelerates population concentration in core cities and reshapes spatial structures, forming a virtuous cycle of \\u0026ldquo;greater transport\\u0026ndash;greater cities\\u0026rdquo; (Han et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Song et al., \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Second, the increasing availability of big data from mobile signaling, GPS trajectories, and social media has enabled scholars to investigate individual migration and commuting behaviors at the micro level, confirming that marginal improvements in accessibility can alter job-housing matching patterns and subsequently affect population mobility and urban functional differentiation (Y. Liao et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Peng et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; R. Wang et al., \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Furthermore, researchers have introduced coupling coordination models, system dynamics frameworks, and spatial econometric techniques to quantitatively assess the interaction intensity, coupling elasticity, and spatial spillover effects between population and transportation systems (J. He et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2024a\\u003c/span\\u003e; Wei et al., \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; C. Wu et al., \\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDespite these advances, several critical limitations remain in the study of population\\u0026ndash;transportation coupling (S. Huang \\u0026amp; Lin, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Wan et al., \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). First, in terms of spatiotemporal scale, most existing studies are limited to short-term cross-sectional or single-city analyses and lack systematic investigation of long-term coupling trajectories across administrative boundaries in polycentric urban agglomerations, hindering the identification of turning points and stage-specific patterns. Second, in terms of mechanism explanation, current research frameworks still focus heavily on the linear relationship between transportation investment and population agglomeration, with insufficient integration of multi-dimensional drivers such as industrial upgrading, technological diffusion, and policy shocks\\u0026mdash;thus failing to fully reveal potential synergistic or suppressive effects. Third, regarding generalization and scalability, existing findings often remain at the level of single-case or local policy summaries, with inadequate discussion on regional differences and spatial spillover mechanisms, limiting their utility in guiding inter-regional collaborative governance.\\u003c/p\\u003e\\u003cp\\u003eTo address these gaps, this study focuses on the PRD urban agglomeration\\u0026mdash;China\\u0026rsquo;s most economically dynamic region with pronounced cross-jurisdictional characteristics\\u0026mdash;and constructs a long-term panel dataset (1990\\u0026ndash;2020, five-year intervals) across nine cities. It develops a comprehensive index system encompassing five dimensions: population, transportation, economy, environment, and policy. The study aims to achieve the following objectives: (1) quantify the spatiotemporal evolution trajectory of population\\u0026ndash;transportation coupling coordination in the PRD, identifying inflection points and regional divergence across development stages; (2) assess the marginal contribution and elasticity of key drivers\\u0026mdash;including GDP level, industrial structure, technological innovation, urban attractiveness, regional planning, hub transport strategies, and population policies\\u0026mdash;in determining the level of coordination; (3) extract generalizable strategies for promoting integrated and resilient regional development, offering operational insights for other polycentric urban areas.\\u003c/p\\u003e\\u003cp\\u003eIn terms of methodology, the study first applies the entropy weight method to objectively assign indicator weights and eliminate subjective bias. It then calculates coupling coordination degrees to quantify system-level interactions between population and transportation. On this basis, a fixed-effects panel regression model is employed to identify the marginal effects of economic, technological, and institutional variables. Robustness and heterogeneity are tested using spatial visualization and quantile regression. Compared to existing literature, this study contributes in three main areas:First, through long-term cross-regional data integration, it establishes a comprehensive indicator system spanning five dimensions and systematically tracks the coupling evolution across nine cities in the PRD from 1990 to 2020, addressing gaps in macro-scale and longitudinal studies of cross-jurisdictional urban agglomerations.Second, it integrates coupling assessment with econometric modeling, combining entropy weighting and coordination index calculations with fixed-effects and quantile regressions to form a replicable methodological framework for evaluating coordination levels, identifying mechanisms, and testing robustness.Third, by explicitly incorporating institutional variables, such as regional planning, transport hub designations, and population policies, into the analytical framework, it quantifies their marginal contributions and elasticity, offering empirical evidence and policy references for differentiated investment, spatial reconfiguration of population, and integrated governance\\u0026mdash;thereby informing transferable strategies for transportation\\u0026ndash;population coordination in polycentric regions globally.\\u003c/p\\u003e\\u003cp\\u003eThe remainder of this paper is organized as follows: Section \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e builds the theoretical framework and research hypotheses for transportation\\u0026ndash;population interaction. Section \\u003cspan refid=\\\"Sec6\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e introduces data sources, indicator system, and methodological design, including entropy weighting, coordination index calculation, and panel model construction. Section \\u003cspan refid=\\\"Sec12\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e presents empirical results, revealing the spatiotemporal patterns and typologies of coordination in the PRD. Section \\u003cspan refid=\\\"Sec16\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e analyzes the marginal elasticity of economic, technological, service-related, and policy variables. Section \\u003cspan refid=\\\"Sec20\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e concludes by summarizing theoretical and practical contributions, proposing policy implications for network optimization, spatial restructuring, and institutional collaboration, and outlining future research directions. This study ultimately deepens the understanding of regional transportation\\u0026ndash;population dynamics and supports high-quality, integrated development strategies.\\u003c/p\\u003e\"},{\"header\":\"2 Literature review\",\"content\":\"\\u003cp\\u003eThe evolution of population in the process of urbanization is essentially driven by the interaction of three forces: natural growth, spatial migration, and socioeconomic restructuring (H. Wu et al., \\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Zou \\u0026amp; Deng, \\u003cspan citationid=\\\"CR109\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Classical demographic transition theory suggests that as a country or region enters the mid-to-late stages of industrialization, birth and death rates tend to converge, and the growth of urban populations becomes increasingly dependent on migration flows rather than natural increase (Skeldon, \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). With improvements in transportation accessibility, widening urban\\u0026ndash;rural income disparities, and the spillover effects of information networks, labor forces tend to migrate along gradients from capital to employment and then to infrastructure, concentrating progressively in urban centers and secondary nodes. This process displays cyclical fluctuations characterized by concentration, dispersion, and re-concentration (Franconi et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Y. Liu et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Migration not only alters the quantity and density of urban populations but also profoundly reshapes employment structures and the spatial distribution of industries (Q. He et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). During the rapid urbanization phase dominated by manufacturing, the secondary sector had a significant labor-absorbing effect, drawing large numbers of surplus rural laborers into industrial clusters. In the subsequent post-industrial stage, dominated by the service economy, the expansion of the tertiary sector and knowledge-intensive industries has redirected population flows toward core business districts and innovation clusters (X. Zhao, \\u003cspan citationid=\\\"CR103\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In North America and Western Europe, the mid-20th century witnessed the rise of suburbanization fueled by the expansion of interstate highways and mortgage finance, leading to classic patterns of decentralization as urban populations dispersed to the suburbs, followed by reurbanization-induced returns (Garcia-L\\u0026oacute;pez et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Weiner, \\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). In Japan, during the formation of the industrial triangle, the development of the Shinkansen and national highway networks prompted massive rural youth migration toward the Tokyo\\u0026ndash;Nagoya\\u0026ndash;Osaka corridor, resulting in a highly centralized monocentric pattern (Masahiko, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In China, the Pearl River Delta and Yangtze River Delta regions have exhibited a more complex cycle of population concentration, dispersion, and re-concentration since the Reform and Opening-Up. In the 1990s, export-oriented manufacturing triggered explosive growth in secondary core cities such as Shenzhen and Suzhou, while improvements in high-speed rail and industrial upgrading have since driven population reflux toward primary cores such as Guangzhou\\u0026ndash;Shenzhen and Shanghai\\u0026ndash;Hangzhou, reflecting the strong shaping influence of transport accessibility on cyclical population agglomeration (Zhu, \\u003cspan citationid=\\\"CR107\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eStructural transformation and quality upgrading constitute the deep-seated drivers behind dynamic population changes. The concentration of surplus labor from non-urban areas into the secondary sector has accelerated the industrial gradient upgrading process (S. Wu et al., \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Meanwhile, the expansion of the service economy and knowledge-intensive industries has guided population redistribution toward multifunctional urban nodes, thereby restructuring employment structures and land-use patterns (C. Tang \\u0026amp; Dou, \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The Silicon Valley phenomenon in the United States demonstrates how the tertiary sector and high-tech clusters reshape employment structures by attracting global migrants through high-skill jobs, thereby enhancing regional educational attainment and income distribution (Adams, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Erkel, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Cross-national population tracking data reveal that the inflow of high-skilled labor into major cities not only improves individual education levels but also generates strong knowledge spillover effects that significantly boost local innovation output and wage levels (R. Guo et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; C. Wang et al., \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). At the same time, institutional constraints\\u0026mdash;such as household registration and land systems\\u0026mdash;exert significant moderating effects on migration decisions. In China, the gradual relaxation of household registration entry policies after 2014 led to a cumulative five-year increase in the resident population of typical second-tier cities (with populations between one and five million) that was approximately 9\\u0026ndash;11% higher than in the equivalent period prior to reform. This effect was most pronounced in cities with a high share of labor-intensive manufacturing and relatively moderate housing prices (X. Guo \\u0026amp; Xu, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eOverall, population evolution during urbanization follows a progressive chain of scale expansion, structural differentiation, and quality upgrading. However, variations in transportation investment timing, the pace of industrial upgrading, and the strength of institutional regulation across different countries have jointly shaped the diverse feedback mechanisms of population dynamics on transport demand, spatial structure, and social stratification (Seifollahi-Aghmiuni et al., \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Although the existing literature has systematically revealed these patterns, insufficient attention has been paid to the temporal feedback mechanisms by which population changes affect transportation demand, and to the elasticity differences among urban concentric zones. Further exploration of these issues is of significant theoretical and practical value for understanding the co-evolution of population and transport systems in polycentric urban agglomerations, optimizing regional resource allocation, and formulating fine-grained governance strategies (H. Wang et al., \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Supply and demand for transportation infrastructure\\u003c/h2\\u003e\\u003cp\\u003eThe supply system of transport infrastructure defines the material boundaries for the movement of people and goods. Its hierarchical network, nodal connectivity, and overall accessibility are widely recognized as key preconditions shaping the spatial structure of regions and the evolution of growth poles (Colon et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Cross-national econometric studies have shown that increased infrastructure density can enhance labor productivity in growth regions, with the effect declining along a clear gradient from hub cities to their peripheries (Dinlersoz \\u0026amp; Fu, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Nkemgha et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In China, the rapid rollout of high-speed rail and expressway networks under the \\u0026ldquo;Eight Vertical and Eight Horizontal\\u0026rdquo; framework has reduced average commuting time by 34.5% in key