A Methodology Framework for Predicting Economic Loss in Korean Peninsula Railway Network

A Methodology Framework for Predicting Economic Loss in Korean Peninsula Railway Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Methodology Framework for Predicting Economic Loss in Korean Peninsula Railway Network Jiyun Jeon, Ji Hyeon Kim, Mintaek Yoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8373543/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 This study proposes a quantitative assessment framework for evaluating the economic losses of simulated railway networks under seismic events. Accordingly, a simulated railway network was constructed based on the existing rail system of the Korean Peninsula, and the transportation volume for each line segment was quantified using the OD(Origin–Destination) Matrix with passenger and freight movement data. Subsequently, the seismic fragility curve and the restoration curve were integrated to derive the seismic functionality loss curve, through which the functionality loss of major structure components, including bridges, embankments, and tunnels, was estimated. In addition, the seismic characteristics of the Korean Peninsula were incorporated by applying the Korean ground-motion attenuation equation and short-period amplification factors to the earthquake scenario. The derived functionality loss of each structure component was then integrated with the corresponding transportation volume to estimate the transportation volume loss for each line segment. The economic value loss was applied to the transportation volume to ultimately evaluate the transportation revenue for each segment. As a result, passenger transportation volume and transportation revenue in High-Speed lines decreased by 50–60%, with cumulative losses reaching hundreds of billions of KRW over the restoration evaluation period of 100 days. Meanwhile, the two conventional lines responsible for freight transport also showed similar loss rates, with cumulative losses reaching tens of billions of KRW over the restoration evaluation period of 100 days. In terms of monetary value, passenger transport losses were 2.5-3 times greater than freight transport losses; however, considering the low substitutability of freight transport and its cascading ripple effects across industries, the practical economic impact of the freight sector is judged to be more significant. The methodology proposed in this study quantitatively demonstrates how the functional degradation of individual structures in a railway network during seismic events translates into actual economic loss. This framework provides a systematic foundation for supporting future decisions on restoration prioritization, evaluating the feasibility of seismic reinforcement investments, and establishing earthquake response strategies. Physical sciences/Engineering Earth and environmental sciences/Natural hazards Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The Korean Peninsula has been shown to no longer be a seismically safe region, as evidenced by large-magnitude earthquakes such as the 2017 Pohang earthquake ( \(\:{M}_{L}\) 5.4) and the 2016 Gyeongju earthquake ( \(\:{M}_{L}\) 5.8) (Kim et al. (2020) 1 and Kim et al. (2016) 2 ). In addition, with the recurrence interval of the Nankai earthquake approaching, its potential impacts on the Korean Peninsula cannot be ruled out, emphasizing the need for establishing appropriate preparedness strategies. Cui et al. (2024) 3 reported that the maximum direct losses associated with the Nankai earthquake are estimated to reach approximately 167.8 trillion (JPY), representing an astronomically large scale of damage. These losses were found to be concentrated in highly urbanized and industrialized regions such as Tokyo, Osaka, and Shizuoka, indicating the potential for not only substantial economic impacts but also significant human casualties. Accordingly, the necessity of preparing in advance for potential economic losses arising from large-scale disasters such as earthquakes is further emphasized. In particular, railway networks function as a critical axis for population movement and industrial logistics, and due to their concentrated distribution along major industrial and urban areas, they are likely to cause widespread and significant impacts in the event of a disaster. Railway networks are systems in which multiple structural components are connected in series, characterized by the propagation of damage to a single structural element during seismic events into functional degradation of the entire network. However, existing seismic evaluation frameworks focus on performance verification of individual structures, and research on economic damage prediction at