Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic

Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic | 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 Research Article Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic jianmin jia, Chunsheng Liu, Hui Zhang, Yan Xiao, Xiaohan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2328657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The outbreak of COVID-19 brings huge challenges to the bike-sharing system and even society structure. Thus, it is urgent to fully understand the impacts of pandemic on bike-sharing behavior. This paper proposed a comprehensive approach to investigate the mobility patterns influenced by the outbreak of COVID-19 pandemic with the case in Washington D.C. Multiple-source data, including bike-sharing trip information, COVID-19 information, geographic and POI information, were collected. Although the total bike-sharing trips decreased up to 80% in spatial-temporal analysis, the trips made by casual user still increased. In addition, the docking stations and trips from 2019 to 2021 were utilized to construct the bike-sharing network. The results present that major network properties, such as connectivity, clustering coefficient, and accessibility, experienced significant decrease during the pandemic. Through the detection of community with modularity method, the evolution of community structure before and after pandemic was captured. The increased long-range and long-time bike-sharing trips results in the combination between central communities and outer communities. To better understand the community structure, the POI (Point of Interests) auxiliary analysis was conducted and central community was found to have similar proportion of POIs even during the pandemic. Implications for bike-sharing management and operation policy was also addressed. COVID-19 Bike-sharing Spatial-temporal Analysis Complex Network Community Detection POI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Through the widespread promotion around the world, bike-sharing system are growing to be an essential part of the sustainable transportation system (Buck et al., 2013 ; Fishman et al., 2016; Zhang et al., 2021 ). Bike-sharing not only provides door-to-door service, but also strengthens the connection between various traffic modes. However, the system is also easily affected by external factors, such as temperature, severe weather and special events (Saberi et al., 2018 ). In Washington D.C., the first case of COVID-19 was announced on March 7, 2020 and the public health emergency was declared on March 11, 2020 (District of Columbia (D.C.) Policy Center., 2020). Subsequently, a series of policies were implemented to prevent the spread of coronavirus (District of Columbia (D.C.) Government, 2020 ). For instance, the residents were required to stay at home. The employees were able to work from home, while the schools and universities also provided the remote learning. Undoubtedly, the outbreak of COVID-19 was found to significantly affected the economy development, social structure and urban transportation system (Chen et al., 2022 ). On the other hand, as an alternative sustainable transportation mode, bike-sharing also has the potential to alleviate the air transmission of virus when compared to the crowed public transit (Kalambay et al., 2022). Consequently, more and more commuters are likely to utilize shared-bicycles other than public transportation modes during the pandemic (Bergantino et al., 2021 ). Bike-sharing users’ perception and mobility patterns were found to be significantly influenced. Kanik ( 2020 ) provided several interesting insights into the decision-making of bike-sharing users in various cities in U.S. The total bike-sharing ridership was found to be decreased, while the most destinations were the pharmacies and grocery stores. Nevertheless, few existing researches investigated the COVID impacts on bike-sharing mobility patterns and community structure. Given this background, this paper proposed to provide better understanding, measurement and characterize of the correlation through examining the bike-sharing usage and mobility patterns during the pandemic. The spatial-temporal statistical approach and complex network theory were employed to infer the change of community structure with the consideration of POI (Point of Interests). The comprehensive results obtained in this paper can also provide support for the policymakers in traffic planning and management during the impact of pandemic emergency. The rest of the paper is depicted as follows. In Section 2, the systematic literature review of bike-sharing and traffic impacts from COVID-19 is introduced. Section 3 introduces the data sources and methodology. Section 4 presents the spatial-temporal analysis for bike-sharing usage and the mobility patterns obtained from the network perspective. Finally, in Section 5 the conclusions and policy implications for bike-sharing system are provided. 2. Literature Review With the development history over half century (Shaheen et al., 2010 ), bike-sharing system can be classified into two categories: the docked system with fixed stations and the dock-less system with designated areas (Xu et al., 2018). Both systems have been implemented successfully around the world. For instance, the Capital bike-sharing program in Washington D.C. of U.S. with docked system and the Mo-bike dock-less program in China (Lin et al., 2018 ). Compared to private car, the bike-sharing system makes huge contribution towards sustainability through promoting environmental benefits (Fishman et al., 2014 ), social equity (Lucas et al., 2016 ), public health (Mueller et al., 2015 ) and economic gains (Bullock et al., 2017 ). Due to the flexibility and vulnerability of ridership, it is still a challenging issue to investigate the factors affecting the bike-sharing system. An extensive of literature have explored the travel pattern analysis of bike-sharing with the consideration of land use, demographic, social-economic features, other transportation mode and weather conditions (Akar et al., 2013 ; Noland et al., 2016 ; Wergin et al., 2017; Jia et al., 2021 ). Given this context, Buck et al. ( 2013 ) conducted the trip purpose survey of bike-sharing, which indicated work was the main purpose for long-term members. Gebhart et al. (2014) found the bike-sharing ridership and trip duration were highly affected by the hourly weather condition, while Fishman et al. ( 2015 ) put emphasis on individual attributes from bike-sharing users. Additionally, the temporal analysis for bike-sharing was conducted to examine the influence from public transit, land use and temporal factors (Wang et al., 2020). Yang et al. (2020) proposed to explore the spatial patterns of ridership through incorporating the demographics into the regression model. In order to investigate the impacts of metro strike, Saberi et al. ( 2018 ) conducted the spatial-temporal analysis before, during and after the disruption. Simultaneously, various models and analytical methods were employed in the bike-sharing research, such as the discrete choice models, geographical weighted regression (GWR) model, clustering models and spatial network method (Benita, 2021 ; Wang et al., 2022 ). For instance, Liu et al. (2019) proposed a multinomial logit (ML) model to examine the interaction between land use and bike-sharing demand. Through the random forest method, Wang et al. ( 2022 ) found the nonlinear effects from built environment on bike-sharing usage. However, the spatial heterogeneities may result in various impact. Furtherly, Qian et al. (2015) and Ji et al. ( 2018 ) proposed the GWR and geographically weighted Poisson regression (GWPR) model, respectively, to address the spatial heterogeneities. On the other hand, the network method is also utilized by several researchers. In the research from Yang et al. ( 2019 ), the network of dock-less bikes-haring system (DBS) was built to measure the travel behavior changes before and after a new metro line open. To explore the distribution of travel demand and dynamic properties, Tian et al. ( 2019 ) created the bike-sharing networks for both docked-based bike-sharing and dock-less bike-sharing systems. Existing researches provide comprehensive understanding of bike-sharing system. However, the outbreak of COVID-19 pandemic brings worldwide challenge to human mobility behavior, which hasn’t been met in previous research (Benita, 2021 ). A huge effort has been made by researchers to investigate the impacts of pandemic on transportation system. Zhang et al. ( 2020 ) and Li et al. ( 2021 ) proposed to explore the dynamic impacts of COVID-19 in terms of the intercity travel. The results showed a non-linear relationship between intercity travel and pandemic mainly due to the change of control policy. The travel pattern analysis on highway network was conducted based on the trajectory information (Li et al., 2022 ), and the traffic flow pattern was defined as before pandemic, during pandemic and recovery stage. On the other hand, a few of researches focused on the urban transportation system. Parker et al. ( 2021 ) examined the public transit usage during pandemic. The regression models were built in terms of the social-demographic factors, such as income, gender, occupation and age. Similarly, the spatial lag model (SLM) was employed to investigate the relationship between taxi travel and land use (Nian et al., 2020 ). The results indicated the recovery of the social activities. Moreover, the performance of bike-sharing system also attracted researcher’s concerns. Kalambay et al. (2022) stated the seasonal impacts on ridership and built the mixed-effects negative binomial (MENB) model to assess before-after impact of COVID-19 outbreak. In order to identify passenger travel patterns and habits, Chen et al. ( 2022 ) examined the spatial-temporal distribution of ridership under pandemic with the complex network method. Although a considerable literature has investigated the bike-sharing program, rare researches put emphasis on the pandemic impacts from system perspective. It is still meaningful to explore the mobility patterns and emergency reaction of bike-sharing users. This paper aims at proposing a systematic approach to measure the impacts in terms of community structure associated with POI (Point of interest), which supports the comprehensive understanding of human civilization and pandemic’s recovery. 