economic corridors, forming one-hour commuting circles in the Pearl River Delta, Yangtze River Delta, and Beijing-Tianjin-Hebei regions\\u0026mdash;thus validating infrastructure\\u0026rsquo;s accelerating role in urban network formation (Jin et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; L. Liu \\u0026amp; Zhang, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In contrast, Sub-Saharan Africa and parts of South Asia have long faced challenges of underinvestment, poor maintenance, and limited accessibility. According to World Bank reports, the average serviceable mileage of trunk and secondary roads in these regions is only about 50% of the global average, with less than 30% rated as being in good condition. This restricts market hinterlands, locks peripheral areas into constraints of time and distance, and significantly raises logistics costs (Calder\\u0026oacute;n et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Muzira \\u0026amp; Qiao, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Inadequate transport supply weakens peripheral regions\\u0026rsquo; accessibility to core markets, exacerbating center-periphery developmental divides and forming classic patterns of spatial exclusion and path-dependent poverty traps (Jaramillo Lizana, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). A review of diverse developmental trajectories reveals that a hierarchically complete and spatiotemporally balanced transport supply system is a crucial precondition for supporting regional economic spatial restructuring and the formation of polycentric networks (Singer et al., \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Yoo et al., \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn contrast to the grand narratives of supply-side planning, demand-side transport analysis focuses more on behavioral mechanisms and the evolution of predictive methods (H. Wang et al., \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Traditional regional-scale models, such as gravity models or four-step models, have been widely used for static predictions of passenger and freight flows. However, they often oversimplify dynamics in polycentric and time-varying networks (Yao \\u0026amp; Sun, \\u003cspan citationid=\\\"CR96\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Y. Zhao et al., \\u003cspan citationid=\\\"CR104\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In the era of big data, mobile signaling, GPS trajectories, ride-hailing order data, and social media check-ins have provided high spatiotemporal resolution flow maps. These enable fine-grained quantification of demand differences, latent bottlenecks, and network resilience\\u0026mdash;forming a robust data foundation for multi-scale decisions on strategic planning and real-time dispatching (W. Chen et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; S. Guo et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Y. Liu, Jia, et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). For instance, researchers in Shenzhen integrated ride-hailing orders, taxi GPS trajectories, and smart subway card data to construct a mixed geographically weighted regression model that reveals a power-law decay relationship between CBD vitality and 15-minute accessibility (J. Tang et al., \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Simultaneously, machine learning and graph neural networks have been increasingly applied to demand forecasting. These tools integrate multi-source real-time data, capture nonlinear and topological dependencies in both time and space, and enable continuous learning and adaptive updating of network states. They offer new paradigms for analyzing demand elasticity and propagation mechanisms under sudden disruptions by dynamically correcting traffic forecasts and quantifying network resilience (F. Huang et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Jiang \\u0026amp; Luo, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eSupply-demand mismatches remain a shared challenge in most rapidly urbanizing regions. On one hand, large-scale infrastructure investment often lags behind fast-growing demand, leading to congestion and negative externalities; on the other hand, premature investments risk asset underutilization and fiscal burdens (Persyn et al., \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Meanwhile, rapidly evolving demand, coupled with technological transitions, imposes structural adjustment pressures on existing networks (Ercan et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Empirical experience from Europe and the U.S. shows that synchronizing land use with network evolution through accessibility analysis and transit-oriented development (TOD) strategies can smooth supply-demand curves over a decadal scale (Staricco \\u0026amp; Vitale Brovarone, \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The Bus Rapid Transit (BRT) systems of Latin America offer an alternative path\\u0026mdash;demonstrating that commuting efficiency can be significantly improved even at relatively low cost (Vergel-Tovar \\u0026amp; Rodriguez, \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Overall, research on transport infrastructure supply and demand has shifted from static scale expansion to dynamic network optimization and behavior-sensitive modeling. However, comprehensive empirical exploration remains urgently needed in areas such as cross-jurisdictional coordination in polycentric urban agglomerations, tiered service balancing, and the application of digital twin platforms.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Interaction between transportation and demographic\\u003c/h2\\u003e\\u003cp\\u003eAt the macro level, numerous cross-national studies have confirmed the leading and feedback relationships between transport infrastructure and the spatial redistribution of population (Guti\\u0026eacute;rrez et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Pokharel et al., \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). First, transport infrastructure such as trunk highways, intercity railways, and multimodal transport corridors compress intercity time\\u0026ndash;space distances, reduce migration and commuting thresholds, and enhance core cities\\u0026rsquo; attraction to labor and capital (Duan et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Z. Li et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Conversely, the resulting agglomeration-induced increase in travel demand stimulates the expansion and hierarchical differentiation of transport networks, forming a cumulative cycle of transport investment, population concentration, and subsequent network upgrading (Y. Yang et al., \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). For instance, empirical studies based on the Minneapolis\\u0026ndash;St. Paul metropolitan area reveal significant spatial heterogeneity in job accessibility along rail corridors. The difference in accessibility between urban centers and competing employment clusters in surrounding areas is identified as a key factor affecting job acquisition and commuting efficiency for low-income groups (Mitropoulos et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). High-speed rail travel surveys in the Chengdu\\u0026ndash;Chongqing city cluster show that station accessibility significantly influences travel behavior in smaller cities. As station distance decreases, the probability of high-speed rail travel increases markedly, along with a notable rise in travel frequency (Cao et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eMicro- and meso-level perspectives further illuminate the real-time feedback mechanisms between travel behavior and job\\u0026ndash;housing relationships within the coupled transport\\u0026ndash;population system (Macias et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Zhong et al., n.d.). Multi-agent simulations based on mobile signaling and public transit smart card data suggest that improved transport accessibility expands individuals\\u0026rsquo; employment opportunity radii (Zhou \\u0026amp; Yang, \\u003cspan citationid=\\\"CR106\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In terms of quantitative methods, spatial panel models, coupling coordination indicator systems, and PVAR (panel vector autoregression) models are widely applied to measure interaction intensity and lag elasticity (R. Yang et al., \\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; X. Zhang \\u0026amp; Zhang, \\u003cspan citationid=\\\"CR100\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Overall, the transport\\u0026ndash;population relationship exhibits bidirectional coupling across multiple scales and pathways: at the macro scale as a cumulative cycle between network density and population agglomeration, at the meso scale as dynamic restructuring of the job\\u0026ndash;housing spatial structure, and at the micro scale as real-time behavioral feedback to transport supply changes in individual travel choices. In sum, the transport and population systems are characterized by multiscale and multipath bidirectional coupling.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Factors influencing the coordination of transportation and demographic\\u003c/h2\\u003e\\u003cp\\u003eWithin existing research frameworks, scholars generally regard economic and industrial fundamentals as the primary exogenous forces driving the coordinated evolution of transport and population systems (Y. Wang, Zou, et al., \\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). First, robust economic growth expands the local government\\u0026rsquo;s tax base, enhances multilateral financing capacity and capital returns, and thereby triggers larger-scale and higher-quality transport investments (L. Zhao \\u0026amp; Jia, \\u003cspan citationid=\\\"CR102\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Empirical evidence from the Egnatia Odos Motorway suggests a significant long-term bidirectional causal relationship between transport efficiency and regional GDP, with the elasticity of infrastructure\\u0026rsquo;s contribution to economic growth increasing alongside industrial upgrading (Magoutas et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Second, shifts in industrial structure reshape the spatial distribution of labor and production factors. High-tech industries and modern services demand greater travel efficiency for business and commuting, thereby driving demand-led expansion of high-speed railways and multimodal corridors (Y. Wang, \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In China\\u0026rsquo;s Yangtze River Delta, the shift of manufacturing toward R\\u0026amp;D and design has significantly intensified reliance on the Shanghai\\u0026ndash;Nanjing intercity railway, where the development of high-speed rail has improved rail accessibility by approximately 50% (Sun et al., \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Meanwhile, the digital economy and automation technologies have reduced the marginal cost of transportation and enhanced network resilience, further unlocking mobility potential within metropolitan areas (Vieira et al., \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These trends underscore the roles of macroeconomic vitality, industrial specialization, and technological innovation as core drivers of synchronized advancement in transport capacity and population concentration (Magazzino \\u0026amp; Mele, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eFurther studies highlight the mediating and moderating roles of institutional environments and urban attractiveness in shaping transport\\u0026ndash;population coupling (Chen J. \\u0026amp; Pan, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; T. Zhang et al., \\u003cspan citationid=\\\"CR101\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). First, spatial planning and investment\\u0026ndash;financing mechanisms determine the sequencing and layout of transport infrastructure (Komornicki \\u0026amp; Szejgiec-Kolenda, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Second, public service accessibility, ecological quality, and cultural and recreational amenities together form a multidimensional index system of urban attractiveness (X. Li et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Governance heterogeneity also generates institutional effects in the transport\\u0026ndash;population interaction system. In Chinese prefecture-level cities with higher degrees of fiscal decentralization, a stronger coupling coordination is observed between rail transit development and resident population growth (Z. Liu et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Lu et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Moreover, environmental constraints are reshaping investment priorities. For example, the EU\\u0026rsquo;s Green Corridors Initiative incorporates carbon reduction performance as a threshold for funding allocation. The resulting low-carbon capital orientation has altered the spatial configuration of infrastructure investments, influencing intercity population agglomeration and dispersion through differential accessibility and factor mobility (Kozera et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eCurrent research has preliminarily outlined the theoretical landscape of transport\\u0026ndash;population coupling from four perspectives: population dynamics, transport supply and demand, cross-scale interaction mechanisms, and exogenous driving forces. However, there remains a lack of systematic evidence regarding the long-term coordination trajectories and quantifiable institutional effects within cross-jurisdictional, polycentric urban agglomerations. This creates opportunities for integrated research based on long-term panel data and multi-source big data. Building on this gap, the present study aims to further explore influencing factors and provide theoretical support for urbanization strategies in developing countries.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3 Data and Methodology\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 Research Framework\\u003c/h2\\u003e\\u003cp\\u003eThis study constructs an integrated research framework encompassing indicator development, dynamic evaluation, and spatial econometric diagnosis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), aiming to systematically analyze the coupling mechanisms between population and transportation. First, a hierarchical indicator system is established based on system interaction mechanisms, with indicator weights determined through a combination of the Analytic Hierarchy Process (AHP) and the entropy weight method, balancing expert judgment and objective data. Second, data from nine prefecture-level cities in the Pearl River Delta from 1990 to 2020 are range-standardized to compute comprehensive indices for both population and transportation systems. A coupling coordination degree model is then applied to jointly measure coupling intensity and synergistic performance. The results are categorized into five levels of coordination types at 0.2 intervals to depict their spatiotemporal patterns. Finally, a fixed effects panel regression model is employed to identify the direct effects of economic, environmental, and policy factors on the coupling degree. Subsequently, a Spatial Durbin Model (SDM) is introduced to capture the spatial spillover effects of natural, technological, and institutional variables. This framework integrates indicator assessment, temporal dynamics, and spatial econometric perspectives, offering precise and operational decision-making support for coordinated population\\u0026ndash;transport planning.