the network level is limited. In the initial response phase following an earthquake, recovery resources (equipment, personnel, and budget) are distributed across multiple damage points; therefore, determining restoration priorities based on economic loss and establishing reinforcement plans for vulnerable structures are essential for recovering network functionality. Moreover, since functionality loss of networks results in economic losses such as reduced transportation volume and logistics delays, the development of predictive models capable of quantifying these losses in advance is required. In this context, this study proposes an analytical framework for quantitatively estimating the economic loss of railway networks following earthquakes and establishing restoration strategies and reinforcement plans based on these estimations. Regarding previous research utilizing OD matrix, Basso et al. (2022) 4 proposed a methodology for estimating comprehensive freight vehicle OD matrices through a multi-data-based approach that integrates freight vehicle traffic data from urban highways with GPS samples. Ceccato et al. (2022) 5 integrated Floating Car Data (FCD) with traditional traffic data through various methods to investigate the correlation between OD matrix accuracy and data collection costs, and proposed an optimal cost-effective data combination. Additionally, Galliani et al. (2024) 6 combined ticket sales data with Automatic Passenger Counting (APC) data and applied the IPF algorithm to present a dynamic OD matrix estimation method that can be updated weekly in real railway networks. Meanwhile, regarding previous component-level studies on bridges, embankments, and tunnels comprising railway networks, Bakhtiari et al. (2020) 7 conducted IDA incorporating nonlinear dynamic analysis and SSI for high-speed railway bridges and presented seismic fragility curves according to pier height variations. Crespi et al. (2025) 8 developed seismic fragility curves for major damage modes targeting 1,182 highway bridges in Italy, and Annad et al. (2023) 9 improved the assessment accuracy through vulnerability analysis of shallow foundation bridges considering combined hazards of scour and earthquake and fuzzy-based damage classification. Additionally, Nielson et al. (2007) 10 proposed a component-level approach that assesses the seismic fragility of entire bridges by integrating the damage probabilities of individual components comprising the bridges. Regarding previous studies on embankments, Shinoda et al. (2022) 11 practically evaluated the fragility of Japanese railway embankments using three seismic intensity measures: PGA, PGV, and Arias Intensity. Hübner et al. (2020) 12 analyzed the correlation between embankment displacement and seismic intensity measures through 1D and 2D ground response analyses, and based on this, presented lognormal seismic fragility functions. Mohammadi et al. (2023) 13 also evaluated the settlement and deformation behavior of railway embankments through probabilistic dynamic analysis applying nonlinear soil model and multiple ground motion records, and developed fragility curves for each damage state. Petala et al. (2024) 14 numerically simulated the effects of fault rupture on embankments and evaluated the seismic fragility of highway embankments during fault rupture propagation. Regarding previous studies on tunnels, Yang et al. (2023) 15 compared and classified existing fragility models for bored tunnels according to burial depth and ground conditions, and reconstructed a weighted combined representative fragility function based on this. Huang et al. (2022) 16 proposed a time-dependent seismic fragility function for circular tunnels embedded in soft ground, reflecting long-term deterioration and time-dependency of soil-tunnel interaction. Liu et al. (2024) 17 conducted nonlinear numerical analysis for circular tunnels in saturated sandy soil and presented fragility curves that quantified damage probabilities under various seismic loading conditions, and Qi et al. (2026) 18 evaluated the seismic response of shield tunnel segments and developed seismic fragility curves based on the probability of damage occurrence. The HAZUS guidelines by the U.S. Federal Emergency Management Agency FEMA (2024) 19 also present an assessment framework centered on seismic fragility curves and recovery curves for individual infrastructure components such as bridges, embankments, and tunnels, but do not address the changes in functionality across the entire railway network or the evaluation of socioeconomic impacts during earthquakes. Most existing studies have also focused on developing fragility curves at the individual component level comprising railway networks and