3. Data Source Description As the selected case area, multiple data sources were collected in Wahington D.C. from January 2019 to December 2021. The bike-sharing program was first launched in 2010 and was widely used around the locals and non-locals in D.C. (Capital bikeshare, 2022 ). With the development over a decade, there are over 5000 bicycles and 600 docking stations providing service, as shown in Fig. 1 (a). The docking stations with in D.C. area was contained in the study. Simultaneously, the historical information for bike-sharing trip was collected, containing the trip time, trip duration, staring and end station with geographic coordinate and membership category. Specifically, the trip information is anonymous. In addition, Washington D.C. government provides the daily report for COVID-19 positives and lives lost from the pandemic outbreak to now, which depicts the changing trends of patient number. Therefore, there are several stages for the research period. For instance, 2019 is the before stage unaffected by pandemic, while 2020 is the outbreak stage influenced by both pandemic transmission and strict prevention policy, such as stay at home and remote working. With better perception of pandemic and policy adjustment, there would be resilient stage in 2021 for society and economy. On the other hand, more datasets were considered in this study. In order to examine the spatial mobility patterns, the geographic information of Washington D.C. area and transportation network were extracted (Open Data DC, 2022 ). Moreover, the POI (point of interests) information, containing working, recreation, lodging and transportation types, was obtained from the open platform Data.world (data.world, 2022 ), and there are over 5,000 POIs involved in the dataset, which is presented in Fig. 1 (b). In this paper, the bike-sharing mobility pattern was investigated and compared before, during and after the COVID-19 outbreak, in terms of spatial and temporal perspectives. Basic statistics, such as patient number, bike-sharing frequency, trip duration and spatial distribution, were analyzed. Moreover, the complex network theory was utilized to conduct the community detection, and the variation of community structure was examined associated with the POI auxiliary analysis. 4. Methodology 4.1 Network Properties Analysis Complex network concept was widely used in the study of network analysis, such as the social relationship network, business relationship and even the multimodal transportation system (Saberi et al., 2018 ; Chen et al., 2022 ). In this paper, the complex network theory was utilized to investigate the bike-sharing mobility patterns in Washington D.C. before, during and after the outbreak of COVID-19 pandemic. In order to construct the network, the docking stations for bicycles are viewed as the nodes, while the pair of OD (origin and destination) between docking station is considered as the link. Therefore, the ODs, i.e. the link, in the network is directed. To model the bike-sharing network in this study, a unique number, \(i\) or \(j\) , is given to each docking station. Specifically, the adjacent matrix \(A\) was used to present the connectivity between nodes. If there is at least one bicycle trip between \(i\) and \(j\) , the link exists and \({a}_{ij}\) is set to 1. Otherwise, the link doesn’t exist and \({a}_{ij}\) is set to 0. Moreover, the number of trips from node \(i\) to node \(j\) is revealed by the link weight \({\omega }_{ij}\) . For comprehensive analysis, other network properties are also examined and expressed as follows. (1) Node degree In order to measure the node centrality, node degree is considered as the most straightforward and convenient metric. Specifically, the most active node is also the most important one. Thus, the node degree indicates the number of links connected to the selected node, which is identified by the adjacent matrix. Additionally, as the bike-sharing network in this study is built as the directed graph, both in-degree and out-degree for the selected node should be considered. Thus, node degree can be expressed as, $${D}_{i}=\sum _{j\in V}{(a}_{ij}+{a}_{ji})$$ 1 Where, \(V\) represents the neighbor set of nodes for node i , and \({a}_{ij}\) and \({a}_{ji}\) represents the entry value in the adjacent matrix. (2) Network connectivity Generally, network connectivity demonstrates the connectivity properties from the systematic perspective with the consideration of links and nodes (Saberi et al., 2018 ). In this paper, it is defined as, $$NC=\frac{2\times L}{{n}^{2}}$$ 2 Where, \(n\) represents the number of nodes, while \(L\) represents the number of links. (3) Node flux As aforementioned, the link weight represents the number of trips between the nodes. Different to the link weight, the node flux is defined as the total number of trip either starts or ends in the bike-sharing station. Thus, the node flux can be expressed as the Eq. 3 . $${NF}_{i}=\sum _{j}{\omega }_{ij}$$ 3 Where, \(i\) represents the selected node \(i\) , \(j\) represents the nodes connected to node \(i\) . \({\omega }_{ij}\) represents the weight between node \(i\) and \(j\) . (4) Clustering coefficient In existing researches, the degree of clustering of a network is usually measured by clustering coefficient (Fishman et al., 2015 ; Chen et al., 2022 ), which indicates the probability that two neighbors of a given node are prone to connect themselves. Addressed as the extent for the node interaction within the network cluster, the clustering coefficient can be computed through the Eq. 4 for each node \(i\) . $${CC}_{i}=\frac{2}{{D}_{i}\left({D}_{i}-1\right)-2{D}_{i}\left(R\right)}{T}_{i}$$ 4 Where, \({CC}_{i}\) is the clustering coefficient for node i . \({D}_{i}\) represents the node degree, while \({D}_{i}\left(R\right)\) represents the reciprocal degree of node i . \({T}_{i}\) represents the number of directed triangles through node i . (5) Network efficiency In order to measure the information transfer between nodes, the network efficiency can be expressed as, $$E=\frac{1}{N(N-1)}\sum _{i\ne j}\frac{1}{{d}_{ij}}$$ 5 Where, \({d}_{ij}\) represents the length of shortest path between node \(i\) and \(j\) . If there is no bicycle trip between nodes, the \({d}_{ij}\) is set to \(+\infty\) . As the value of \(E\) is ranged from 0 to 1, a larger value indicates the better performance of network efficiency. (6) Gini coefficient First introduced in the measurement of income distribution, the Gini coefficient is extended to many other issues, such as the inequality of in transportation systems (Feng et al., 2014; Su et al., 2018 ; Chen et al., 2019 ). In this paper, a general definition of Gini coefficient is employed as, $$G=1+\frac{1}{n}-\frac{1}{{n}^{2}\stackrel{-}{x}}\sum _{i=1}^{n}(n-i+1){x}_{i}$$ 6 Where, \(G\) is the Gini coefficient and it ranges from 0 to 1. The higher Gini coefficient indicates the inequality. \({x}_{i}\) represents the service rate of bike-sharing station \(i\) . (7) Accessibility The accessibility and convenience of the station also attracts scholars’ attention (Shen et al., 2018 ). To measure the node accessibility within the network, it can be defined as, $${AC}_{i}=\sum _{j=1}^{n}{T}_{j}{\delta }_{ij}/{t}_{ij}$$ 7 Where, \({A}_{i}\) represents the node accessibility. \({T}_{j}\) represents the travel demand in node \(j\) . \({t}_{ij}\) defines the travel time between node \(i\) and \(j\) . \({\delta }_{ij}\) is the adjustment coefficient and equal to 0 if the distance between node \(i\) and \(j\) is more than 5 km. Otherwise, \({\delta }_{ij}\) is 1. (8) Spatial autocorrelation The Moran’s Index is commonly utilized to examine the spatial autocorrelation with regional structure (Ni and Chen, 2020 ). The typical format of Moran’s I can be expressed as, $$I=\frac{n}{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{i,j}}\times \frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{W}_{i,j}({y}_{i}-\overline{y})({y}_{j}-\overline{y})}{{\sum }_{i=1}^{n}({y}_{i}-\overline{y}{)}^{2}}$$ 8 Where, n represents the number of spatial units, i.e. the nodes. \({W}_{i,j}\) is the spatial weight between node \(i\) and \(j\) . \({y}_{i}\) represents the observation value of node \(i\) , while \(\overline{y}\) is the mean value. 4.2 Detection of Community Structure Detection of community structure is one of the most relevant approaches to demonstrate the real system, which can be represented as network or graph (Fortunato, 2010 ). Therefore, the technique is widely used in the research involved sociology, biology and computer science. In this paper, detecting communities in the bike-sharing network means to find the way to partition the bike-sharing stations into disjoint clusters so that the stations in the same clusters are more densely connected to each other in terms of bike-sharing usage behavior. Basically, a community within the network must represent the similarity and common bike-sharing mobility patterns. Modularity-based method is considered as the classical approach to conduct the community detection (Duan et al., 2014 ). Newman and Girvan ( 2004 ) proposed the most popular quality function of modularity, which proposes to find the possible existence of clusters through the comparison between real edges density and the expected density in a subgraph. The number of expected edges falling between pair of nodes can be calculated based on the number of links and node degree. Consequently, the sum of the difference between the real number of edges and expected number of edges over all the pairs of nodes in the same community can be obtained. The basic format of modularity can be expressed as, $$Q=\frac{1}{2l}\sum _{i,j}({A}_{ij}-\frac{{D}_{i}{D}_{j}}{2l})\delta ({C}_{i},{C}_{j})$$ 9 Where, \({A}_{ij}\) represents the value in adjacent matrix. \(l\) is the number of links in the network. \({D}_{i}\) represents the degree of node \(i\) . The \(\delta -\) function generates 1 if node i and j are in the same community, otherwise zero. In addition, the information of POI (point of interest) was also involved in the auxiliary analysis to further understand the community structure. Several types of POI, including Eating&Drinking, Government and Public Services, Leisure, Lodging, Tourism, public transport facilities, were selected as introduced in the data source section. In this paper, a buffer area surrounding the bike-sharing station was created to conduct the auxiliary analysis, while the radius is 1 km. To differentiate the specific function, frequency density (FD) and category ratio (CR) were introduced to infer the specific function of each community (Liu et al., 2020). Consequently, FD and CR for each bike-sharing station are determined by the frequency and category of POI within the community area. Specifically, the normalization of the FD was commonly utilized to consider the variance in number of POI. Therefore, the category ratio (CR) can be obtained through the normalized FD. Eq. 10 to 12 address the detailed definition. \({FD}_{ij}=\frac{{P}_{ij}}{{S}_{j}}(i=\text{1,2},\dots ,7;j=\text{1,2},\dots ,m\) (10) \({FD}_{normalize}(i,j)=\frac{{FD}_{ij}-{FD}_{min}}{{FD}_{max}-{FD}_{min}}\) (11) \({CR}_{ij}=\frac{{FD}_{normalize}(i,j)}{{\sum }_{i=1}^{9}{FD}_{normalize}(i,j)}\) (12) Where, \(m\) is the community number. \({P}_{ij}\) represent the number of POI type i within the community j . \({S}_{j}\) represents the total area of community j . 