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Data sources and processing\\u003c/h2\\u003e\\u003cp\\u003eTaking into account the accuracy, accessibility, and temporal continuity of the data, this study collected population, transportation, and economic data from 9 prefecture-level cities over a span of 7 years, from 1990 to 2020, with a 5-year interval. The population data was sourced from the Guangdong Provincial Census and the 1% population sampling survey data, while the transportation and economic data were obtained from the Guangdong Provincial Statistical Yearbook and the statistical yearbooks of the respective cities. In cases of missing data, interpolation was performed using the growth rate method.\\u003c/p\\u003e\\u003cp\\u003eTo mitigate the impact of dimensionality on data calculations and comparative analysis, the maximum-minimum method was employed to standardize the raw data prior to data analysis. Specifically, the equation is as follows:\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{X}_{ij}=\\\\frac{{r}_{ij}-\\\\text{m}\\\\text{i}\\\\text{n}\\\\left({r}_{ij}\\\\right)}{\\\\text{max}\\\\left({r}_{ij}\\\\right)-\\\\text{m}\\\\text{i}\\\\text{n}\\\\left({r}_{ij}\\\\right)}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{X}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e stands for the normalised value, while \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{r}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the raw data.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 The entropy method\\u003c/h2\\u003e\\u003cp\\u003eThe entropy method was utilized in this study to determine the weights of different indicators associated with demographic and transportation. As an objective approach, it mitigates the influence of human factors and is extensively employed in system evaluation research. Grounded on the concept of information entropy, the entropy method calculates the entropy of indicator values to capture their significance and variances, consequently determining their weights(B. Wu et al., \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The fundamental principle of the entropy method lies in the notion that indicators with higher entropy values demonstrate more pronounced differences and impact on decision-making outcomes, indicating a higher weight. The calculation procedures of the entropy method are as follows:\\u003c/p\\u003e\\u003cp\\u003e(1) Calculate the proportion of the standardize data:\\u003cdiv id=\\\"Equ2\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ2\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{p}_{ij}=\\\\frac{{X}_{ij}}{\\\\sum\\\\:_{j}{X}_{ij}}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e2\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e(2) Calculate the entropy of each indicator:\\u003cdiv id=\\\"Equ3\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ3\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{e}_{i}=-\\\\frac{1}{lnn}\\\\sum\\\\:_{j}{p}_{ij}ln{p}_{ij}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e3\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e(3) Calculate the weights of each indicator:\\u003cdiv id=\\\"Equ4\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ4\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{w}_{i}=\\\\frac{1-{e}_{i}}{{\\\\sum\\\\:}_{i}(1-{e}_{i})}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e4\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the above formulas, \\u003cem\\u003ei\\u003c/em\\u003e and \\u003cem\\u003ej\\u003c/em\\u003e denote the ordinal numbers of indicators and observations, respectively, \\u003cem\\u003en\\u003c/em\\u003e is the number of observations and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{X}_{ij}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the standardize data.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.4 Coupling coordination degree model\\u003c/h2\\u003e\\u003cp\\u003eThe widely adopted model for assessing the collaborative development level of different systems is the coupling coordination degree model(N. Li et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2023a\\u003c/span\\u003e; Zhuang et al., \\u003cspan citationid=\\\"CR108\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), which has been applied in this study to analyze demographics and transportation. Coupling refers to the interrelation and interaction between systems, while coordination refers to the degree of synergy and mutual development. The purpose of the coupling coordination degree model is to evaluate the overall operational status and level of coordinated development by quantifying and analyzing the degree of coupling and coordination between systems. In this study, the coupling analysis of demographic and transportation systems focuses on examining the relationship between changes in population size and structure and the construction of transportation infrastructure. The calculation procedures for the coupling coordination degree model are as follows:\\u003c/p\\u003e\\u003cp\\u003e(1) Calculate coupling degree between demographic and transportation systems of city \\u003cem\\u003ek\\u003c/em\\u003e in year \\u003cem\\u003et\\u003c/em\\u003e:\\u003cdiv id=\\\"Equ5\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ5\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:C(k,t)=2\\\\sqrt{\\\\frac{f\\\\left(P\\\\right)\\\\times\\\\:g\\\\left(T\\\\right)}{{(f\\\\left(P\\\\right)+g\\\\left(T\\\\right))}^{2}}}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e5\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e(2) Calculate coordination degree between demographic and transportation systems of city \\u003cem\\u003ek\\u003c/em\\u003e in year \\u003cem\\u003et\\u003c/em\\u003e:\\u003cdiv id=\\\"Equ6\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ6\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:D(k,t)={\\\\alpha\\\\:}f\\\\left(P\\\\right)+\\\\beta\\\\:g\\\\left(T\\\\right)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e6\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e(3) Calculate coupling coordination degree between demographic and transportation systems of city \\u003cem\\u003ek\\u003c/em\\u003e in year \\u003cem\\u003et\\u003c/em\\u003e:\\u003cdiv id=\\\"Equ7\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ7\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:T(k,t)=\\\\sqrt{C(k,t)\\\\times\\\\:D(k,t)}$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e7\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e(4) Calculate coupling coordination degree between demographic and transportation systems of study area in year \\u003cem\\u003et\\u003c/em\\u003e:\\u003cdiv id=\\\"Equ8\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ8\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:T\\\\left(t\\\\right)=\\\\frac{1}{m}\\\\sum\\\\:_{k=1}^{m}T(k,t)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e8\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the above formulas, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:f\\\\left(P\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:g\\\\left(T\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represent the comprehensive evaluation index of demographic and transportation systems of city \\u003cem\\u003ek\\u003c/em\\u003e in year \\u003cem\\u003et\\u003c/em\\u003e, respectively, and \\u003cem\\u003em\\u003c/em\\u003e is the number of cities.\\u003c/p\\u003e\\u003cp\\u003eIn order to assess the development and interaction level of demographic and transportation systems across various time periods and cities, the coupling coordination degree was graded according to previous research. This study categorized the coupling coordination degree into 5 main categories and 2 subcategories (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The main categories represented the overall level of coupling coordination, with each 0.2 level further divided into 5 levels. The subcategories compared the relative development levels of demographic and transportation, and were further divided into advanced transportation and lagging transportation types.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eClassification of coordination types.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eT\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoordination type\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eg (T)\\u0026thinsp;\\u0026gt;\\u0026thinsp;f (P)\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ef (P)\\u0026thinsp;\\u0026gt;\\u0026thinsp;g (T)\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0.8\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003eT\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHigh coupling\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdvanced transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLagging transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0.6\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003eT\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eModerate coupling\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdvanced transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLagging transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0.4\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003eT\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eLow coupling\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdvanced transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLagging transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0.2\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003eT\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eModerate uncoupling\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdvanced transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLagging transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e0\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u003cem\\u003eT\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSevere uncoupling\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAdvanced transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLagging transportation\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.5 Panel data regression\\u003c/h2\\u003e\\u003cp\\u003ePanel data regression analysis is a method that uses panel data to analyze the relationship between independent variables and dependent variables. It has the advantage of being able to control for the influence of unobserved variables that do not vary over time. Panel data regression methods mainly include fixed effects models and random effects models. The fixed effects model assumes the presence of unobserved fixed effects among individuals, while the random effects model allows for random effects among individuals. In this study, the fixed effects model was used to explore the factors influencing the degree of coupling coordination between demographics and transportation. The regression model is as follows:\\u003cdiv id=\\\"Equ9\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ9\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{T}_{it}=\\\\alpha\\\\:+\\\\beta\\\\:{ECO}_{it}+\\\\gamma\\\\:{ENV}_{it}+\\\\gamma\\\\:{POL}_{it}+{FE}_{i}+ϵ$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e9\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the equation, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{T}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the coupling coordination degree between demographic and transportation, while \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{ECO}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{ENV}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{POL}_{it}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represent a series of variables related to economic, environmental and policy factors respectively. \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{FE}_{i}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e denotes the fixed effects, which are related to the city and do not changing over time.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4 Model\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1 The index system for demographic\\u003c/h2\\u003e\\u003cp\\u003ePopulation urbanization is reflected in various aspects, including spatial aggregation of the population, an increase in the proportion of non-agricultural population, and an improvement in residents\\u0026rsquo; living standards. Referring to existing studies on the selection of demographic indicators(Ren et al., \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Y. Zhang et al., \\u003cspan citationid=\\\"CR101\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), this study selected six commonly used indicators to represent the demographic in terms of population size, population density, urban-rural structure, employment structure, population quality, and living standards. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e provides a detailed explanation of the meanings associated with each indicator. To assess the rationality of the indicator selection, the average correlation coefficients among the indicators were calculated using 2020 data. The results, shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, indicate that the average correlation coefficient for each indicator within the population system does not exceed 0.7, suggesting that the selected indicators possess a certain level of representativeness.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDetailed description of demographic system indicators.