analyzing damage and functionality degradation characteristics. Yoo et al. (2025) 20 proposed a model that calculates component functionality loss by combining seismic fragility curves and recovery curves, and evaluates the functionality degradation of railway networks based on this, but quantitative analysis of railway network transportation revenue loss has still not been sufficiently conducted. Accordingly, this study aims to quantitatively predict transportation revenue losses in railway networks following earthquake events. Based on these predictions, a foundational framework is established for rational restoration prioritization and seismic retrofitting strategies for vulnerable structural components. To achieve this, seismic functionality loss curves are derived by combining seismic fragility curves and restoration curves. These curves are then linked with OD matrix-based population and freight transportation volumes and revenues to present a consistent analytical framework that traces the damage propagation process from functionality loss of individual structural components to overall functionality loss of the railway network, ultimately resulting in transportation revenue loss. Furthermore, the proposed methodology is applied to a simulated railway network constructed based on the Korean Peninsula railway system to estimate transportation revenue loss for High-Speed lines and Conventional lines. By identifying vulnerable segments and critical bottleneck sections based on a restoration evaluation period of 100 days, this study proposes an economic loss prediction methodology that quantitatively supports future restoration strategies and pre-emptive reinforcement investment directions. Methodology Process of Economic Loss Prediction To derive the economic damage of railway networks, it is necessary to develop a model that can quantitatively calculate transportation volume loss. First, the transportation volume of passengers and freight for each station in the railway network must be identified, and the functionality loss of structure components during seismic events must be quantitatively assessed. In this study, the OD Matrix, a data analysis matrix, was utilized to determine the transportation volume for each station, and seismic functionality loss curves were employed to assess the functionality loss of structure components. The process of deriving the economic loss of railway networks is illustrated in Fig. 3 . Transportation Volume and Transportation Revenue Prediction This study sequentially applied proportional distribution, proportional renormalization, and the Fratar method to estimate the passenger and freight transportation volume of the railway network. The proportional distribution method constructs the initial OD Matrix based on the total production and attraction of origins and destinations, while the proportional renormalization process compensates for total volume inconsistencies and cumulative errors that may occur during the transportation volume distribution process. The Fratar method iteratively satisfies the total volume constraints of each origin and destination while maintaining the distribution pattern of the existing OD Matrix, thereby ensuring consistency with actual data. The formulas for the proportional distribution and proportional renormalization processes are shown in Eq. ( 3 )-( 4 ). $$\:{OD}_{ij}=\:\frac{Origi{n}_{i}\:\times\:\:Destinatio{n}_{j}}{\sum\:Destination}$$ 3 $$\:{OD}_{ij}^{{\prime\:}}={OD}_{ij}\times\:\:\frac{Origi{n}_{i}\:}{{\sum\:}_{i}O{D}_{ij}}$$ 4 In addition, by utilizing the transportation revenue calculated by combining passenger count and freight transportation volume with passenger fares or freight rates, the proportion of passenger and freight movement before the earthquake can be identified, and the economic value can also be determined. Seismic Functionality Loss Prediction for Structure Component To quantitatively derive the economic damage to railway networks, the development of seismic functionality loss curves is essential, and these curves can be derived by combining seismic fragility curves and restoration curves. This study derived seismic functionality loss curves based on the formulas presented by Huang et al. (2022) 21 to obtain functionality loss values for each component of the railway network. The process of deriving seismic functionality loss curves is shown in Fig. 4 . To derive the seismic functionality loss curve, deriving the functionality curve is necessary, and the equation for deriving this curve is shown in Eq. 5 . Through the functionality curve, the functionality of a structure over time for a specific seismic intensity can be probabilistically evaluated, and it is constructed in a