5. Results And Discussion 5.1 Spatial-temporal mobility patterns The data cleansing process was conducted, as the abnormal information in the original bike-sharing records. For instance, the maximum trip duration is over 1,000 minutes, while the minimum trip duration is below 1 minutes. Such extremely long or short trips are considered as invalid trips, which have to be removed from the dataset to avoid the bias in the analysis. Subsequently, facing the variation of COVID-19 cases from 2019 to 2021, the temporal mobility patterns of bike-sharing were investigated for member and casual user respectively, as Fig. 1 presents. The bike-sharing usage for members has a significant decrease up to 58.61% in the comparison between 2019 and 2020, which is mainly influenced by the outbreak of pandemic in 2020. However, the trips completed by casual users experienced a 40.22% increase at the same time, mainly due to the encouraging policy for bike-sharing and shift from public transport. Interestingly, with the deep perception of COVID-19 pandemic and policy adjustment, the bike-sharing mobility patterns are resilient when compared the system performance 2020 and 2021, which is consistent with the recovery of economy and society. Moreover, multi-resolution analysis, involving monthly and weekly bike-sharing trips, for the temporal mobility patterns were performed before and after the pandemic outbreak as Fig. 3 . It can be found that the member trips play a dominant role in bike-sharing system before the pandemic, while the trips from casual users increased to nearly half of the total trips after the pandemic. Specifically, there is significant seasonal variation around the year mainly due to the weather influence. As for the mobility behavior in a week as Fig. 3 (b), there are more trips in weekday for members and in weekend for casual users, which may be caused by the policy adjustment and tourists. In addition, cumulative density of travel distance and duration were also examined as Fig. 4 , which indicates the significant difference between members and casual users. Both average trip distance and duration for members are lower than that for casual users. It is mainly caused by the different trip purpose: the members have relative fixed trip time for commuting or mode transfer, while casual users conduct flexible and random trips. Notably, the COVID-19 brought unobvious changes for members before and after the pandemic. In contrast, the casual users performed trips with longer distance and lower duration mainly due to the increased flexible users. On the other hand, the spatial patterns for bike-sharing trips were shown in Fig. 5 . There is a homogeneous distribution of bike-sharing trips in central Washington D.C. area before and after the outbreak of COVID-19. However, the pandemic still resulted in huge decrease in total number of trips, as the number of active docking stations, with high travel demand, decrease significantly from 115 in 2019 to 31 in 2020. Specifically, the active docking stations returned to 41in 2021, which indicates the well resilience of bike-sharing system. 5.2 Mobility Patterns Analysis from Network Perspective Considering the seasonal effects on the bike-sharing system, four seasons, including Winter (from December of the previous year to February of the current year), Spring (from March to May), Summer (from June to August) and Fall (from September to November), were extracted to investigate the bike-sharing mobility patterns before and after the COVID-19 pandemic from network perspective. The network properties for bike-sharing system from 2019 to 2021 was provided by Table 1 . The results indicate that the number of nodes were relatively stable while the number of links was fluctuated, which was influenced by the decreased trips between partial docking stations. Notably, the network connectivity in the Spring experienced approximately 20% decrease between 2019 and 2020. The disruption of pandemic resulted in the massive cancellation for membership trips, which decreased the network connectivity. While in 2021, the connectivity was improved due to the returning member and increased casual users. Table 1 Comparison of network properties in different periods. 2019 2020 2021 Winter Spring Summer Autumn Winter Spring Summer Autumn Winter Spring Summer Autumn Node 298 292 298 298 292 298 294 294 297 293 291 289 Link 22312 26,197 27,122 26,425 18,384 23,142 26,309 25,089 22,023 23,911 24,984 25,056 Connectivity 0.85 1.08 1.07 1.04 0.69 0.87 1.05 0.99 0.82 0.94 1.02 1.05 Mean Degree 149.74 179.43 182.03 177.35 125.92 155.32 178.97 170.67 148.3 163.22 171.71 173.4 Max Degree 238 258 258 255 223 250 257 252 243 247 249 250 Mean link weight 12.17 17 18.44 16.76 5.77 6.82 9.9 9.46 8.22 9.12 11.97 12.49 Max link weight 932 1,243 1,279 1,143 167 233 572 470 419 509 725 1,281 Mean Flux 1,527.82 2,663.87 2,932.59 2,581.32 576.52 875.47 1,513.18 1,370.75 998.35 1,252.98 1,768.08 1,880.83 Max Flux 10,642 16,275 16,291 15,675 2,984 3,588 6,365 5,795 4,737 6,456 7,944 7,837 CC 0.75 0.81 0.81 0.80 0.67 0.74 0.79 0.78 0.72 0.76 0.79 0.79 Efficiency 0.74 0.81 0.81 0.80 0.71 0.76 0.80 0.79 0.74 0.78 0.79 0.80 Gini coefficient 0.66 0.66 0.67 0.67 0.61 0.59 0.63 0.63 0.63 0.63 0.65 0.64 Accessibility 853.14 1,099.59 1,242.11 1,231.11 272.93 329.91 482.14 505.32 508.53 472.23 668.94 743.35 Moran’s I 0.55 0.51 0.53 0.55 0.58 0.50 0.52 0.55 0.59 0.55 0.57 0.57 The node degree presents the number of stations connected to the selected stations. Similarly, the mean node degree decreased in 2020 and increased in 2021, which indicates the fluctuation of interaction between bike-sharing stations. However, the stations with the most node degree still provide stable performance. These stations, ensuring the necessary trips to be available even under the pandemic, should be concerned in the emergency policy making. Additionally, link weight describes the trips between two stations, while the node flux presents the total number of trips either begins or ends on the selected station. When compared the statistics in 2019 and 2020 in Table 1 , the pandemic caused the decrease ranging from 40–80%. In contrast, there is a strong recovery in 2021. For instance, the max link weight in Autumn increased from 470 to 1,281. Depicting the node interaction within the network cluster, the average CC (clustering coefficient) demonstrates worse local connection in bike-sharing network after the COVID-19 pandemic. The value of network efficiency and accessibility also prove the well performance of bike-sharing network in normal condition. Interestingly, the Gini coefficient after the pandemic is lower, which means the relatively even distribution of bike-sharing demand. It is mainly caused by the inactivity of docking stations with low demand. On the other hand, the value of Moran’s I are over 0.5 from 2019 to 2021, which suggests the potential community structures in bike-sharing network. Consequently, the local Moran’s I index was utilized to better understand the spatial clustering characteristics of bike-sharing trips, as shown in Fig. 6 . High-High (Low-Low) Cluster defines the stations, with high (low) bike-sharing demand, are also surrounded by neighbor stations with high (low) bike-sharing demand. High-Low (Low-High) outlier defines the stations, with high (low) bike-sharing demand, are surrounded by neighbor stations with low (high) bike-sharing demand. NS means not significant. It is found that the bike-sharing trips mainly concentrates in the central area of Washington D.C., where the land-use is mixed and bicycle facilities are well implemented. In contrast, the outer area is filled by the bike-sharing stations with low demand. This spatial distribution indicates the spatial heterogeneity in bike-sharing demand. 5.3 Community Detection and POI auxiliary analysis Considering the emergence of COVID-19 in March 2020 and the seasonal variations, the bike-sharing in Spring was selected to explore the Community Structure. Figure 7 presents the community structure based on the modularity-based approach. The detected community in 2019 and 2021 demonstrates similar size and distribution and are more locally connected, which is consistent with the network clustering coefficient. However, during the outbreak of COVID-19 pandemic in 2020, there are only three large-scale communities for bike-sharing system. The increased long-range and long-time bike-sharing trips strengthen the connection between central communities and outer communities, which finally results in the integration of small communities and central communities. Therefore, the community strategy is recommended for the scheduling of shared bicycles during the public emergency event. Specifically, the number of bicycles in service should be reassigned according to the community size and location. To better understand the function of various community, the community structure obtained was further examined in terms of POI. As described in methodology section, a buffer area, with 1 km radius, surrounding the bike-sharing station was created to conduct the POI auxiliary analysis. Only POIs within the buffer area is considered. The results are presented in Table 2 . As Table 2 illustrates, community 2 is located in the central urban area with more service facilities, such as tourism and leisure. Notably, this community demonstrates a similar proportion of POI before and after the outbreak of COVID pandemic. Public Transport, Tourism and Leisure account for approximately 60% of all POI types. Thus, community 2 is the active for recreation. Community 1 and community 3 are located in outer suburban area and connected to central area. These communities also have similar POI distribution before and after the outbreak of pandemic. However, besides the Eating and Drinking facilities, community 1 provides over 20% of lodging, while community 3 provides over 20% of Government and Public Services. This indicates the various function between community 1 and community 3. On the other hand, community 4, dominated by Government and Public Services, Public transport and Leisure, is integrated with community 3 during outbreak of pandemic in 2020 Spring. Interestingly, with the recovery of bike-sharing system, community 5 is located in different position in 2019 and 2021. Tourism POI within community 5 in 2019 is over 40%, while Government and Public Services POI within community 5 in 2019 is over 40%, which is mainly caused by the flexible trips of casual users. On the other hand, to examine the relationship between the number of POI within the buffer area and bike-sharing demand for docking stations, the correlations analysis was conducted and shown in Fig. 8 . The results indicate the number of lodging, leisure and public transport facility have strong correlations with bike-sharing demand. The lodging and leisure are area with dense population, which generates huge traffic demand. The number of public transport facility provide the public service, while the bike-sharing system provides the connection between various transportation modes. The low value of tourism demonstrates the weak relationship with bike-sharing demand. Table 2 Comparison of CR (category ratio) 2019 2020 2021 Category Ratio% 1 2 3 4 5 1 2 3 1 2 3 4 5 Eating & Drinking 22.79 13.31 20.99 10.50 7.26 22.94 11.90 14.71 24.86 13.34 18.04 14.42 0 Government and Public Services 16.27 8.18 23.96 28.23 18.73 19.00 8.93 28.52 16.83 8.06 27.41 21.77 42.15 Leisure 14.11 21.34 13.24 20.02 8.68 12.70 20.49 16.21 12.41 19.27 13.55 18.51 22.42 Lodging 22.73 17.84 13.04 8.20 10.44 19.99 17.13 9.33 21.17 19.82 12.34 8.88 5.28 Public Transport 15.26 17.99 16.12 18.46 14.68 14.69 18.52 16.54 14.01 17.76 15.05 19.32 17.34 Tourism 8.84 21.34 12.65 14.6 40.21 10.68 23.04 14.7 10.72 21.75 13.62 17.10 12.81 6. Conclusions Contributing to current research related to bike-sharing behavior, this paper proposed a comprehensive approach to investigate the mobility pattern influenced by