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSubsystem\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eIndicator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDescription\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation size\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal population\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePermanent population size\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePopulation density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePermanent population divided by administrative area\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUrban-rural structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUrbanization rate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eUrbanization rate of permanent population\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEmployment structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eIndustrial structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProportion of population in secondary and tertiary industries\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation quality\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEducational level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProportion of population with college education and above\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLiving standard\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDisposable income\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDisposable income of permanent population\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eThe average correlation coefficient for each indicator within demographic system.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIndicator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eTotal population\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003ePopulation density\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eUrbanization rate\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCorrelation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIndicator\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eIndustrial structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEducational level\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDisposable income\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCorrelation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2 The index system for transportation\\u003c/h2\\u003e\\u003cp\\u003eIn evaluating urban transportation levels, previous studies have comprehensively considered the infrastructure construction of various transportation modes, including highways, railways, waterways, and aviation(Dong et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Pradhan et al., \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Different regions may exhibit structural differences in transportation modes. Referring to previous research, this study categorized the transportation system into five subsystems: highways, railways, waterways, aviation, and management. These subsystems encompass a total of 19 evaluation indicators (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). By utilizing 2020 data, the average correlation coefficients among indicators within the five subsystems were calculated. As shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, all average correlation coefficients do not exceed 0.7, indicating that the selected indicators demonstrate good representativeness.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eThe average correlation coefficient of transportation system indicators.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSubsystem\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eIndicator\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eAverage correlation coefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"7\\\" rowspan=\\\"8\\\"\\u003e\\u003cp\\u003eHighway\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGrade highway mileage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eGrade highway network density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.24\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway mileage\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway network density\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.42\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway passenger volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway passenger turnover\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway freight volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eHighway freight turnover\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003eRailway\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRailway passenger volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.60\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRailway passenger turnover\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRailway freight volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRailway freight turnover\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eWater\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWater transport passenger volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWater transport freight volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eAviation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCivil aviation passenger volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCivil aviation freight volume\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNumber of airports\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eManagement\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eProportion of transportation workers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePer capita transport fixed assets investment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.34\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.3 The variables that affect coupling coordination\\u003c/h2\\u003e\\u003cp\\u003eThe factors that influence the degree of coupling coordination between demographic and transportation systems mainly include economic factors, environmental factors, and policy factors(Y. Liu, Nath, et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Sung \\u0026amp; Eom, \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In terms of economic factors, GDP is the most direct variable for measuring urban economic development levels. Developed areas have a strong attraction for populations and possess sufficient funds to construct regional transportation infrastructure, which significantly promotes the development of both demographic and transportation systems. The industrial structure has a significant impact on regional population flow patterns, and an increase in the proportion of non-agricultural employment will bring more job opportunities. Differences in economic structure may create differentiated development dynamics for urban population and economic development. Fixed asset investment promotes urban economic development by expanding industrial scale, while fiscal expenditure and resident consumption enhance economic vitality by facilitating economic circulation. This study constructs an index that reflects urban economic structure by using the ratio of the sum of urban fiscal expenditure and resident consumption to fixed asset investment. Additionally, this study selects the number of patent authorizations per ten thousand people as a variable reflecting urban technological levels. Cities with higher technological levels will generate more high-tech jobs and attract more high-quality talent. At the same time, the rapid flow of factors also relies on efficient transportation infrastructure, which contributes to the coordinated development of demographic transportation systems. The urban environment represents the attractiveness of a city and is an important factor influencing population inflow(R. Du et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eUrban attractiveness encompasses various aspects, including healthcare, education, and greenery. This study selects four indicators to reflect these aspects: the number of doctors per 10,000 people, the number of medical beds per 10,000 people, the number of primary and secondary school teachers per 10,000 people, and the urban greening rate. Regarding policy factors, this study considers the impact of regional development policies, transportation development policies, and population policies on the coupling coordination of population and transportation systems. In 2009, the \\u003cem\\u003eOutline of Reform and Development Plan for the Pearl River Delta Region\\u003c/em\\u003e was released, proposing regional development requirements and guidelines in terms of population carrying capacity, industrial development, and facility construction. The regional development policy variable uses a time dummy variable, set to 1 after 2009 and 0 before 2009. In the comprehensive transportation system plan for the Pearl River Delta, Guangzhou, Shenzhen, and Zhuhai were designated as regional transportation hubs, playing significant roles in the regional transportation system. The transportation development policy variable uses a regional dummy variable, with Guangzhou, Shenzhen, and Zhuhai set to 1 and other cities set to 0. Considering that Guangdong Province has fully relaxed the two-child policy since 2018, the population policy variable uses a time dummy variable, set to 1 after 2018 and 0 before 2018.\\u003c/p\\u003e\\u003cp\\u003eDescriptive statistics of the variables are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. To avoid multicollinearity among variables, those with VIF values greater than 10 were removed, including the industrial structure and healthcare service level_1. Since the model already incorporates individual fixed effects, this study replaced the original variables with interaction terms for transportation development policy and regional development policy.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDescriptive statistics of variables.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDescription\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eObservation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMean\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eStandard deviation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMinimum\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eMaximum\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCoupling coordination degree\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoupling coordination degree between population and transportation system\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e52.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e99.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEconomic structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eRatio of the sum of urban fiscal expenditure and resident consumption to fixed asset investment\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.84\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.68\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4.57\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of technological development\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNumber of patent authorizations per 10000 people\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e19.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e127.13\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of medical services_1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNumber of doctors per 10000 people\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e18.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e33.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of medical services_2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNumber of medical beds per 10000 people\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e30.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e8.69\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e60.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of educational services\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNumber of primary and secondary school teachers per 10000 people\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e77.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.70\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e23.49\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e115.91\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of urban greening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eUrban greening rate\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e37.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e19.27\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e57.94\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTransportation development policy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e=\\u0026thinsp;1 if the city is Guangzhou, Shenzhen, or Zhuhai\\u003c/p\\u003e\\u003cp\\u003e=\\u0026thinsp;0 if the city is another city\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.48\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRegional development policy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e=\\u0026thinsp;1 if the year is after 2009\\u003c/p\\u003e\\u003cp\\u003e=\\u0026thinsp;0 if the year is before 2009\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation policy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e=\\u0026thinsp;1 if the year is after 2018\\u003c/p\\u003e\\u003cp\\u003e=\\u0026thinsp;0 if the year is before 2018\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.35\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5 Results\",\"content\":\"\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.1 The spatiotemporal differentiation of demographic and transportation systems\\u003c/h2\\u003e\\u003cp\\u003eFrom 1990 to 2020, the population aggregation capacity of PRD steadily increased, with its demographic system evolving through three phases. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e illustrate the changes in the comprehensive evaluation indices of the demographic system in PRD and in individual cities, separately. Overall, the comprehensive evaluation index of the demographic system in PRD rose from 0.09 in 1990 to 0.54 in 2020, indicating that PRD played a significant role in accommodating population growth over the past 30 years.\\u003c/p\\u003e\\u003cp\\u003eThe growth trends of the comprehensive evaluation indices of demographic system in individual cities varied. From 1990 to 2000, the reform and opening-up policy prompted cities such as Guangzhou, Shenzhen, Dongguan, and Foshan to undergo industrial transformation, developing export-oriented industries and creating a large number of job opportunities. At the same time, social changes weakened controls over the floating population, thus accelerating urban population aggregation. In contrast, cities like Zhaoqing, Jiangmen, and Huizhou developed more slowly during this period, influenced by the siphoning effect of the more developed cities. Between 2000 and 2015, PRD continued to see population aggregation and urbanization, with steady development of demographic system in each city. The level of development remained relatively stable, forming a clear hierarchy. Shenzhen and Guangzhou were in the first tier, Zhaoqing, Jiangmen, and Huizhou were in the third tier, while the remaining cities were classified in the second tier. From 2015 to 2020, the demographic system in PRD underwent leapfrog development, with ongoing optimization of the population structure and significant improvements in residents\\u0026rsquo; living standards, and the comprehensive evaluation indices of demographic system in each city increased substantially.