manner that comprehensively considers the functionality recovery characteristics for each damage state and the probability of damage occurrence. Here, \(\:Q\left[{ds}_{i}\right|t]\) represents the level of functionality that the structure has recovered at time \(\:t\) after the earthquake for each damage state. This is calculated based on the restoration curve, which expresses the restoration process of the structure as a time-functionality curve, and is derived by reflecting the average restoration period, restoration rate, and restoration speed for each damage state. $$\:Q\left(t\right)=\:\sum\:_{i=0}^{4}Q\left[d{s}_{i}|t\right]P\left[d{s}_{i}|IM\right]$$ 5 Next, \(\:P\left[{ds}_{i}\right|IM]\) represents the probability that the structure will experience damage state given a specific seismic intensity (IM), and is calculated using the formulas for each damage state of the seismic fragility curve as shown in Eq. (6)-(8). \(\:P\left[{ds}_{i}\right|IM]\) derived through these formulas quantitatively expresses the damage level that may occur in the structure as seismic intensity varies, and allows the likelihood of each damage state to be identified. \(\:P\left[{ds}_{i}|IM\right]=1-P\left[ds>d{s}_{i+1}|IM\right],\) \(\:when\:i=0\) (6) \(\:P\left[{ds}_{j}|IM\right]=P\left[ds>{ds}_{j}|IM\right]-P\left[ds>{ds}_{j+1}|IM\right],\) \(\:when\:j=1,\:2,\:3\) (7) \(\:P\left[{ds}_{k}|IM\right]=P\left[ds>{ds}_{k}|IM\right],\) \(\:when\:k=4\) (8) Using the seismic functionality curve \(\:Q\left(t\right)\) defined above, the functionality loss from the time of earthquake occurrence ( \(\:{t}_{0}\) ) to the restoration evaluation period ( \(\:{t}_{f}\) ) can be derived for each seismic intensity using Eq. ( 9 ). In other words, it is a concept that calculates how much functionality the structure maintained on average during the restoration evaluation period ( \(\:{t}_{f}\) ) at a specific seismic intensity (IM), and considers the shortfall as functionality loss. For example, given a specific structure's functionality curve as shown in Fig. 5 , if the structure's functionality during the restoration evaluation period of 100 days appears to be approximately 0.7 under an earthquake scenario with PGA of 0.4g, the functionality loss becomes 0.3. This means that the structure operated with approximately 30% functionality loss for 100 days after the earthquake, and the shaded area in Fig. 5 corresponds to the functionality loss. $$\:Functionality\:Loss\:\left(L\right)=1-\frac{{\int\:}_{{t}_{0}}^{{t}_{f}}Q\left(t\right)}{{t}_{f}-{t}_{0}\:}$$ 9 when PGA = 0.4g, restoration evaluation period: 100 days Figure 5 . Schematic drawing of seismic functionality loss, After evaluating the functionality loss of individual structure components using this method, the overall functionality loss of the railway network is estimated by assigning, to each line segment, the maximum functionality loss among the structure components composing that segment. Transportation Volume Loss and Transportation Revenue Loss Prediction To evaluate transportation volume loss in railway networks during seismic events, the baseline transportation volume of the entire network is first calculated using the OD Matrix, boarding and alighting data for each station, and generation-attraction data. The calculated transportation volume is then combined with the functionality loss of each structure component based on functionality loss curves, ultimately deriving the transportation volume loss of the railway network. This enables quantitative assessment of how the performance degradation of individual structures affects the overall transport capacity of the railway network. In addition, by linking passenger fares and freight rates to passenger and freight transportation data and utilizing the OD Matrix along with the calculated economic value, a transport revenue matrix for the network can be constructed. Economic loss can also be combined in the same manner to predict transportation revenue loss caused by earthquakes, enabling comprehensive evaluation of how structural damage to the railway network impacts not only transportation volume but also economic value. The transportation volume and economic loss prediction procedure proposed in this study can serve as essential foundational data for quantitatively assessing the economic damage caused by functional degradation of railway networks due to earthquakes. Through the derived transportation volume loss and transportation revenue loss, the actual economic impact scale of service disruptions and functional degradation on society during seismic events can be identified, providing crucial evidence for determining restoration priorities, establishing emergency response strategies, and