the outbreak of COVID-19 pandemic. Through spatial-temporal analysis of mobility patterns, statistical comparison of network properties and community inference with POI, a deep understanding of the bike-sharing behavior and community structure was provided. Multiple-source dataset in Washington D.C., containing bike-sharing trip information, COVID-19 information, geographic layout and POI information, were utilized to in the study. Major findings are summarized as follows. Although serious affected by the pandemic, the bike-sharing trips increased with the recovery of economy and society. Specifically, the casual users increased dramatically. In network perspective, major metrics, such as connectivity, clustering coefficient, and accessibility, experienced significant decrease during the pandemic. The change of network property also indicates the spatial heterogeneity in bike-sharing demand. The detection of community captures the evolution of community structure, which demonstrates the resilience of bike-sharing system. POI auxiliary analysis provides the inference of community function. The community strategy was recommended for the management and operation of bike-sharing system during public emergency event. From policy perspective, this paper provides deep understanding the mobility patterns of bike-sharing affected by COVID-19 pandemic. A number of fixed trips, such as commuting trips, are going to shift from public transportation to bike-sharing system, which is verified by the increased casual users. Therefore, the management and optimization of bike-sharing should consider the community strategy to provide the service for flexible trips. Further research can be performed to examine the individual mobility patterns under disruption. For instance, the demographic survey could help understand the user behavior and preference to change transportation mode during the emergency events. Moreover, the optimization of bicycles in each docking stations can also be conducted with the consideration of community strategy, which signifies the connection with the community. Declarations Ethical Approval Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research was funded by National Natural Science Funding (CN), grant number 41901396, 42001396 and Youth Innovations Science and technology support project in Colleges of ShanDong Province, grant 2021KJ058. Availability of data and materials The bike-sharing dataset utilized to support the findings of this paper is derived from Capital Bikeshare, available at https://s3.amazonaws.com/capitalbikeshare-data /index.html. The geographic information is derived from Open data DC and is available at https://opendata.dc.gov. References Akar, G., Fischer, N., Namgung, M.: Bicycling choice and gender case study: the Ohio state university. Int. J. Sustainable Transp. 7 (5), 347–365 (2013) Beck, M.J., Hensher, D.A.: Insights into the impact of COVID-19 on household travel and activities in Australia–The early days under restrictions. Transp. Policy. 96 , 76–93 (2020) Benita, F.: Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. Sustainable Cities and Society. 70 , 102916 (2021) Bergantino, A.S., Intini, M., Tangari, L.: Influencing factors for potential bike-sharing users: An empirical analysis during the COVID-19 pandemic. Res. Transp. 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Geogr. 59 , 31–42 (2015) Saberi, M., Mahmassani, H.S., Brockmann, D., Hosseini, A. A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin-destination demand networks.Transportation,1–20. (2016) Saberi, M., Ghamami, M., Gu, Y., Shojaei, M.H.S., Fishman, E.: Understanding the impacts of a public transportation disruption on bicycle sharing mobility patterns: A case of Tube strike in London. J. Transp. Geogr. 66 , 154–166 (2018) Shaheen, S.A., Guzman, S., Zhang, H.: Bikesharing in Europe, the Americas, and Asia. Transp. Res. Rec. 2143 (1), 159–167 (2010) Shen, Y., Zhang, X., Zhao, J.: Understanding the usage of dockless bike sharing in Singapore. Int. J. Sustainable Transp. 12 (9), 686–700 (2018) Su, R., Fang, Z., Xu, H., Huang, L.: Uncovering spatial inequality in taxi services in the context of a subsidy war among E-hailing apps. ISPRS Int. J. Geo-Information. 7 (6), 230 (2018) Tian, Z.H., Zhou, J., Wang, M.G.: Dynamic evolution of demand fluctuation in bike-sharing systems for green travel. J. Clean. Prod. 231 , 1364–1374 (2019) Wang, Y., Zhan, Z., Mi, Y., Sobhani, A., Zhou, H.: Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity. Transp. Res. Part D: Transp. Environ. 104 , 103194 (2022) Wergin, J., Buehler, R.: Where do bikeshare bikes actually go?: analysis of capital bikeshare trips with GPS data. Transp. Res. Rec. 2662 (1), 12–21 (2017) Yang, Y.X., Heppenstall, A., Turner, A., Comber, A.: A spatial and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Comput. Environ. Urban Syst. 77 , 101361 (2019) Zhang, H., Zhuge, C., Jia, J., Shi, B., Wang, W.: Green travel mobility of dockless bike-sharing based on trip data in big cities: a spatial network analysis. J. Clean. Prod. 313 , 127930 (2021) Zhang, Y., Zhang, A., Wang, J.: Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China. Transp. Policy. 94 , 34–42 (2020) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2328657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":156735496,"identity":"52af3fed-c2da-42df-9e27-2731eae26666","order_by":0,"name":"jianmin 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(b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/3467a404b41cbcb93709bc84.png"},{"id":29915161,"identity":"7af3b7e0-e5d6-434b-a4a7-e63c38bb24b3","added_by":"auto","created_at":"2022-12-05 16:19:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe variation of COVID-19 cases and bike-sharing trips\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/3469bc594019ca72d58dfdbe.png"},{"id":29914616,"identity":"254dbd89-d467-41a1-821a-cd055850c1e4","added_by":"auto","created_at":"2022-12-05 16:11:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-resolution analysis for the temporal mobility patterns: Monthly and Weekly\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/47f3c68f786518abf484cadd.png"},{"id":29914618,"identity":"c3d0ec21-c5ac-4c02-ae54-e158cdd20814","added_by":"auto","created_at":"2022-12-05 16:11:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative density for travel distance and duration\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/d9277546830b3ec1057ffc5a.png"},{"id":29913874,"identity":"7225b3be-9311-455c-8cbe-c6169c3696d1","added_by":"auto","created_at":"2022-12-05 16:03:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":284199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial characteristics of daily bike-sharing trips from 2019 to 2021\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/69d88f025f4cec96bb957211.png"},{"id":29913869,"identity":"572bc514-2dba-434d-84e9-4a4bbc785315","added_by":"auto","created_at":"2022-12-05 16:03:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":293478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial clustering characteristics for the bike-sharing trips\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/d62bb699bf057a2752a3598c.png"},{"id":29914620,"identity":"d7080efa-b2ba-45df-866a-8ddb3f0f3f7f","added_by":"auto","created_at":"2022-12-05 16:11:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":285447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommunity Structure of bike-sharing system from 2019 Spring to 2021 Spring\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/1098015ff56e379a258f4678.png"},{"id":29913867,"identity":"64f97788-15b0-463f-a41d-d4076b00b86c","added_by":"auto","created_at":"2022-12-05 16:03:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":125834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between POI category and bike-sharing demand\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/e13420ad5dc2add17b13ee08.png"},{"id":36680263,"identity":"b2f85461-6a35-4dcc-a9cb-6e815928cebb","added_by":"auto","created_at":"2023-05-07 09:59:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2389665,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2328657/v1/46834b0a-a6bb-43a1-94d5-ca9418edd3fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eThrough the widespread promotion around the world, bike-sharing system are growing to be an essential part of the sustainable transportation system (Buck et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fishman et al., 2016; Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Bike-sharing not only provides door-to-door service, but also strengthens the connection between various traffic modes. However, the system is also easily affected by external factors, such as temperature, severe weather and special events (Saberi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Washington D.C., the first case of COVID-19 was announced on March 7, 2020 and the public health emergency was declared on March 11, 2020 (District of Columbia (D.C.) Policy Center., 2020). Subsequently, a series of policies were implemented to prevent the spread of coronavirus (District of Columbia (D.C.) Government, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, the residents were required to stay at home. The employees were able to work from home, while the schools and universities also provided the remote learning. Undoubtedly, the outbreak of COVID-19 was found to significantly affected the economy development, social structure and urban transportation system (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, as an alternative sustainable transportation mode, bike-sharing also has the potential to alleviate the air transmission of virus when compared to the crowed public transit (Kalambay et al., 2022). Consequently, more and more commuters are likely to utilize shared-bicycles other than public transportation modes during the pandemic (Bergantino et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Bike-sharing users\u0026rsquo; perception and mobility patterns were found to be significantly influenced. Kanik (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provided several interesting insights into the decision-making of bike-sharing users in various cities in U.S. The total bike-sharing ridership was found to be decreased, while the most destinations were the pharmacies and grocery stores. Nevertheless, few existing researches investigated the COVID impacts on bike-sharing mobility patterns and community structure.\u003c/p\u003e \u003cp\u003eGiven this background, this paper proposed to provide better understanding, measurement and characterize of the correlation through examining the bike-sharing usage and mobility patterns during the pandemic. The spatial-temporal statistical approach and complex network theory were employed to infer the change of community structure with the consideration of POI (Point of Interests). The comprehensive results obtained in this paper can also provide support for the policymakers in traffic planning and management during the impact of pandemic emergency. The rest of the paper is depicted as follows. In Section 2, the systematic literature review of bike-sharing and traffic impacts from COVID-19 is introduced. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e3\u003c/span\u003e introduces the data sources and methodology. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the spatial-temporal analysis for bike-sharing usage and the mobility patterns obtained from the network perspective. Finally, in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e5\u003c/span\u003e the conclusions and policy implications for bike-sharing system are provided.\u003c/p\u003e "},{"header":"2. Literature Review","content":"\u003cp\u003eWith the development history over half century (Shaheen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), bike-sharing system can be classified into two categories: the docked system with fixed stations and the dock-less system with designated areas (Xu et al., 2018). Both systems have been implemented successfully around the world. For instance, the Capital bike-sharing program in Washington D.C. of U.S. with docked system and the Mo-bike dock-less program in China (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Compared to private car, the bike-sharing system makes huge contribution towards sustainability through promoting environmental benefits (Fishman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), social equity (Lucas et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), public health (Mueller et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and economic gains (Bullock et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to the flexibility and vulnerability of ridership, it is still a challenging issue to investigate the factors affecting the bike-sharing system.\u003c/p\u003e \u003cp\u003eAn extensive of literature have explored the travel pattern analysis of bike-sharing with the consideration of land use, demographic, social-economic features, other transportation mode and weather conditions (Akar et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Noland et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wergin et al., 2017; Jia et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given this context, Buck et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) conducted the trip purpose survey of bike-sharing, which indicated work was the main purpose for long-term members. Gebhart et al. (2014) found the bike-sharing ridership and trip duration were highly affected by the hourly weather condition, while Fishman et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) put emphasis on individual attributes from bike-sharing users. Additionally, the temporal analysis for bike-sharing was conducted to examine the influence from public transit, land use and temporal factors (Wang et al., 2020). Yang et al. (2020) proposed to explore the spatial patterns of ridership through incorporating the demographics into the regression model. In order to investigate the impacts of metro strike, Saberi et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted the spatial-temporal analysis before, during and after the disruption.\u003c/p\u003e \u003cp\u003eSimultaneously, various models and analytical methods were employed in the bike-sharing research, such as the discrete choice models, geographical weighted regression (GWR) model, clustering models and spatial network method (Benita, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Liu et al. (2019) proposed a multinomial logit (ML) model to examine the interaction between land use and bike-sharing demand. Through the random forest method, Wang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found the nonlinear effects from built environment on bike-sharing usage. However, the spatial heterogeneities may result in various impact. Furtherly, Qian et al. (2015) and Ji et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) proposed the GWR and geographically weighted Poisson regression (GWPR) model, respectively, to address the spatial heterogeneities. On the other hand, the network method is also utilized by several researchers. In the research from Yang et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the network of dock-less bikes-haring system (DBS) was built to measure the travel behavior changes before and after a new metro line open. To explore the distribution of travel demand and dynamic properties, Tian et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) created the bike-sharing networks for both docked-based bike-sharing and dock-less bike-sharing systems. Existing researches provide comprehensive understanding of bike-sharing system.\u003c/p\u003e \u003cp\u003eHowever, the outbreak of COVID-19 pandemic brings worldwide challenge to human mobility behavior, which hasn\u0026rsquo;t been met in previous research (Benita, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A huge effort has been made by researchers to investigate the impacts of pandemic on transportation system. Zhang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Li et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposed to explore the dynamic impacts of COVID-19 in terms of the intercity travel. The results showed a non-linear relationship between intercity travel and pandemic mainly due to the change of control policy. The travel pattern analysis on highway network was conducted based on the trajectory information (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the traffic flow pattern was defined as before pandemic, during pandemic and recovery stage. On the other hand, a few of researches focused on the urban transportation system. Parker et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the public transit usage during pandemic. The regression models were built in terms of the social-demographic factors, such as income, gender, occupation and age. Similarly, the spatial lag model (SLM) was employed to investigate the relationship between taxi travel and land use (Nian et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results indicated the recovery of the social activities. Moreover, the performance of bike-sharing system also attracted researcher\u0026rsquo;s concerns. Kalambay et al. (2022) stated the seasonal impacts on ridership and built the mixed-effects negative binomial (MENB) model to assess before-after impact of COVID-19 outbreak. In order to identify passenger travel patterns and habits, Chen et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the spatial-temporal distribution of ridership under pandemic with the complex network method.\u003c/p\u003e \u003cp\u003eAlthough a considerable literature has investigated the bike-sharing program, rare researches put emphasis on the pandemic impacts from system perspective. It is still meaningful to explore the mobility patterns and emergency reaction of bike-sharing users. This paper aims at proposing a systematic approach to measure the impacts in terms of community structure associated with POI (Point of interest), which supports the comprehensive understanding of human civilization and pandemic\u0026rsquo;s recovery.\u003c/p\u003e"},{"header":"3. Data Source Description","content":"\u003cp\u003eAs the selected case area, multiple data sources were collected in Wahington D.C. from January 2019 to December 2021. The bike-sharing program was first launched in 2010 and was widely used around the locals and non-locals in D.C. (Capital bikeshare, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the development over a decade, there are over 5000 bicycles and 600 docking stations providing service, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a). The docking stations with in D.C. area was contained in the study. Simultaneously, the historical information for bike-sharing trip was collected, containing the trip time, trip duration, staring and end station with geographic coordinate and membership category. Specifically, the trip information is anonymous.\u003c/p\u003e \u003cp\u003eIn addition, Washington D.C. government provides the daily report for COVID-19 positives and lives lost from the pandemic outbreak to now, which depicts the changing trends of patient number. Therefore, there are several stages for the research period. For instance, 2019 is the before stage unaffected by pandemic, while 2020 is the outbreak stage influenced by both pandemic transmission and strict prevention policy, such as stay at home and remote working. With better perception of pandemic and policy adjustment, there would be resilient stage in 2021 for society and economy.\u003c/p\u003e \u003cp\u003eOn the other hand, more datasets were considered in this study. In order to examine the spatial mobility patterns, the geographic information of Washington D.C. area and transportation network were extracted (Open Data DC, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the POI (point of interests) information, containing working, recreation, lodging and transportation types, was obtained from the open platform Data.world (data.world, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and there are over 5,000 POIs involved in the dataset, which is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b).\u003c/p\u003e \u003cp\u003eIn this paper, the bike-sharing mobility pattern was investigated and compared before, during and after the COVID-19 outbreak, in terms of spatial and temporal perspectives. Basic statistics, such as patient number, bike-sharing frequency, trip duration and spatial distribution, were analyzed. Moreover, the complex network theory was utilized to conduct the community detection, and the variation of community structure was examined associated with the POI auxiliary analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Network Properties Analysis\u003c/h2\u003e \u003cp\u003eComplex network concept was widely used in the study of network analysis, such as the social relationship network, business relationship and even the multimodal transportation system (Saberi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this paper, the complex network theory was utilized to investigate the bike-sharing mobility patterns in Washington D.C. before, during and after the outbreak of COVID-19 pandemic. In order to construct the network, the docking stations for bicycles are viewed as the nodes, while the pair of OD (origin and destination) between docking station is considered as the link. Therefore, the ODs, i.e. the link, in the network is directed. To model the bike-sharing network in this study, a unique number, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e, is given to each docking station. Specifically, the adjacent matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(A\\)\u003c/span\u003e\u003c/span\u003e was used to present the connectivity between nodes. If there is at least one bicycle trip between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e, the link exists and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is set to 1. Otherwise, the link doesn\u0026rsquo;t exist and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is set to 0. Moreover, the number of trips from node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e to node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e is revealed by the link weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega }_{ij}\\)\u003c/span\u003e\u003c/span\u003e. For comprehensive analysis, other network properties are also examined and expressed as follows.