\\u003c/p\\u003e\\u003cp\\u003eCompared to the demographic system, the comprehensive evaluation index of the transportation system in PRD has grown relatively slowly. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the comprehensive evaluation index of transportation system was only 0.21 in 2020, an increase of 0.17 compared to 1990. The higher concentration of transportation system resources in PRD reflects the central characteristics of transportation planning. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e illustrates the changes in the comprehensive evaluation indices of transportation system in each city of PRD from 1990 to 2020. Guangzhou serves as the primary transportation hub, followed by Shenzhen and Zhuhai, with their transportation system indices significantly leading the other cities. As the capital of Guangdong Province, Guangzhou holds significant political, economic, and cultural importance within the region. Since 2000, Guangzhou\\u0026rsquo;s transportation system has developed rapidly, consistently outpacing other cities in transportation infrastructure. Shenzhen and Zhuhai, located along the coast near Guangzhou, have benefited from their advantageous geographic positions, resulting in relatively higher allocation of transportation resources. Since 2005, the development level of the transportation system in Shenzhen and Zhuhai has seen significant improvements. The indices of transportation system in the remaining six cities show a fluctuating growth trend and are notably lagging behind the top three cities.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.2 Spatial and temporal changes in coupling coordination between demographic and transportation systems\\u003c/h2\\u003e\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e illustrate the changes in coupling coordination of demographic and transportation systems in PRD and individual cities from 1990 to 2020. Overall, the coupling coordination of demographic and transportation systems in PRD shows a steady upward trend. From 1990 to 2020, the coupling coordination increased from 0.21 to 0.54, gradually shifting from moderate uncoupling to low coupling. In terms of individual cities, the dynamic trends of the coupling coordination in different cities align with the overall changes, but there are significant differences in relative levels. Guangzhou consistently maintained the highest coupling coordination across all years, rising from 0.41 to 0.85. Shenzhen followed closely, with its coupling coordination increasing from 0.22 to 0.75. Zhuhai\\u0026rsquo;s coupling coordination is slightly above the overall level, while the remaining cities below the overall level.\\u003c/p\\u003e\\u003cp\\u003eBased on the standards in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the coordination types of demographic and transportation systems in various cities of PRD have been classified, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. In this figure, the surface elements represent the classification results of the primary categories, while the point elements represent those of the secondary categories. Overall, PRD's coordination type of demographic and transportation systems exhibits a circular and stepped development pattern. According to the dynamic changes in the coordination type of each city, the PRD can be divided into three circles. Guangzhou and Shenzhen constitute the core circle, Foshan, Zhuhai, Dongguan, Zhongshan, and Huizhou constitute the peripheral circle, while Zhaoqing and Jiangmen comprise the outer circle. The change in the coordination type of demographic and transportation systems follows the direction of the core, peripheral, and outer circles. In 1995, with the exception of the two cities in the outer circle, all other cities were classified as moderate uncoupling or higher. In 2000, the cities in the outer circle upgraded to moderate uncoupling. In 2015, the core circle upgraded to moderate coupling, while the peripheral circle transitioned to low coupling, and the outer circle remained at moderate uncoupling. In 2020, Guangzhou was the only city in PRD to achieve high coupling, while Shenzhen and Zhuhai were classified as moderate coupling. Other cities in peripheral circle were all classified as low coupling, and the two cities in the outer circle were categorized as moderate uncoupling.\\u003c/p\\u003e\\u003cp\\u003eFrom the perspective of the relative development of demographic and transportation systems, since 2005, the comprehensive evaluation index of Guangzhou transportation system has consistently exceeded that of its demographic system. In contrast, other cities in PRD have been classified as lagging transportation type. This disparity can be attributed to the allocation of transportation system resources, which are primarily concentrated in the core circle and not evenly distributed across all cities.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e5.3 Factors related the coupling coordination of demographic and transportation\\u003c/h2\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e presents the results of the panel regression analysis. The adjusted R-squared value of the model exceeds 0.95, and it passed the F-test at a significance level of 0.1%, indicating that economic, environmental, and policy factors significantly influence the coupling coordination between demographic and transportation systems. GDP, economic structure, technological level, urban services, regional policies, and transportation policies all impact the coordinated development of demographic and transportation systems at different significance levels. Specifically, the coefficient of GDP is 0.0569, which indicates that at the 0.1% significance level, a 1% increase in GDP will lead to an average increase of 0.0569 in the coupling coordination. The coefficient for economic structure is positive at the 0.1% significance level, suggesting that a consumption-oriented socio-economic structure significantly promotes the coupling coordination. Technological level also significantly enhances the coupling coordination at the 0.1% significance level, although its effect is relatively weak, with a coefficient of only 0.0009. The coefficient for urban medical services is positive, while that for educational services is negative, indicating a differential impact of medical and educational facilities on the coupling coordination. The interaction term between regional development policy and transportation development policy has a coefficient of 0.0575, with a significance level of 0.1%, suggesting that since 2009, the coupling coordination in Guangzhou, Shenzhen, and Zhuhai has increased by an average of 0.0575 due to policy dividends. The study could not identify significant relationships of other influencing factors, which may be attributed to the limited sample size or unclear sample differences. Specifically, only the data from 2020 was affected by the two-child policy, resulting in a limited number of time series observations, making it impossible to quantify the relationship at this time. Additionally, the green space rate in cities is generally high, and with the provision of ample green space, the impact of increasing urban greening rates on population mobility may no longer be significant.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003ePanel data regression results.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eVariable\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCoefficient\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eStandard deviation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eP-value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGDP\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0569***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0049\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEconomic structure\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0219***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0051\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of technological development\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0009***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of medical services\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0010*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.050\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of educational services\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e-0.0008**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0003\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.010\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLevel of urban greening\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.565\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTransportation * regional development policy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0575***\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0141\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.000\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePopulation policy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0162\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0149\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.282\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eConstant\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.0622\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.0380\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.109\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eObservation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e63\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAdjusted R squared\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e0.9815\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eF-value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e206.38***\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFixed effect\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eYES\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eNote: * p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; ** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01; *** p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"6 Discussion\",\"content\":\"\\u003cp\\u003eMany studies define transportation lag as a development bottleneck caused by underinvestment, flawed decision-making, or imbalanced facility distribution, resulting in infrastructure falling behind the pace of urbanization (W. Wang \\u0026amp; Xie, \\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Based on panel data from nine cities between 1990 and 2020, this study finds no generalized absolute undersupply in the Pearl River Delta (PRD); instead, it exhibits internally unbalanced relative lag. Among them, Guangzhou and Shenzhen consistently show transportation indices exceeding population indices (transport/population ratio\\u0026thinsp;\\u0026gt;\\u0026thinsp;1.2 in 2020), while Zhaoqing and Jiangmen fall below 0.8. Consequently, although the region's overall coupling coordination degree increased from 0.21 to 0.54, a hierarchical pattern\\u0026mdash;core, periphery, and outer circle\\u0026mdash;remains. Consistent with previous research, this gradient stems from excessive transportation resource concentration in core cities and weak connectivity in peripheral nodes and intercity corridors. The result is spatial fragmentation of network connectivity and over-aggregation of infrastructure (Q. Du et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022b\\u003c/span\\u003e; J. He et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2024b\\u003c/span\\u003e; N. Li et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2023b\\u003c/span\\u003e). The persistent outperformance of transportation indices over population indices in core cities, contrasted with widespread transportation lag elsewhere, highlights internal resource allocation imbalances. Therefore, improvements should focus on intercity trunk corridors, secondary hub enhancements, and public fiscal rebalancing to mitigate core siphoning effects, enhance peripheral accessibility, and improve network resilience and regional equity.\\u003c/p\\u003e\\u003cp\\u003eWhile prior studies emphasize the role of economic scale and investment-driven growth in population\\u0026ndash;transportation coordination, they often overlook the influence of consumption structure and technological spillovers (Lu \\u0026amp; Li, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Our findings also suggest that consumption upgrading expands the spatial domain of the service sector and increases everyday mobility demand. Meanwhile, technological advancement\\u0026mdash;through innovation activities\\u0026mdash;drives demand for efficient transport systems, in turn attracting population migration and urban expansion, thereby facilitating coordinated urbanization through positive feedback loops. These findings corroborate similar conclusions from the Yangtze River Delta, suggesting that the PRD should seize the current window of digital economy and consumption upgrading to accelerate multi-modal integration and intelligent transportation deployment, providing sustainable support for the diffusion of service industries and advanced manufacturing chains (Chang et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe \\u0026ldquo;Outline of the Reform and Development Plan for the Pearl River Delta\\u0026rdquo; is a strategic policy document guiding industrial layout, urban development, infrastructure networks, technological innovation, and ecological protection in the region, with a strong emphasis on regional coordination and inter-city linkages (Cheshmehzangi \\u0026amp; Tang, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). From 2010 to 2020, the coordinated development policies of the PRD yielded notable progress: the region's overall strength and coupling coordination steadily improved\\u0026mdash;permanent population increased by approximately 40%, per capita income doubled, expressway mileage rose by 20%, and total mileage of classified roads doubled. However, policy dividends have shown a \\u0026ldquo;hub-lock-in\\u0026rdquo; effect, with the three core cities clearly leading in population attractiveness, transport accessibility, and port throughput. In contrast, non-core cities suffer from lower policy gains and weaker accessibility, limiting their ability to absorb high-value-added industries. The next stage must maintain core competitiveness while linking transportation investment with industrial transfer and public service improvement through intercity rail express lines, integrated transit corridors, and intelligent freight hubs. This would support a \\u0026ldquo;multi-core\\u0026ndash;multi-node\\u0026ndash;networked\\u0026rdquo; layout to prevent the structural fragility caused by excessive monocentric concentration.