estimating restoration costs. Economic losses include passenger and freight delay costs, revenue losses due to transport unavailability, alternative operation costs, and restoration costs. Furthermore, by analyzing the relationship between functionality loss and transportation volume loss for each line, the impact of specific structure damage on the overall network operational efficiency can be quantitatively evaluated. This enables vulnerability assessment from a network-wide perspective beyond the individual structure level, making it possible to establish efficient disaster response and restoration strategies that consider systemic resilience. In conclusion, this study establishes an integrated evaluation framework linking functionality degradation–transportation volume loss–economic loss of railway networks, thereby providing practical and quantitative decision-making criteria for policy decisions such as seismic reinforcement investments and restoration planning. Related works OD Matrix The OD (Origin-Destination) Matrix is a data analysis matrix for quantifying spatial travel demand within transportation networks, representing the number of passengers and freight volume traveling from origin zone \(\:i\) to destination zone \(\:j\) . Each matrix element \(\:{T}_{ij}\) represents the travel volume between Origin and Destination, and the OD Matrix is utilized as key input data for trip distribution, capacity assessment, and system performance analysis under normal and bridge conditions by comprehensively describing the movement patterns of railway network transportation volume. Additionally, the Fratar method of proportional fitting was employed to iteratively calibrate transportation volumes between each Origin-Destination pair in the transportation network. In the Fratar method, the total production of each origin zone and the total attraction of each destination zone are defined as \(\:{O}_{i}={\sum\:}_{j}{T}_{ij}\) and \(\:{D}_{j}={\sum\:}_{i}{T}_{ij}\) , respectively, and must satisfy the conservation condition of total traffic flow \(\:{\sum\:}_{i}{O}_{i}={\sum\:}_{j}{D}_{j}={\sum\:}_{i,j}{T}_{ij}\) . In actual transportation networks, directional imbalances exist, so generally \(\:{T}_{ij}\ne\:{T}_{ji}\) . Seismic Fragility Curve A seismic fragility curve is a statistical function that expresses the probability of a structure component reaching or exceeding a certain damage state at a specific seismic intensity level (e.g., PGA or Sa), and generally, fragility curves are standard cumulative distribution functions that express the exceedance probability as a logarithmic function of intensity. There are two main methods for deriving seismic fragility curves: the numerical method, which statistically calculates damage probabilities by applying ground motions to structures, and the empirical method, which calculates probabilities based on observed data from past earthquakes. In practice, when observational data are insufficient, numerical results or literature values are referenced to construct the final curves. Since this study applied the methodology to a simulated railway network, the numerical method was utilized to evaluate transportation revenue loss. The seismic fragility function is shown in Eq. (1), and the seismic fragility curves provided in FEMA (2024) 19 are presented in Fig. 1 . In this equation, \(\:P\left[{ds\ge\:ds}_{i}|IM\right]\) represents the probability of exceeding a given damage state for a specific seismic intensity measure (IM), and \(\:\varPhi\:(\cdot\:)\) denotes the cumulative distribution function of the standard normal distribution. \(\:{\mu\:}_{i}\) and \(\:{\beta\:}_{i}\) represent the mean and standard deviation of the natural logarithm of the intensity measure, respectively, and the damage states of structure components are classified into five levels: none ( \(\:{ds}_{0}\) ​), minor ( \(\:{ds}_{1}\) ​), moderate ( \(\:{ds}_{2}\) ​), extensive ( \(\:{ds}_{3}\) ​), and complete ( \(\:{ds}_{4}\) ​). \(\:P\left[{ds\ge\:ds}_{i}|IM\right]=\:\varPhi\:\left(\frac{ln\left(\right(IM)-{\mu\:}_{i})}{{\beta\:}_{i}}\right)\) (1) Restoration Curve A restoration curve is a curve that represents how the functionality level of a component changes over time after an earthquake, allowing the identification of expected damage extent and expected restoration time caused by the earthquake. Additionally, it is utilized as a key indicator for quantitatively evaluating the time required for the functionality of a component to return to a fully functional state, and provides important basis for establishing disaster response plans and analyzing the restoration speed to normal operation of structures. The standard normal distribution function Eq. (2) was