\u003c/p\u003e \u003cp\u003e(1) Node degree\u003c/p\u003e \u003cp\u003eIn order to measure the node centrality, node degree is considered as the most straightforward and convenient metric. Specifically, the most active node is also the most important one. Thus, the node degree indicates the number of links connected to the selected node, which is identified by the adjacent matrix. Additionally, as the bike-sharing network in this study is built as the directed graph, both in-degree and out-degree for the selected node should be considered. Thus, node degree can be expressed as,\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${D}_{i}=\\sum _{j\\in V}{(a}_{ij}+{a}_{ji})$$\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\\(V\\)\u003c/span\u003e\u003c/span\u003e represents the neighbor set of nodes for node \u003cem\u003ei\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{ij}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{ji}\\)\u003c/span\u003e\u003c/span\u003e represents the entry value in the adjacent matrix.\u003c/p\u003e \u003cp\u003e(2) Network connectivity\u003c/p\u003e \u003cp\u003eGenerally, network connectivity demonstrates the connectivity properties from the systematic perspective with the consideration of links and nodes (Saberi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this paper, it is defined as,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$NC=\\frac{2\\times L}{{n}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e represents the number of nodes, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(L\\)\u003c/span\u003e\u003c/span\u003e represents the number of links.\u003c/p\u003e \u003cp\u003e(3) Node flux\u003c/p\u003e \u003cp\u003eAs aforementioned, the link weight represents the number of trips between the nodes. Different to the link weight, the node flux is defined as the total number of trip either starts or ends in the bike-sharing station. Thus, the node flux can be expressed as the Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${NF}_{i}=\\sum _{j}{\\omega }_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e represents the selected node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e represents the nodes connected to node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega }_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the weight between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(4) Clustering coefficient\u003c/p\u003e \u003cp\u003eIn existing researches, the degree of clustering of a network is usually measured by clustering coefficient (Fishman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which indicates the probability that two neighbors of a given node are prone to connect themselves. Addressed as the extent for the node interaction within the network cluster, the clustering coefficient can be computed through the Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for each node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${CC}_{i}=\\frac{2}{{D}_{i}\\left({D}_{i}-1\\right)-2{D}_{i}\\left(R\\right)}{T}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CC}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the clustering coefficient for node \u003cem\u003ei\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the node degree, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}\\left(R\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the reciprocal degree of node \u003cem\u003ei\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the number of directed triangles through node \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e(5) Network efficiency\u003c/p\u003e \u003cp\u003eIn order to measure the information transfer between nodes, the network efficiency can be expressed as,\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$E=\\frac{1}{N(N-1)}\\sum _{i\\ne j}\\frac{1}{{d}_{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the length of shortest path between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e. If there is no bicycle trip between nodes, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is set to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(+\\infty\\)\u003c/span\u003e\u003c/span\u003e. As the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(E\\)\u003c/span\u003e\u003c/span\u003e is ranged from 0 to 1, a larger value indicates the better performance of network efficiency.\u003c/p\u003e \u003cp\u003e(6) Gini coefficient\u003c/p\u003e \u003cp\u003eFirst introduced in the measurement of income distribution, the Gini coefficient is extended to many other issues, such as the inequality of in transportation systems (Feng et al., 2014; Su et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this paper, a general definition of Gini coefficient is employed as,\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$G=1+\\frac{1}{n}-\\frac{1}{{n}^{2}\\stackrel{-}{x}}\\sum _{i=1}^{n}(n-i+1){x}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(G\\)\u003c/span\u003e\u003c/span\u003e is the Gini coefficient and it ranges from 0 to 1. The higher Gini coefficient indicates the inequality. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the service rate of bike-sharing station \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(7) Accessibility\u003c/p\u003e \u003cp\u003eThe accessibility and convenience of the station also attracts scholars\u0026rsquo; attention (Shen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To measure the node accessibility within the network, it can be defined as,\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${AC}_{i}=\\sum _{j=1}^{n}{T}_{j}{\\delta }_{ij}/{t}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the node accessibility. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{j}\\)\u003c/span\u003e\u003c/span\u003e represents the travel demand in node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{ij}\\)\u003c/span\u003e\u003c/span\u003e defines the travel time between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the adjustment coefficient and equal to 0 if the distance between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e is more than 5 km. Otherwise, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{ij}\\)\u003c/span\u003e\u003c/span\u003e is 1.\u003c/p\u003e \u003cp\u003e(8) Spatial autocorrelation\u003c/p\u003e \u003cp\u003eThe Moran\u0026rsquo;s Index is commonly utilized to examine the spatial autocorrelation with regional structure (Ni and Chen, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The typical format of Moran\u0026rsquo;s I can be expressed as,\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$I=\\frac{n}{{\\sum }_{i=1}^{n}{\\sum }_{j=1}^{n}{w}_{i,j}}\\times \\frac{{\\sum }_{i=1}^{n}{\\sum }_{j=1}^{n}{W}_{i,j}({y}_{i}-\\overline{y})({y}_{j}-\\overline{y})}{{\\sum }_{i=1}^{n}({y}_{i}-\\overline{y}{)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, n represents the number of spatial units, i.e. the nodes. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i,j}\\)\u003c/span\u003e\u003c/span\u003e is the spatial weight between node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the observation value of node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\overline{y}\\)\u003c/span\u003e\u003c/span\u003e is the mean value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Detection of Community Structure\u003c/h2\u003e \u003cp\u003eDetection of community structure is one of the most relevant approaches to demonstrate the real system, which can be represented as network or graph (Fortunato, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Therefore, the technique is widely used in the research involved sociology, biology and computer science. In this paper, detecting communities in the bike-sharing network means to find the way to partition the bike-sharing stations into disjoint clusters so that the stations in the same clusters are more densely connected to each other in terms of bike-sharing usage behavior. Basically, a community within the network must represent the similarity and common bike-sharing mobility patterns.\u003c/p\u003e \u003cp\u003eModularity-based method is considered as the classical approach to conduct the community detection (Duan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Newman and Girvan (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) proposed the most popular quality function of modularity, which proposes to find the possible existence of clusters through the comparison between real edges density and the expected density in a subgraph. The number of expected edges falling between pair of nodes can be calculated based on the number of links and node degree. Consequently, the sum of the difference between the real number of edges and expected number of edges over all the pairs of nodes in the same community can be obtained. The basic format of modularity can be expressed as,\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$Q=\\frac{1}{2l}\\sum _{i,j}({A}_{ij}-\\frac{{D}_{i}{D}_{j}}{2l})\\delta ({C}_{i},{C}_{j})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the value in adjacent matrix. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(l\\)\u003c/span\u003e\u003c/span\u003e is the number of links in the network. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the degree of node \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\delta -\\)\u003c/span\u003e\u003c/span\u003efunction generates 1 if node \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e are in the same community, otherwise zero.\u003c/p\u003e \u003cp\u003eIn addition, the information of POI (point of interest) was also involved in the auxiliary analysis to further understand the community structure. Several types of POI, including Eating\u0026amp;Drinking, Government and Public Services, Leisure, Lodging, Tourism, public transport facilities, were selected as introduced in the data source section. In this paper, a buffer area surrounding the bike-sharing station was created to conduct the auxiliary analysis, while the radius is 1 km. To differentiate the specific function, frequency density (FD) and category ratio (CR) were introduced to infer the specific function of each community (Liu et al., 2020). Consequently, FD and CR for each bike-sharing station are determined by the frequency and category of POI within the community area. Specifically, the normalization of the FD was commonly utilized to consider the variance in number of POI. Therefore, the category ratio (CR) can be obtained through the normalized FD. Eq.\u0026nbsp;10 to 12 address the detailed definition.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({FD}_{ij}=\\frac{{P}_{ij}}{{S}_{j}}(i=\\text{1,2},\\dots ,7;j=\\text{1,2},\\dots ,m\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({FD}_{normalize}(i,j)=\\frac{{FD}_{ij}-{FD}_{min}}{{FD}_{max}-{FD}_{min}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({CR}_{ij}=\\frac{{FD}_{normalize}(i,j)}{{\\sum }_{i=1}^{9}{FD}_{normalize}(i,j)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(12)\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\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(m\\)\u003c/span\u003e\u003c/span\u003e is the community number. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represent the number of POI type \u003cem\u003ei\u003c/em\u003e within the community \u003cem\u003ej\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{j}\\)\u003c/span\u003e\u003c/span\u003e represents the total area of community \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results And Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Spatial-temporal mobility patterns\u003c/h2\u003e \u003cp\u003eThe data cleansing process was conducted, as the abnormal information in the original bike-sharing records. For instance, the maximum trip duration is over 1,000 minutes, while the minimum trip duration is below 1 minutes. Such extremely long or short trips are considered as invalid trips, which have to be removed from the dataset to avoid the bias in the analysis.\u003c/p\u003e \u003cp\u003eSubsequently, facing the variation of COVID-19 cases from 2019 to 2021, the temporal mobility patterns of bike-sharing were investigated for member and casual user respectively, as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents. The bike-sharing usage for members has a significant decrease up to 58.61% in the comparison between 2019 and 2020, which is mainly influenced by the outbreak of pandemic in 2020. However, the trips completed by casual users experienced a 40.22% increase at the same time, mainly due to the encouraging policy for bike-sharing and shift from public transport. Interestingly, with the deep perception of COVID-19 pandemic and policy adjustment, the bike-sharing mobility patterns are resilient when compared the system performance 2020 and 2021, which is consistent with the recovery of economy and society.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, multi-resolution analysis, involving monthly and weekly bike-sharing trips, for the temporal mobility patterns were performed before and after the pandemic outbreak as Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It can be found that the member trips play a dominant role in bike-sharing system before the pandemic, while the trips from casual users increased to nearly half of the total trips after the pandemic. Specifically, there is significant seasonal variation around the year mainly due to the weather influence. As for the mobility behavior in a week as Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b), there are more trips in weekday for members and in weekend for casual users, which may be caused by the policy adjustment and tourists.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, cumulative density of travel distance and duration were also examined as Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which indicates the significant difference between members and casual users. Both average trip distance and duration for members are lower than that for casual users. It is mainly caused by the different trip purpose: the members have relative fixed trip time for commuting or mode transfer, while casual users conduct flexible and random trips. Notably, the COVID-19 brought unobvious changes for members before and after the pandemic. In contrast, the casual users performed trips with longer distance and lower duration mainly due to the increased flexible users.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, the spatial patterns for bike-sharing trips were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. There is a homogeneous distribution of bike-sharing trips in central Washington D.C. area before and after the outbreak of COVID-19. However, the pandemic still resulted in huge decrease in total number of trips, as the number of active docking stations, with high travel demand, decrease significantly from 115 in 2019 to 31 in 2020. Specifically, the active docking stations returned to 41in 2021, which indicates the well resilience of bike-sharing system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Mobility Patterns Analysis from Network Perspective\u003c/h2\u003e \u003cp\u003eConsidering the seasonal effects on the bike-sharing system, four seasons, including Winter (from December of the previous year to February of the current year), Spring (from March to May), Summer (from June to August) and Fall (from September to November), were extracted to investigate the bike-sharing mobility patterns before and after the COVID-19 pandemic from network perspective. The network properties for bike-sharing system from 2019 to 2021 was provided by Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results indicate that the number of nodes were relatively stable while the number of links was fluctuated, which was influenced by the decreased trips between partial docking stations. Notably, the network connectivity in the Spring experienced approximately 20% decrease between 2019 and 2020. The disruption of pandemic resulted in the massive cancellation for membership trips, which decreased the network connectivity. While in 2021, the connectivity was improved due to the returning member and increased casual users.\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\u003eComparison of network properties in different periods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c19\" namest=\"c15\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eWinter\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003eSpring\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003eAutumn\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLink\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26,197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e26,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e18,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23,142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e25,089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e22,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23,911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e24,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e25,056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConnectivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e149.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e177.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e125.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e155.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e178.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e170.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e148.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e163.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e171.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e173.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax Degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean link weight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e16.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e12.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax link weight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1,143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1,281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Flux\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,527.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,663.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,932.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e2,581.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e576.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e875.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1,513.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e1,370.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e998.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1,252.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1,768.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1,880.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax Flux\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e15,675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2,984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3,588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e5,795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4,737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e6,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e7,944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e7,837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEfficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGini coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccessibility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e853.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,099.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,242.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e1,231.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e272.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e329.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e482.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e505.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e508.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e472.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e668.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e743.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMoran\u0026rsquo;s I\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.57\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\u003eThe node degree presents the number of stations connected to the selected stations. Similarly, the mean node degree decreased in 2020 and increased in 2021, which indicates the fluctuation of interaction between bike-sharing stations. However, the stations with the most node degree still provide stable performance. These stations, ensuring the necessary trips to be available even under the pandemic, should be concerned in the emergency policy making.\u003c/p\u003e \u003cp\u003eAdditionally, link weight describes the trips between two stations, while the node flux presents the total number of trips either begins or ends on the selected station. When compared the statistics in 2019 and 2020 in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the pandemic caused the decrease ranging from 40\u0026ndash;80%. In contrast, there is a strong recovery in 2021. For instance, the max link weight in Autumn increased from 470 to 1,281. Depicting the node interaction within the network cluster, the average CC (clustering coefficient) demonstrates worse local connection in bike-sharing network after the COVID-19 pandemic. The value of network efficiency and accessibility also prove the well performance of bike-sharing network in normal condition. Interestingly, the Gini coefficient after the pandemic is lower, which means the relatively even distribution of bike-sharing demand. It is mainly caused by the inactivity of docking stations with low demand.