\\u003c/p\\u003e\\u003cp\\u003eUrban services such as healthcare and education can significantly enhance urban attractiveness, support the urbanization process, and increase demand for transport infrastructure. This study also validates the positive role of healthcare services: medical resources have a positive effect on the coupling coordination degree (0.0010, p\\u0026thinsp;=\\u0026thinsp;0.05), whereas the supply of basic education shows a negative effect. High-quality healthcare sustainably attracts both permanent residents and cross-regional patients. In contrast, universal primary education, due to its numerical balance and shorter migration cycles, contributes less to long-term population agglomeration and cross-city transport demand. Its marginal attractiveness is more dependent on \\u0026ldquo;quality\\u0026rdquo; (H. Zhang et al., \\u003cspan citationid=\\\"CR101\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Educational services generally struggle to drive long-term or cross-regional transport demand. Migration for education tends to be temporary or phased, meaning that while education resources may attract short-term floating populations, they rarely provide lasting urban appeal or significant transportation load.\\u003c/p\\u003e\\u003cp\\u003eIn terms of limitations, this study uses five-year interval data, resulting in a limited sample size that may not fully capture short-cycle shocks such as the two-child policy. The lack of continuous time-series data may reduce the reliability of some conclusions. Moreover, the absence of micro-level mobility trajectory data constrains the granularity of commuting and logistics chain analysis. Future research should expand the sample scope to include more cities and longer time periods, in order to systematically evaluate the impact mechanisms of policy and economic factors on population\\u0026ndash;transport coupling coordination. Additionally, future studies should consider incorporating broader environmental and social dimensions\\u0026mdash;such as public safety and health services\\u0026mdash;to broaden analytical perspectives and enhance the explanatory power and practical relevance of the research framework.\\u003c/p\\u003e\"},{\"header\":\"7 Conclusion and policy implications\",\"content\":\"\\u003cp\\u003eBased on long-term panel data from nine cities in the Pearl River Delta (PRD) spanning 1990\\u0026ndash;2020, this study applies range standardization, an entropy-weighted coupling coordination model, and fixed-effect spatial Durbin regression to systematically reveal the spatiotemporal patterns and driving mechanisms of regional population\\u0026ndash;transport system co-evolution. The results show that:\\u003c/p\\u003e\\u003cp\\u003e(1) The overall coupling coordination degree in the PRD rose from 0.21 to 0.54, indicating a gradual transition from \\\"moderate imbalance\\\" to \\\"low-level coupling.\\\" However, a significant \\u0026ldquo;core\\u0026ndash;periphery\\u0026ndash;outer ring\\u0026rdquo; gradient persists. Guangzhou (0.85) and Shenzhen (0.75) have entered a high coupling zone, while Zhaoqing and Jiangmen remain in the moderate imbalance category; (2) A pronounced spatial mismatch exists between population and transportation resources across the PRD. Since 2005, Guangzhou\\u0026rsquo;s composite transport index has consistently exceeded its population index, whereas most other cities exhibit characteristics of transportation-lagged urban development; (3) Panel regression analysis reveals significant positive elasticities between coupling coordination and economic growth, consumption-led industrial structure, and technological progress. Specifically, the elasticity coefficients are 0.0569 for GDP, 0.0219 for consumption-oriented economy, and 0.0009 for patent density (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), confirming the \\\"consumption\\u0026ndash;technology\\u0026ndash;transport\\u0026ndash;population\\\" positive feedback hypothesis; (4) Public services and policy effects show clear differentiation: medical resources significantly promote coupling coordination (0.0010, p\\u0026thinsp;=\\u0026thinsp;0.05), while basic education exhibits a weak negative effect (\\u0026ndash;0.0008, p\\u0026thinsp;=\\u0026thinsp;0.01). Notably, the interaction term for regional development and transport policy reaches a coefficient of 0.0575 (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), indicating that following the coordinated integration policy launched in 2009, core cities such as Guangzhou, Shenzhen, and Zhuhai experienced an annual additional increase of nearly 0.06 in their coupling degree. This confirms the magnifying effect of macro-level integration planning. However, excessive concentration of resources and functions in single hubs has objectively weakened the ability of peripheral cities to obtain transport investment and host high-value-added activities, thereby exacerbating regional development imbalances.\\u003c/p\\u003e\\u003cp\\u003eBased on the above findings, this study proposes several policy recommendations for improving population\\u0026ndash;transport coupling coordination in the PRD:\\u003c/p\\u003e\\u003cp\\u003eFirst, activate regional synergistic growth through a \\u0026ldquo;multi-core\\u0026ndash;networked\\u0026rdquo; transport structure. Core cities should continue to consolidate their roles as international-level integrated hubs, while establishing 30\\u0026ndash;60 minutes commuting zones and 1.5-hour industrial cooperation zones through intercity rail express, cross-river corridors, and trunk\\u0026ndash;branch highway systems to enhance accessibility and network resilience of peripheral nodes (Y. Wang, Cao, et al., \\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Simultaneously, leverage special bonds, toll rebates, and land premium redistribution to align transport investments with the staged transfer of manufacturing and service industries, thereby avoiding further internal imbalance. By combining consumption upgrading and digital economy diffusion, accelerate deployment of 5G-V2X, vehicle\\u0026ndash;infrastructure coordination systems, and urban traffic control centers. Open transport data APIs to attract private sector participation in autonomous bus services, smart logistics hubs, and multimodal transport pilots\\u0026mdash;bridging network gaps in peripheral cities through technological spillovers.\\u003c/p\\u003e\\u003cp\\u003eSecond, enhance the quality of population flows through equitable public services and collaborative governance (Barrutia et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). At the provincial level, a \\u0026ldquo;Greater Bay Area Population\\u0026ndash;Transport Coordination Monitoring Index Cluster\\u0026rdquo; should be established. This would use mobile signaling, transit IC cards, and remote sensing imagery to track coupling dynamics in real time, linking monitoring outcomes with fiscal transfers and infrastructure investment quotas to create dynamic incentives. For peripheral cities with weak healthcare capacity, accessibility to quality medical care can be improved through branch hospital deployment, telemedicine centers, and intercity medical shuttle lines. In the education sector, efforts should be made to situate quality high schools and vocational\\u0026ndash;educational integration platforms in outer-ring cities, supported by school transit rail lines and regional public transit networks to enhance local educational magnetism and curb talent outflow from non-core cities. Through differentiated and refined coordination policies in transport and public services, the PRD could transition from \\u0026ldquo;low-level coupling\\u0026rdquo; to \\u0026ldquo;high-quality coordination,\\u0026rdquo; thus providing sustainable support for the Greater Bay Area's competitiveness among global city clusters.\\u003c/p\\u003e\\u003cp\\u003eIn summary, this paper develops an integrated analytical framework combining multidimensional indicators, coupling models, and spatial econometrics, with a specific focus on the nine cities of the Pearl River Delta. It is the first to systematically quantify the joint effects of core\\u0026ndash;periphery gradients, consumption\\u0026ndash;technology feedback loops, and policy coordination on population\\u0026ndash;transport coupling evolution. The findings offer actionable decision-making paths and empirical evidence for population and transport governance in rapidly urbanizing regions.\\u003c/p\\u003e\\u003cp\\u003eHowever, the study faces limitations in data granularity and variable dimensions. First, five-year interval statistical data may not capture short-cycle shocks; second, micro-level travel behavior and real-time traffic flow data are not incorporated into the analysis. Future research could integrate mobile signaling, GPS trajectories, and multi-source remote sensing to deepen exploration of policy timeliness and individual travel response mechanisms, thereby refining the theory and practice of population\\u0026ndash;transport coordination under regional integration.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (Grant numbers (42201183) ;2023 New Recruitment Talents Financial Subsidy for Scientific Research Initiatives (1270110343).\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eZhaoya Gong provided supervision and resources. Di Lyu led the writing and revision of the manuscript, with contributions from Weiwang Zhu. Weiwang Zhu and Libin Ouyang collected and analysed the research data.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe authors declare that the data supporting the conclusions are described in the paper. Due to privacy and security restrictions, certain datasets analyzed in this study are not publicly available. Interested researchers can request access by contacting the corresponding author, Zhaoya Gong, subject to evaluation and compliance with relevant policies.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAdams, S. B. (2021). From orchards to chips: Silicon Valley\\u0026rsquo;s evolving entrepreneurial ecosystem. In The dynamics of entrepreneurial ecosystems (pp. 15\\u0026ndash;35). Routledge.\\u003c/li\\u003e\\n\\u003cli\\u003eBarrutia, J. M., Echebarria, C., Aguado-Moralejo, I., Apaolaza-Ib\\u0026aacute;\\u0026ntilde;ez, V., \\u0026amp; Hartmann, P. (2022). Leading smart city projects: Government dynamic capabilities and public value creation. Technological Forecasting and Social Change, 179, 121679. https://doi.org/10.1016/j.techfore.2022.121679\\u003c/li\\u003e\\n\\u003cli\\u003eCalder\\u0026oacute;n, C., Cantu, C., \\u0026amp; Chuhan-Pole, P. (2018). Infrastructure Development in Sub-Saharan Africa: A Scorecard (SSRN Scholarly Paper 3172503). Social Science Research Network. https://papers.ssrn.com/abstract=3172503\\u003c/li\\u003e\\n\\u003cli\\u003eCao, W., Shi, F., Zhu, Q., \\u0026amp; Li, Q. (2025). How access distance to high-speed rail stations affects individual travel behavior in small cities: A case study of the Chengdu-Chongqing corridor. Journal of Public Transportation, 27, 100127. https://doi.org/10.1016/j.jpubtr.2025.100127\\u003c/li\\u003e\\n\\u003cli\\u003eChang, K., Zhang, H., \\u0026amp; Li, B. (2024). The Impact of Digital Economy and Industrial Agglomeration on the Changes of Industrial Structure in the Yangtze River Delta. Journal of the Knowledge Economy, 15(2), 9207\\u0026ndash;9227. https://doi.org/10.1007/s13132-023-01448-w\\u003c/li\\u003e\\n\\u003cli\\u003eChen J., \\u0026amp; Pan L. (2024, October 15). Impact of the Coupling Coordination Degree of Human Capital and Infrastructure on High-Quality Economic Development: Empirical Evidence from Chinese Cities. | EBSCOhost. https://doi.org/10.3390/su16208905\\u003c/li\\u003e\\n\\u003cli\\u003eChen, W., Liang, Y., Zhu, Y., Chang, Y., Luo, K., Wen, H., Li, L., Yu, Y., Wen, Q., Chen, C., Zheng, K., Gao, Y., Zhou, X., \\u0026amp; Zheng, Y. (2024). Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2403.14151\\u003c/li\\u003e\\n\\u003cli\\u003eCheshmehzangi, A., \\u0026amp; Tang, T. (2022). Pearl River Delta City Cluster: From Dual-Core Structure Economic Development Strategies to Regional Economic Plans. In A. Cheshmehzangi \\u0026amp; T. Tang (Eds.), China\\u0026rsquo;s City Cluster Development in the Race to Carbon Neutrality (pp. 63\\u0026ndash;75). Springer Nature. https://doi.org/10.1007/978-981-19-7673-5_5\\u003c/li\\u003e\\n\\u003cli\\u003eColon, C., Hallegatte, S., \\u0026amp; Rozenberg, J. (2021). Criticality analysis of a country\\u0026rsquo;s transport network via an agent-based supply chain model. Nature Sustainability, 4(3), 209\\u0026ndash;215.\\u003c/li\\u003e\\n\\u003cli\\u003eDinlersoz, E. M., \\u0026amp; Fu, Z. (2022). Infrastructure investment and growth in China: A quantitative assessment. Journal of Development Economics, 158, 102916. https://doi.org/10.1016/j.jdeveco.2022.102916\\u003c/li\\u003e\\n\\u003cli\\u003eDong, L., Longwu, L., Zhenbo, W., Liangkan, C., \\u0026amp; Faming, Z. (2021). Exploration of coupling effects in the Economy\\u0026ndash;Society\\u0026ndash;Environment system in urban areas: Case study of the Yangtze River Delta Urban Agglomeration. Ecological Indicators, 128, 107858.\\u003c/li\\u003e\\n\\u003cli\\u003eDu, Q., Wang, X., Li, Y., Zou, P. X. W., Han, X., \\u0026amp; Meng, M. (2022a). An analysis of coupling coordination relationship between regional economy and transportation: Empirical evidence from China. Environmental Science and Pollution Research, 29(23), 34360\\u0026ndash;34378. https://doi.org/10.1007/s11356-022-18598-0\\u003c/li\\u003e\\n\\u003cli\\u003eDu, Q., Wang, X., Li, Y., Zou, P. X. W., Han, X., \\u0026amp; Meng, M. (2022b). An analysis of coupling coordination relationship between regional economy and transportation: Empirical evidence from China. Environmental Science and Pollution Research, 29(23), 34360\\u0026ndash;34378. https://doi.org/10.1007/s11356-022-18598-0\\u003c/li\\u003e\\n\\u003cli\\u003eDu, R., Liu, K., Zhao, D., \\u0026amp; Fang, Q. (2024). Urban amenity and urban economic resilience: Evidence from China. Frontiers in Public Health, 12, 1392908.\\u003c/li\\u003e\\n\\u003cli\\u003eDuan, L., Niu, D., Sun, W., \\u0026amp; Zheng, S. (2021). Transportation infrastructure and capital mobility: Evidence from China\\u0026rsquo;s high-speed railways. The Annals of Regional Science, 67(3), 617\\u0026ndash;648. https://doi.org/10.1007/s00168-021-01059-w\\u003c/li\\u003e\\n\\u003cli\\u003eErcan, T., Onat, N. C., Tatari, O., \\u0026amp; Mathias, J.