utilized to derive restoration curves, and Fig. 2 presents the restoration curves provided by FEMA (2024) 19 . In this formula, \(\:Q\left[{ds}_{i}\right|t]\) denotes the time-dependent probability of restoration for each damage state, where \(\:t\) represents the restoration evaluation period. The term \(\:\varPhi\:\left(\cdot\:\right)\:\) is the cumulative distribution function of the standard normal distribution, while \(\:\delta\:\) and \(\:\:\gamma\:\) correspond to the mean and standard deviation, respectively, of the lognormal distribution governing the restoration time. \(\:Q\left[{ds\ge\:ds}_{i}|t\right]=\:\varPhi\:\left(\frac{\text{l}\text{n}\left(\right(t)-{\delta\:}_{i})}{{\gamma\:}_{i}}\right)\) (2) Results and Application Application of the Proposed Methodology to Simulated Railway Network In this study, the Korean Peninsula railway network was selected as the research subject to quantitatively evaluate the economic damage to railway networks during seismic events, and appropriate earthquake scenarios were established. The railway network under study was constructed to include both high-speed lines and conventional lines in order to derive economic loss for passengers and freight. For the high-speed line, the Honam High-Speed Railway section was adopted, and the actual operating segment consisting of five stations was reflected in the model. This segment is a representative high-speed rail axis that plays a critical role in regional and long-distance passenger transport. The conventional lines were based on the actual routes of the Gyeongbu Line, Honam Line, and Jeolla Line, and a network consisting of a total of 38 stations was constructed using schematic diagrams, railway route maps, and latitude-longitude coordinates for each station. Through this, a dual railway network structure was implemented in which high-speed lines primarily handle passenger transport and conventional lines primarily handle freight transport. To derive the PGA for each structure component, GIS-based ground classification data were utilized across the entire Honam High-Speed Railway section to subdivide bridge, embankment, tunnel, and cut segments, and the length and ground characteristics of each segment were determined. Using ground classification (S1 ~ S5), shear wave velocity (Vs), and ground composition information, the calculation of structure component lengths for the Honam High-Speed Railway confirmed that it consists of 19 bridges, 18 embankments, 7 tunnels, and 8 cut segments. For conventional lines, the structure component lengths for each segment were also calculated based on latitude-longitude data for each station, resulting in 39 bridges, 42 embankments, and 15 tunnel segments. This data is utilized as foundational data for calculating functionality loss for each component and evaluating the functionality degradation of the entire network. The railway network modeling image for which transportation volume loss was evaluated in this study based on the given data is shown in Fig. 6 , and the target lines are as follows: (1) High-Speed Line 1: Osong–Gongju–Iksan–Jeongeup–Gwangju Songjeong (2) Conventional Line 2: Osong –Daejeon–Dongiksan–Dongsan (3) Conventional Line 3: Osong – Daejeon –Hanam Definition of Earthquake Scenarios The earthquake scenario was established based on the Korean Peninsula seismic risk assessment map proposed by Lee et al. (2022) 22 to consider conditions that could cause actually significant damage to the railway network proposed in this study. The epicenter location was set at a point approximately 13 km from Iksan station (35.99°N, 127.08°E), and the earthquake magnitude was set to \(\:{M}_{L}\) = 6.0, similar to the 2016 Gyeongju earthquake. The focal depth was selected as 7 km for conservative application in this study, based on the report by Grigoli et al. (2018) 23 that the focal depth of the Pohang earthquake was 3 to 7 km. This is to simulate realistic earthquake disaster scenarios that domestic railway infrastructure may face by reflecting moderate-scale earthquake cases observed on the Korean Peninsula. Next, an attenuation relationship considering epicentral distance and ground conditions is required to calculate the spatial distribution of seismic dynamic loads. This study calculated the bedrock peak ground acceleration (PGA) at each component location by applying the ground motion attenuation equation Eq. ( 10 ) and model coefficients Table 1 from Emolo et al. (2015) 24 , which reflect the ground characteristics of the Korean Peninsula. Subsequently, the final surface PGA was derived by applying the short-period amplification factors by ground type Table 2 from the Korean seismic design code KDS 17 10 00 (2024) 25 . In other