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, the value of Moran\u0026rsquo;s I are over 0.5 from 2019 to 2021, which suggests the potential community structures in bike-sharing network. Consequently, the local Moran\u0026rsquo;s I index was utilized to better understand the spatial clustering characteristics of bike-sharing trips, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. High-High (Low-Low) Cluster defines the stations, with high (low) bike-sharing demand, are also surrounded by neighbor stations with high (low) bike-sharing demand. High-Low (Low-High) outlier defines the stations, with high (low) bike-sharing demand, are surrounded by neighbor stations with low (high) bike-sharing demand. NS means not significant. It is found that the bike-sharing trips mainly concentrates in the central area of Washington D.C., where the land-use is mixed and bicycle facilities are well implemented. In contrast, the outer area is filled by the bike-sharing stations with low demand. This spatial distribution indicates the spatial heterogeneity in bike-sharing demand.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Community Detection and POI auxiliary analysis\u003c/h2\u003e \u003cp\u003eConsidering the emergence of COVID-19 in March 2020 and the seasonal variations, the bike-sharing in Spring was selected to explore the Community Structure. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the community structure based on the modularity-based approach. The detected community in 2019 and 2021 demonstrates similar size and distribution and are more locally connected, which is consistent with the network clustering coefficient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, during the outbreak of COVID-19 pandemic in 2020, there are only three large-scale communities for bike-sharing system. The increased long-range and long-time bike-sharing trips strengthen the connection between central communities and outer communities, which finally results in the integration of small communities and central communities. Therefore, the community strategy is recommended for the scheduling of shared bicycles during the public emergency event. Specifically, the number of bicycles in service should be reassigned according to the community size and location.\u003c/p\u003e \u003cp\u003eTo better understand the function of various community, the community structure obtained was further examined in terms of POI. As described in \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003emethodology\u003c/span\u003e section, a buffer area, with 1 km radius, surrounding the bike-sharing station was created to conduct the POI auxiliary analysis. Only POIs within the buffer area is considered. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates, community 2 is located in the central urban area with more service facilities, such as tourism and leisure. Notably, this community demonstrates a similar proportion of POI before and after the outbreak of COVID pandemic. Public Transport, Tourism and Leisure account for approximately 60% of all POI types. Thus, community 2 is the active for recreation. Community 1 and community 3 are located in outer suburban area and connected to central area. These communities also have similar POI distribution before and after the outbreak of pandemic. However, besides the Eating and Drinking facilities, community 1 provides over 20% of lodging, while community 3 provides over 20% of Government and Public Services. This indicates the various function between community 1 and community 3. On the other hand, community 4, dominated by Government and Public Services, Public transport and Leisure, is integrated with community 3 during outbreak of pandemic in 2020 Spring. Interestingly, with the recovery of bike-sharing system, community 5 is located in different position in 2019 and 2021. Tourism POI within community 5 in 2019 is over 40%, while Government and Public Services POI within community 5 in 2019 is over 40%, which is mainly caused by the flexible trips of casual users.\u003c/p\u003e \u003cp\u003eOn the other hand, to examine the relationship between the number of POI within the buffer area and bike-sharing demand for docking stations, the correlations analysis was conducted and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The results indicate the number of lodging, leisure and public transport facility have strong correlations with bike-sharing demand. The lodging and leisure are area with dense population, which generates huge traffic demand. The number of public transport facility provide the public service, while the bike-sharing system provides the connection between various transportation modes. The low value of tourism demonstrates the weak relationship with bike-sharing demand.\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\u003eComparison of CR (category ratio)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory Ratio%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEating \u0026amp; Drinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGovernment and Public Services\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e42.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeisure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLodging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePublic Transport\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e17.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTourism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.81\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 \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eContributing to current research related to bike-sharing behavior, this paper proposed a comprehensive approach to investigate the mobility pattern influenced by the outbreak of COVID-19 pandemic. Through spatial-temporal analysis of mobility patterns, statistical comparison of network properties and community inference with POI, a deep understanding of the bike-sharing behavior and community structure was provided. Multiple-source dataset in Washington D.C., containing bike-sharing trip information, COVID-19 information, geographic layout and POI information, were utilized to in the study. Major findings are summarized as follows.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAlthough serious affected by the pandemic, the bike-sharing trips increased with the recovery of economy and society. Specifically, the casual users increased dramatically.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn network perspective, major metrics, such as connectivity, clustering coefficient, and accessibility, experienced significant decrease during the pandemic. The change of network property also indicates the spatial heterogeneity in bike-sharing demand.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe detection of community captures the evolution of community structure, which demonstrates the resilience of bike-sharing system. POI auxiliary analysis provides the inference of community function. The community strategy was recommended for the management and operation of bike-sharing system during public emergency event.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFrom policy perspective, this paper provides deep understanding the mobility patterns of bike-sharing affected by COVID-19 pandemic. A number of fixed trips, such as commuting trips, are going to shift from public transportation to bike-sharing system, which is verified by the increased casual users. Therefore, the management and optimization of bike-sharing should consider the community strategy to provide the service for flexible trips.\u003c/p\u003e \u003cp\u003eFurther research can be performed to examine the individual mobility patterns under disruption. For instance, the demographic survey could help understand the user behavior and preference to change transportation mode during the emergency events. Moreover, the optimization of bicycles in each docking stations can also be conducted with the consideration of community strategy, which signifies the connection with the community.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by National Natural Science Funding (CN), grant number 41901396, 42001396 and Youth Innovations Science and technology support project in Colleges of ShanDong Province, grant 2021KJ058.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bike-sharing dataset utilized to support the findings of this paper is derived from Capital Bikeshare, available at https://s3.amazonaws.com/capitalbikeshare-data /index.html. The geographic information is derived from Open data DC and is available at https://opendata.dc.gov.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkar, G., Fischer, N., Namgung, M.: Bicycling choice and gender case study: the Ohio state university. Int. J. Sustainable Transp. \u003cb\u003e7\u003c/b\u003e(5), 347\u0026ndash;365 (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck, M.J., Hensher, D.A.: Insights into the impact of COVID-19 on household travel and activities in Australia\u0026ndash;The early days under restrictions. Transp. Policy. \u003cb\u003e96\u003c/b\u003e, 76\u0026ndash;93 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenita, F.: Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. 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Policy. \u003cb\u003e94\u003c/b\u003e, 34\u0026ndash;42 (2020)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Bike-sharing, Spatial-temporal Analysis, Complex Network, Community Detection, POI","lastPublishedDoi":"10.21203/rs.3.rs-2328657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2328657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe outbreak of COVID-19 brings huge challenges to the bike-sharing system and even society structure. Thus, it is urgent to fully understand the impacts of pandemic on bike-sharing behavior. This paper proposed a comprehensive approach to investigate the mobility patterns influenced by the outbreak of COVID-19 pandemic with the case in Washington D.C. Multiple-source data, including bike-sharing trip information, COVID-19 information, geographic and POI information, were collected. Although the total bike-sharing trips decreased up to 80% in spatial-temporal analysis, the trips made by casual user still increased. In addition, the docking stations and trips from 2019 to 2021 were utilized to construct the bike-sharing network. The results present that major network properties, such as connectivity, clustering coefficient, and accessibility, experienced significant decrease during the pandemic. Through the detection of community with modularity method, the evolution of community structure before and after pandemic was captured. The increased long-range and long-time bike-sharing trips results in the combination between central communities and outer communities. To better understand the community structure, the POI (Point of Interests) auxiliary analysis was conducted and central community was found to have similar proportion of POIs even during the pandemic. Implications for bike-sharing management and operation policy was also addressed.\u003c/p\u003e","manuscriptTitle":"Understanding Bike-sharing Mobility Patterns in Response to the COVID-19 Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-05 16:03:26","doi":"10.21203/rs.3.rs-2328657/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a8e1c19-c6c8-4e5c-81f8-dc6268193bb2","owner":[],"postedDate":"December 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-07T09:59:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-05 16:03:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2328657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2328657","identity":"rs-2328657","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}