-D. (2017). Public transportation adoption requires a paradigm shift in urban development structure. Journal of Cleaner Production, 142, 1789\\u0026ndash;1799. https://doi.org/10.1016/j.jclepro.2016.11.109\\u003c/li\\u003e\\n\\u003cli\\u003eErkel, B. (2023). Policy Appeal and Tech Talent Migration: A Comparative Case Study of Australia and the United States Assessing Policy Elements That Determine Each Country\\u0026rsquo;s Attractiveness for High-Skilled Tech Migrants.\\u003c/li\\u003e\\n\\u003cli\\u003eFranconi, L., Mantuano, M., \\u0026amp; Ichim, D. (2024). Population grid and location quotient of land cover to capture the urban-rural nature of labour market areas in Italy. GeoJournal, 89(1), 6.\\u003c/li\\u003e\\n\\u003cli\\u003eGarcia-L\\u0026oacute;pez, M.-\\u0026Agrave;., Pasidis, I., \\u0026amp; Viladecans-Marsal, E. (2024). Suburbanization and transportation in European cities. Journal of Economic Geography, 24(6), 843\\u0026ndash;869.\\u003c/li\\u003e\\n\\u003cli\\u003eGuo, R., Ning, L., \\u0026amp; Chen, K. (2022). How do human capital and R\\u0026amp;D structure facilitate FDI knowledge spillovers to local firm innovation? A panel threshold approach. The Journal of Technology Transfer, 47(6), 1921\\u0026ndash;1947.\\u003c/li\\u003e\\n\\u003cli\\u003eGuo, S., Huang, Q., \\u0026amp; Wen, C. (2024). Analysis of the Spatial Heterogeneity of Commuting Flows in Beijing: Perspectives from Mobile Phone Data. Sensors and Materials, 36(10), 4455. https://doi.org/10.18494/SAM5253\\u003c/li\\u003e\\n\\u003cli\\u003eGuo, X., \\u0026amp; Xu, J. (2025). The impact of China\\u0026rsquo;s 2014 Hukou reform on economic growth. Economic Analysis and Policy, 85, 641\\u0026ndash;655. https://doi.org/10.1016/j.eap.2024.12.031\\u003c/li\\u003e\\n\\u003cli\\u003eGuti\\u0026eacute;rrez, J., Conde\\u0026ccedil;o-Melhorado, A., \\u0026amp; Mart\\u0026iacute;n, J. C. (2010). Using accessibility indicators and GIS to assess spatial spillovers of transport infrastructure investment. Journal of Transport Geography, 18(1), 141\\u0026ndash;152. https://doi.org/10.1016/j.jtrangeo.2008.12.003\\u003c/li\\u003e\\n\\u003cli\\u003eHan, D., Attipoe, S. G., Han, D., \\u0026amp; Cao, J. (2023). Does transportation infrastructure construction promote population agglomeration? Evidence from 1838 Chinese county-level administrative units. Cities, 140, 104409.\\u003c/li\\u003e\\n\\u003cli\\u003eHe, J., Yang, S., Deng, S., Ye, J., \\u0026amp; Chen, H. (2024a). Research on the Decoupling Relationship between Transportation Land and Population Growth: A Case of Guangdong Province in China. Land, 13(4), 484.\\u003c/li\\u003e\\n\\u003cli\\u003eHe, J., Yang, S., Deng, S., Ye, J., \\u0026amp; Chen, H. (2024b). Research on the Decoupling Relationship between Transportation Land and Population Growth: A Case of Guangdong Province in China. Land, 13(4), 484. https://doi.org/10.3390/land13040484\\u003c/li\\u003e\\n\\u003cli\\u003eHe, Q., Musterd, S., \\u0026amp; Boterman, W. (2023). Geographical structure of the local segregation of migrants in (sub) urban China. GeoJournal, 88(2), 1449\\u0026ndash;1467.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang, F., Yi, P., Wang, J., Li, M., Peng, J., \\u0026amp; Xiong, X. (2022). A dynamical spatial-temporal graph neural network for traffic demand prediction. Information Sciences, 594, 286\\u0026ndash;304. https://doi.org/10.1016/j.ins.2022.02.031\\u003c/li\\u003e\\n\\u003cli\\u003eHuang, S., \\u0026amp; Lin, Y. (2025). Research on High-Quality Urbanization Development and Optimization Pathways Based on the Coupling Coordination Perspective of \\u0026ldquo;Population\\u0026ndash;Land\\u0026ndash;Economy\\u0026ndash;Environment\\u0026rdquo;: A Case Study of Jiangsu Province, China. Land, 14(2), 435.\\u003c/li\\u003e\\n\\u003cli\\u003eJaramillo Lizana, J. (2025). Regional Inequality in Peru: Causes, Effects, and Strategies for Equitable Development. https://doi.org/10.2139/ssrn.5020164\\u003c/li\\u003e\\n\\u003cli\\u003eJiang, W., \\u0026amp; Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207, 117921. https://doi.org/10.1016/j.eswa.2022.117921\\u003c/li\\u003e\\n\\u003cli\\u003eJin, F., Jiao, J., Qi, Y., \\u0026amp; Yang, Y. (2017). Evolution and geographic effects of high-speed rail in East Asia: An accessibility approach. Journal of Geographical Sciences, 27(5), 515\\u0026ndash;532. https://doi.org/10.1007/s11442-017-1390-8\\u003c/li\\u003e\\n\\u003cli\\u003eKomornicki, T., \\u0026amp; Szejgiec-Kolenda, B. (2023). The development of transport infrastructure in Poland and the role of spatial planning and cohesion policy in investment processes. Planning Practice \\u0026amp; Research, 38(5), 694\\u0026ndash;713. https://doi.org/10.1080/02697459.2020.1852677\\u003c/li\\u003e\\n\\u003cli\\u003eKozera, A., Satoła, Ł., \\u0026amp; Standar, A. (2024). European Union co-funded investments in low-emission and green energy in urban public transport in Poland. Renewable and Sustainable Energy Reviews, 200, 114530. https://doi.org/10.1016/j.rser.2024.114530\\u003c/li\\u003e\\n\\u003cli\\u003eLi, N., Song, Y., Xia, W., \\u0026amp; Fu, S.-N. (2023a). Regional transportation integration and high-quality economic development, coupling coordination analysis, in the Yangtze River Delta, China. Systems, 11(6), 279.\\u003c/li\\u003e\\n\\u003cli\\u003eLi, N., Song, Y., Xia, W., \\u0026amp; Fu, S.-N. (2023b). Regional Transportation Integration and High-Quality Economic Development, Coupling Coordination Analysis, in the Yangtze River Delta, China. Systems, 11(6), 279. https://doi.org/10.3390/systems11060279\\u003c/li\\u003e\\n\\u003cli\\u003eLi, T., Dong, Y., Wei, X., Zhou, H., \\u0026amp; Li, Z. (2024). The rapid prosperity of China\\u0026rsquo;s Pearl River Delta from the perspective of social\\u0026ndash;ecological coupling: Implications for sustainable management. Scientific Reports, 14(1), 19914.\\u003c/li\\u003e\\n\\u003cli\\u003eLi, X., Li, Y., Jia, T., Zhou, L., \\u0026amp; Hijazi, I. H. (2022). The six dimensions of built environment on urban vitality: Fusion evidence from multi-source data. Cities, 121, 103482. https://doi.org/10.1016/j.cities.2021.103482\\u003c/li\\u003e\\n\\u003cli\\u003eLi, Z., Tang, J., Feng, T., Liu, B., Cao, J., Yu, T., \\u0026amp; Ji, Y. (2024). Investigating urban mobility through multi-source public transportation data: A multiplex network perspective. Applied Geography, 169, 103337. https://doi.org/10.1016/j.apgeog.2024.103337\\u003c/li\\u003e\\n\\u003cli\\u003eLiao, Y., Yeh, S., \\u0026amp; Jeuken, G. S. (2019). From individual to collective behaviours: Exploring population heterogeneity of human mobility based on social media data. EPJ Data Science, 8(1), 1\\u0026ndash;22.\\u003c/li\\u003e\\n\\u003cli\\u003eLiao, Z., \\u0026amp; Liang, S. (2024). Spatiotemporal differences and influencing factors of urban vitality and urban expansion coupling coordination in the Pearl River Delta. Heliyon, 10(4).\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, J., Meng, B., Xu, J., \\u0026amp; Li, R. (2023). Exploring Public Transportation Supply\\u0026ndash;Demand Structure of Beijing from the Perspective of Spatial Interaction Network. ISPRS International Journal of Geo-Information, 12(6), 213. https://doi.org/10.3390/ijgi12060213\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, L., \\u0026amp; Zhang, M. (2021). The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development\\u0026mdash;Updated Evidence from China\\u0026rsquo;s Mid-Yangtze River City-Cluster Region. Sustainability, 13(8), 4227. https://doi.org/10.3390/su13084227\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Y., Jia, R., Ye, J., \\u0026amp; Qu, X. (2022). How machine learning informs ride-hailing services: A survey. Communications in Transportation Research, 2, 100075. https://doi.org/10.1016/j.commtr.2022.100075\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Y., Nath, N., Murayama, A., \\u0026amp; Manabe, R. (2022). Transit-oriented development with urban sprawl? Four phases of urban growth and policy intervention in Tokyo. Land Use Policy, 112, 105854.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Y., Tang, D., \\u0026amp; Wang, F. (2024). Research on the spatial spillover effect of high-speed railway on the income of urban residents in China. Humanities and Social Sciences Communications, 11(1), 1\\u0026ndash;13.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu, Z., Xia, H., \\u0026amp; Zhang, T. (2024). A review of research methods on the coupling relationship between urban rail transit and urban space: Revealing spatiotemporal relationships through big data. International Journal of Digital Earth, 17(1), 2339363. https://doi.org/10.1080/17538947.2024.2339363\\u003c/li\\u003e\\n\\u003cli\\u003eLu, C., Hong, W., Wang, Y., \\u0026amp; Zhao, D. (2021). Study on the Coupling Coordination of Urban Infrastructure and Population in the Perspective of Urban Integration. IEEE Access, 9, 124070\\u0026ndash;124086. https://doi.org/10.1109/ACCESS.2021.3110368\\u003c/li\\u003e\\n\\u003cli\\u003eLu, C., \\u0026amp; Li, J. (2025). Influence of Population Agglomeration on Regional High-Quality Economic Development: Evidence from Guangdong Province in China (SSRN Scholarly Paper 5276411). Social Science Research Network. https://doi.org/10.2139/ssrn.5276411\\u003c/li\\u003e\\n\\u003cli\\u003eMa, F., Guo, Y., Yuen, K. F., Woo, S., \\u0026amp; Shi, W. (2019). Association between New Urbanization and Sustainable Transportation: A Symmetrical Coupling Perspective. Symmetry, 11(2), 192. https://doi.org/10.3390/sym11020192\\u003c/li\\u003e\\n\\u003cli\\u003eMacias, L. H., Ravalet, E., \\u0026amp; R\\u0026eacute;rat, P. (2025). How does telework impact daily and residential mobilities: New geographies of working and living in Switzerland. Applied Geography, 178, 103591. https://doi.org/10.1016/j.apgeog.2025.103591\\u003c/li\\u003e\\n\\u003cli\\u003eMagazzino, C., \\u0026amp; Mele, M. (2021). On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics, 88, 100947. https://doi.org/10.1016/j.retrec.2020.100947\\u003c/li\\u003e\\n\\u003cli\\u003eMagoutas, A., Manolopoulos, D., Tsoulfas, G. T., \\u0026amp; Koudeli, M. (2023). Economic impact of road transportation infrastructure projects: The case of Egnatia Odos Motorway. European Planning Studies, 31(4), 780\\u0026ndash;801. https://doi.org/10.1080/09654313.2022.2082243\\u003c/li\\u003e\\n\\u003cli\\u003eMasahiko, M. (2022). Why Do Firms Concentrate in Tokyo? An Economic Geography Perspective. Japan Labor Issues/The Japan Institute for Labour Policy and Training, International Research Exchange Section [編], 6(36\\u0026ndash;40), 43\\u0026ndash;54.\\u003c/li\\u003e\\n\\u003cli\\u003eMitropoulos, L., Karolemeas, C., Tsigdinos, S., Vassi, A., \\u0026amp; Bakogiannis, E. (2023). A composite index for assessing accessibility in urban areas: A case study in Central Athens, Greece. Journal of Transport Geography, 108, 103566. https://doi.org/10.1016/j.jtrangeo.2023.103566\\u003c/li\\u003e\\n\\u003cli\\u003eMuzira, S., \\u0026amp; Qiao, W. (2022). To Pave or Not to Pave: A Framework for Systematic Decision Making in the Choice of Paving Technologies for Rural Roads. Transportation Research Record: Journal of the Transportation Research Board, 2676(7), 46\\u0026ndash;54. https://doi.org/10.1177/03611981221076446\\u003c/li\\u003e\\n\\u003cli\\u003eNkemgha, G. Z., Nchofoung, T. N., \\u0026amp; Sundjo, F. (2023). Financial development and human capital thresholds for the infrastructure development-industrialization nexus in Africa. Cities, 132, 104108. https://doi.org/10.1016/j.cities.2022.104108\\u003c/li\\u003e\\n\\u003cli\\u003ePeng, H., Du, Y., Liu, Z., Yi, J., Kang, Y., \\u0026amp; Fei, T. (2019). Uncovering patterns of ties among regions within metropolitan areas using data from mobile phones and online mass media. GeoJournal, 84, 685\\u0026ndash;701.\\u003c/li\\u003e\\n\\u003cli\\u003ePersyn, D., Barbero, J., D\\u0026iacute;az‐Lanchas, J., Lecca, P., Mandras, G., \\u0026amp; Salotti, S. (2023). The ripple effects of large‐scale transport infrastructure investment. Journal of Regional Science, 63(4), 755\\u0026ndash;792. https://doi.org/10.1111/jors.12639\\u003c/li\\u003e\\n\\u003cli\\u003ePokharel, R., Bertolini, L., \\u0026amp; Te Br\\u0026ouml;mmelstroet, M. (2023). How does transportation facilitate regional economic development? A heuristic mapping of the literature. Transportation Research Interdisciplinary Perspectives, 19, 100817. https://doi.org/10.1016/j.trip.2023.100817\\u003c/li\\u003e\\n\\u003cli\\u003ePradhan, R. P., Arvin, M. B., \\u0026amp; Nair, M. (2021). Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities, 115, 103213.\\u003c/li\\u003e\\n\\u003cli\\u003eRen, Y., Tian, Y., \\u0026amp; Xiao, X. (2022). Spatial effects of transportation infrastructure on the development of urban agglomeration integration: Evidence from the Yangtze River Economic Belt. Journal of Transport Geography, 104, 103431.\\u003c/li\\u003e\\n\\u003cli\\u003eSeifollahi-Aghmiuni, S., Kalantari, Z., Egidi, G., Gaburova, L., \\u0026amp; Salvati, L. (2022). Urbanisation-driven land degradation and socioeconomic challenges in peri-urban areas: Insights from Southern Europe. Ambio, 51(6), 1446\\u0026ndash;1458.\\u003c/li\\u003e\\n\\u003cli\\u003eShen, J., Ren, X., Wu, H., \\u0026amp; Feng, Z. (2024). The Relationship between the Construction of Transportation Infrastructure and the Development of New Urbanization. ISPRS International Journal of Geo-Information, 13(6), 194.\\u003c/li\\u003e\\n\\u003cli\\u003eSinger, M. E., Cohen-Zada, A. L., \\u0026amp; Martens, K. (2022). Core versus periphery: Examining the spatial patterns of insufficient accessibility in U.S. metropolitan areas. Journal of Transport Geography, 100, 103321. https://doi.org/10.1016/j.jtrangeo.2022.103321\\u003c/li\\u003e\\n\\u003cli\\u003eSkeldon, R. (2012). Migration transitions revisited: Their continued relevance for the development of migration theory. Population, Space and Place, 18(2), 154\\u0026ndash;166.\\u003c/li\\u003e\\n\\u003cli\\u003eSong, G., Cai, J., \\u0026amp; Fu, Y. (2023). Regional development study how to develop a small city affected by siphoning: A case of a Chinese city. Prosperitas, 10(3), 1\\u0026ndash;13.\\u003c/li\\u003e\\n\\u003cli\\u003eStaricco, L., \\u0026amp; Vitale Brovarone, E. (2018). Promoting TOD through regional planning. A comparative analysis of two European approaches. Journal of Transport Geography, 66, 45\\u0026ndash;52. https://doi.org/10.1016/j.jtrangeo.2017.11.011\\u003c/li\\u003e\\n\\u003cli\\u003eSun, W., Wang, C., Liu, C., \\u0026amp; Wang, L. (2021). High-Speed Rail Network Expansion and Its Impact on Regional Economic Sustainability in the Yangtze River Delta, China, 2009\\u0026ndash;2018. Sustainability, 14(1), 155. https://doi.org/10.3390/su14010155\\u003c/li\\u003e\\n\\u003cli\\u003eSung, H., \\u0026amp; Eom, S. (2024). Evaluating transit-oriented new town development: Insights from Seoul and Tokyo. Habitat International, 144, 102996.\\u003c/li\\u003e\\n\\u003cli\\u003eTang, C., \\u0026amp; Dou, J. (2022). Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network. Frontiers in Physics, 10. https://doi.org/10.3389/fphy.2022.855380\\u003c/li\\u003e\\n\\u003cli\\u003eTang, J., Gao, F., Han, C., Cen, X., \\u0026amp; Li, Z. (2021). Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi. Journal of Transport Geography, 95, 103134. https://doi.org/10.1016/j.jtrangeo.2021.103134\\u003c/li\\u003e\\n\\u003cli\\u003eTao, Z. (2019). Research on the degree of coupling between the urban public infrastructure system and the urban economic, social, and environmental system: A case study in Beijing, China. Mathematical Problems in Engineering, 2019(1), 8206902.\\u003c/li\\u003e\\n\\u003cli\\u003eTong, D., Liu, T., Li, G., \\u0026amp; Yu, L. (2014). Empirical analysis of city contact in Zhujiang (Pearl) River Delta, China. Chinese Geographical Science, 24(3), 384\\u0026ndash;392. https://doi.org/10.1007/s11769-014-0667-4\\u003c/li\\u003e\\n\\u003cli\\u003eVergel-Tovar, C. E., \\u0026amp; Rodriguez, D. A. (2018). The ridership performance of the built environment for BRT systems: Evidence from Latin America. Journal of Transport Geography, 73, 172\\u0026ndash;184. https://doi.org/10.1016/j.jtrangeo.2018.06.018\\u003c/li\\u003e\\n\\u003cli\\u003eVieira, J., Po\\u0026ccedil;as Martins, J., Marques De Almeida, N., Patr\\u0026iacute;cio, H., \\u0026amp; Gomes Morgado, J. (2022). Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins. Sustainability, 14(12), 7060. https://doi.org/10.3390/su14127060\\u003c/li\\u003e\\n\\u003cli\\u003eWan, J., Wang, Z., Ma, C., Su, Y., Zhou, T., Wang, T., Zhao, Y., Sun, H., Li, Z., \\u0026amp; Wang, Y. (2023). Spatial-temporal differentiation pattern and influencing factors of high-quality development in counties: A case of Sichuan, China. Ecological Indicators, 148, 110132.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, C., Wang, L., Xue, Y., \\u0026amp; Li, R. (2022). Revealing spatial spillover effect in high-tech industry agglomeration from a high-skilled labor flow network perspective. Journal of Systems Science and Complexity, 35(3), 839\\u0026ndash;859.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, H., Li ,Jinyang, Wang ,Pengling, Teng ,Jing, \\u0026amp; and Loo, B. P. Y. (2023). Adaptability analysis methods of demand responsive transit: A review and future directions. Transport Reviews, 43(4), 676\\u0026ndash;697. https://doi.org/10.1080/01441647.2023.2165574\\u003c/li\\u003e\\n\\u003cli\\u003eWang, H., Zhang, X., Zhang, X., Liu, R., \\u0026amp; Ning, X. (2024). Understanding coordinated development through spatial structure and network robustness: A case study of the Beijing-Tianjin-Hebei region. Journal of Geographical Sciences, 34(5), 1007\\u0026ndash;1036.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, Q., Qian, Y., Zeng, J., Yin, F., \\u0026amp; Zhu, L. (2021). Land Transportation Accessibility and Urbanization Spatial Pattern Based on Coupling Coordination\\u0026mdash;Taking Chengdu-Chongqing Urban Agglomeration as an Example. IOP Conference Series: Earth and Environmental Science, 696(1), 012035. https://doi.org/10.1088/1755-1315/696/1/012035\\u003c/li\\u003e\\n\\u003cli\\u003eWang, R., Zhang, X., \\u0026amp; Li, N. (2022). Zooming into mobility to understand cities: A review of mobility-driven urban studies. Cities, 130, 103939.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, S., Liu, X., Zhou, C., Hu, J., \\u0026amp; Ou, J. (2017). Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China\\u0026rsquo;s megacities. Applied Energy, 185, 189\\u0026ndash;200.\\u003c/li\\u003e\\n\\u003cli\\u003eWang, W., \\u0026amp; Xie, M. (2024). Can transportation infrastructure improve resource misallocation? Evidence from China. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32724\\u003c/li\\u003e\\n\\u003cli\\u003eWang, Y. (2022). The impacts of improvements in the unified economic and environmental efficiency of transportation infrastructure on industrial structure transformation and upgrade from the perspective of resource factors. PLOS ONE, 17(12), e0278722. https://doi.org/10.1371/journal.pone.0278722\\u003c/li\\u003e\\n\\u003cli\\u003eWang, Y., Cao, G., Yan, Y., \\u0026amp; Wang, J. (2022). Does high-speed rail stimulate cross-city technological innovation collaboration? Evidence from China. Transport Policy, 116, 119\\u0026ndash;131. https://doi.org/10.1016/j.tranpol.2021.11.024\\u003c/li\\u003e\\n\\u003cli\\u003eWang, Y., Zou, H., Duan, X., \\u0026amp; Wang, L. (2022). Coordinated Evolution and Influencing Factors of Population and Economy in the Yangtze River Economic Belt. International Journal of Environmental Research and Public Health, 19(21), 14395. https://doi.org/10.3390/ijerph192114395\\u003c/li\\u003e\\n\\u003cli\\u003eWei, W., Yuan-rui, M., Lei, G., \\u0026amp; Liang, G. (2025). Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin. Ecological Indicators, 175, 113521.\\u003c/li\\u003e\\n\\u003cli\\u003eWeiner, E. (2008). Urban Transportation Planning in the United States: History, Policy, and Practice. Springer. https://doi.org/10.1007/978-0-387-77152-6\\u003c/li\\u003e\\n\\u003cli\\u003eWu, B., Jin, X., Li, D., \\u0026amp; Wang, B. (2023). Spatial\\u0026ndash;temporal evolution of coupling coordination development between regional highway transportation and new urbanization: A case study of Heilongjiang, China. Sustainability, 15(23), 16365.\\u003c/li\\u003e\\n\\u003cli\\u003eWu, C., Huang, X., \\u0026amp; Chen, B. (2020). Telecoupling mechanism of urban land expansion based on transportation accessibility: A case study of transitional Yangtze River economic Belt, China. Land Use Policy, 96, 104687.\\u003c/li\\u003e\\n\\u003cli\\u003eWu, H., Jiang, Z., Zhu, L., Lin, A., Zhou, H., \\u0026amp; Cen, L. (2024). Analyzing multiscale associations and couplings between integrated development and eco-environmental systems: A case study of the central plains urban agglomeration, China. Applied Geography, 171, 103387. https://doi.org/10.1016/j.apgeog.2024.103387\\u003c/li\\u003e\\n\\u003cli\\u003eWu, S., Ma, L., Wang, L., Chen, X., \\u0026amp; Shi, Z. (2023). Differences of Social Space of Rural Migrant Labor Force: The Influence of Local Quality. Land, 12(3), 644.\\u003c/li\\u003e\\n\\u003cli\\u003eYang, R., An, X., Chen, Y., \\u0026amp; Yang, X. (2023). The knowledge analysis of panel vector autoregression: A systematic review. Sage Open, 13(4), 21582440231215991.\\u003c/li\\u003e\\n\\u003cli\\u003eYang, Y., Lu, X., Chen, J., \\u0026amp; Li, N. (2022). Factor mobility, transportation network and green economic growth of the urban agglomeration. Scientific Reports, 12(1), 20094. https://doi.org/10.1038/s41598-022-24624-5\\u003c/li\\u003e\\n\\u003cli\\u003eYao, L., \\u0026amp; Sun, L. (2013). Practical Methods in Traffic Demand Forecasting Model. In W. Wang \\u0026amp; G. Wets (Eds.), Computational Intelligence for Traffic and Mobility (pp. 297\\u0026ndash;319). Atlantis Press. https://doi.org/10.2991/978-94-91216-80-0_15\\u003c/li\\u003e\\n\\u003cli\\u003eYoo, S., Kumagai, J., \\u0026amp; Managi, S. (2024). Urban-rural gap induced by high-speed rail: 35 years of evidence from Japan. Research in Transportation Business \\u0026amp; Management, 55, 101131. https://doi.org/10.1016/j.rtbm.2024.101131\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, H., Zhou, B.-B., Liu, S., Hu, G., Meng, X., Liu, X., Shi, H., Gao, Y., Hou, H., \\u0026amp; Li, X. (2023). Enhancing intercity transportation will improve the equitable distribution of high-quality health care in China. Applied Geography, 152, 102892. https://doi.org/10.1016/j.apgeog.2023.102892\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, T., Qiu, Y., Ding, R., Yin, J., Cao, Y., \\u0026amp; Du, Y. (2023). Coupling coordination and influencing factors of urban spatial accessibility and economic spatial pattern in the New Western Land-Sea Corridor. Environmental Science and Pollution Research, 30(19), 54511\\u0026ndash;54535. https://doi.org/10.1007/s11356-023-26121-2\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, X., \\u0026amp; Zhang, Z. (2022). Interaction Effects of R\\u0026amp;D Investment, Industrial Structure Rationalization, and Economic Growth in China Based on the PVAR Model. Sustainability, 15(1), 545. https://doi.org/10.3390/su15010545\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Y., Zhu, T., Guo, H., \\u0026amp; Yang, X. (2023). Analysis of the coupling coordination degree of the Society-Economy-Resource-Environment system in urban areas: Case study of the Jingjinji urban agglomeration, China. Ecological Indicators, 146, 109851.\\u003c/li\\u003e\\n\\u003cli\\u003eZhao, L., \\u0026amp; Jia, Y. (2021). Interactive Correlation between Economy and Integrated Transportation Development of Metropolitan Areas in China: Quantitative Study. Journal of Urban Planning and Development, 147(4), 05021041. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000759\\u003c/li\\u003e\\n\\u003cli\\u003eZhao, X. (2024). The influence of knowledge-intensive service industry agglomeration in the Yangtze River Delta urban agglomeration on regional economy. Heliyon, 10(3).\\u003c/li\\u003e\\n\\u003cli\\u003eZhao, Y., Zhang, H., An, L., \\u0026amp; Liu, Q. (2018). Improving the approaches of traffic demand forecasting in the big data era. Cities, 82, 19\\u0026ndash;26. https://doi.org/10.1016/j.cities.2018.04.015\\u003c/li\\u003e\\n\\u003cli\\u003eZhong, Y., Wang, H., Jing, T., Yang, Y., Zou, H., \\u0026amp; Jin, Y. (n.d.). Unveiling the Spatiotemporal Evolution Characteristics of Urban Residents\\u0026rsquo; Travel Patterns and Spillover Effects of Jobs-Housing Spaces in China Based on Multi-Source Data. Available at SSRN 5179218.\\u003c/li\\u003e\\n\\u003cli\\u003eZhou, J., \\u0026amp; Yang, Y. (2021). Transit-based accessibility and urban development: An exploratory study of Shenzhen based on big and/or open data. Cities, 110, 102990. https://doi.org/10.1016/j.cities.2020.102990\\u003c/li\\u003e\\n\\u003cli\\u003eZhu, P. (2021). Does high-speed rail stimulate urban land growth? Experience from China. Transportation Research Part D: Transport and Environment, 98, 102974.\\u003c/li\\u003e\\n\\u003cli\\u003eZhuang, S., Xia, N., Gao, X., Zhao, X., Liang, J., Wang, Z., \\u0026amp; Li, M. (2024). Coupling coordination analysis between railway transport accessibility and tourism economic connection during 2010\\u0026ndash;2019: A case study of the Yangtze River Delta. Research in Transportation Business \\u0026amp; Management, 55, 101134.\\u003c/li\\u003e\\n\\u003cli\\u003eZou, J., \\u0026amp; Deng, X. (2022). Spatial differentiation and driving forces of migrants\\u0026rsquo; socio-economic integration in urban China: Evidence from CMDS. Social Indicators Research, 159(3), 1035\\u0026ndash;1056.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Coupling coordination, Transport infrastructure, Population dynamics, Pearl River Delta\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7168229/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7168229/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe demographic\\u0026ndash;transport nexus is central to regional integration, but remains insufficiently studied in rapidly urbanizing contexts. Taking China\\u0026rsquo;s Pearl River Delta (PRD) as a representative megaregion, this study uses panel data from nine PRD cities spanning 1990 to 2020. We construct an entropy-weighted indicator system and apply a coupling\\u0026ndash;coordination model in combination with spatial Durbin regressions to trace the co-evolution of population and transport systems and identify their driving forces. Findings reveal that: (1) the regional coupling-coordination index rose from 0.21 to 0.54 but still shows a clear core\\u0026ndash;periphery gradient\\u0026mdash;Guangzhou and Shenzhen already display high coordination, whereas ZhaoQing and Jiangmen lag behind; (2) economic growth, a consumption-oriented economic structure and technological progress significantly enhance coordination; (3) the 2009 PRD Master Plan mainly benefits core cities, with limited policy spill-overs; (4) medical-service provision improves coordination, while basic-education supply is not significant, highlighting service-level disparities. We recommend strengthening peripheral inter-city corridors, building 30- to 60-minute commuting rings, and linking transport investment to real-time coupling metrics and coordinated industry relocation to advance the region toward higher-level integration.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Spatiotemporal Patterns and Drivers of Population–Transport Coordination in the Pearl River Delta\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-06 04:29:04\",\"doi\":\"10.21203/rs.3.rs-7168229/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-09-26T09:45:29+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-24T02:30:41+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-23T18:05:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"18627053986906084010402033336050501797\",\"date\":\"2025-09-23T03:55:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"241090819257901955238296935962270747716\",\"date\":\"2025-09-22T19:04:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-09-22T16:20:02+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-09-10T07:45:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-07-29T03:04:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-07-25T05:38:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-07-25T05:34:19+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"5f65455a-2ea4-4e48-9dbc-186cafa97816\",\"owner\":[],\"postedDate\":\"October 6th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":55384724,\"name\":\"Humanities/Complex networks\"},{\"id\":55384725,\"name\":\"Social science/Complex networks\"},{\"id\":55384726,\"name\":\"Social science/Development studies\"},{\"id\":55384727,\"name\":\"Earth and environmental sciences/Environmental social sciences\"},{\"id\":55384728,\"name\":\"Scientific community and society/Geography\"},{\"id\":55384729,\"name\":\"Social science/Geography\"}],\"tags\":[],\"updatedAt\":\"2025-12-08T16:01:01+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7168229\",\"link\":\"https://doi.org/10.1038/s41598-025-26997-9\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2025-12-02 15:57:32\",\"publishedOnDateReadable\":\"December 2nd, 2025\"},\"versionCreatedAt\":\"2025-10-06 04:29:04\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-025-26997-9\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-025-26997-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7168229\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7168229\",\"identity\":\"rs-7168229\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}