words, the amplified surface acceleration at the point where the actual structure is located was calculated by reflecting the short-period amplification factors according to the ground classification (S1 to S5) of each section to the PGA of each component. The surface PGA calculated in this manner is subsequently utilized as the input seismic intensity (Intensity Measure, IM) for calculating functionality loss curves for each component such as railway bridges, embankments, and tunnels. $$\:{ln}PGA=a+bM+c\:ln\:[\:\sqrt{{\:R}_{epi}^{2}+{h}^{2}}\:]+\:{dR}_{epi}+e$$ 10 Table 1 Attenuation equation parameters for Korean Peninsula, data: Emolo et al. (2015) 24 Parameter a b c h d e PGA ( \(\:\text{m}/{\text{s}}^{2}\) ) -3.07 0.76 -0.76 1.7 -0.0029 0.326 Table 2 Short-period amplification factors, data: KDS 17 10 00 (2024) 25 Ground Type \(\:\text{S}\le\:0.1\) \(\:\text{S}=0.2\) \(\:\text{S}\ge\:0.3\) \(\:{\text{S}}_{2}\) 1.4 1.4 1.3 \(\:{\text{S}}_{3}\) 1.7 1.5 1.3 \(\:{\text{S}}_{4}\) 1.6 1.4 1.2 \(\:{\text{S}}_{5}\) 1.8 1.3 1.3 Transportation Volume and Transportation Revenue Prediction of Railway Network To determine the transportation volume for High-Speed Line 1, which is responsible for passenger transport, station-by-station transportation performance data provided by KORAIL (2024) 26 was utilized. The boarding and alighting passenger counts for each station are shown in Table 3 . Additionally, to estimate freight volumes on Conventional Line 2 and Conventional Line 3, which handle freight transportation, urban freight data provided by KOTI (2020) 27 were utilized. The transportation data for Conventional Line 2 are presented in Table 4 . Based on the provided information, the derived transportation volume matrices for High-Speed Line 1 and Conventional Line 2 are presented in Table 5 and Table 6 , respectively. Next, to estimate the passenger transport economic value for High-Speed Line 1, the monetary value of one KTX train set was first calculated as 10,059,600 KRW based on 998 standard seats and 127 special seats. Subsequently, the daily number of KTX operations based on a specific date was compiled for each station in both upward and downward directions, and this was multiplied by the monetary value per train set to calculate the average daily train transportation revenue (KRW/day) for each inter-station segment. The matrix for the passenger transport economic value of High-Speed Line 1 is shown in Table 7 . Additionally, to calculate freight transportation revenue on Conventional Line 2 and Conventional Line 3, a unit price of 45.9 KRW per km was applied based on freight fare information from KORAIL (2017) 28 . A matrix for transportation revenue (KRW/day) was calculated by reflecting the actual travel distance for each segment, and the transportation revenue matrix for Conventional Line 2 is shown in Table 8 . Through this, the passenger and freight transportation volume and transportation revenue under normal operating conditions before the earthquake were quantitatively derived from the perspective of the simulated railway network. Table 3 Number of passenger movements by station on the High-Speed Line 1, data: KORAIL (2024) 26 Osong Gongju Iksan Jeongeup Gwangju Songjeong Boarding 385,721 12,325 205,535 42,236 218,274 Alighting 377,066 12,161 203,501 43,050 222,625 Table 4 Number of freight movements by station on the Conventional Line 2, data: KOTI (2020) 27 Osong Daejeon Dongiksan Dongsan Generation 194,384 28,213 133,308 74,728 Attraction 223,828 56,395 93,839 65,629 Table 5 Passenger transportation volume of High-Speed Line 1 (before the earthquake) Alighting Boarding Osong Gongju Iksan Jeongeup Gwangju Songjeong Osong - 9,745 163,076 34,498 178,401 Gongju 5,492 - 2,964 627 3,242 Iksan 118,339 3,817 - 13,511 69,869 Jeongeup 19,532 630 10,542 - 11,532 Gwangju Songjeong 129,454 4,175 69,866 14,780 - Table 6 Freight transportation volume of Conventional Line 2 (before the earthquake) Generation Attraction Osong Daejeon Dongiksan Dongsan Osong - 44,914 74,735 74,735 Daejeon 15,346 - 6,434 6,434 Dongiksan 79,768 20,098 - 33,442 Dongsan 79,768 20,098 33,442 - Table 7 Passenger transportation revenue of High-Speed Line 1 (before the earthquake) Alighting Boarding Osong Gongju Iksan Jeongeup Gwangju Songjeong Osong - 140,834,400 352,086,000 533,158,800 714,231,600 Gongju 100,596,000 - 211,251,600 392,324,400 573,397,200 Iksan 251,490,000 150,894,000 - 181,072,800 36,2145,600 Jeongeup 422,503,200 321,907,200 171,013,200 - 181,072,800 Gwangju Songjeong 593,516,400 492,920,400 342,026,400 171,013,200 - Table 8 Freight transportation revenue of Conventional Line 2 (before the earthquake) Generation Attraction Osong Daejeon Dongiksan Dongsan Osong - 76,071,931 433,937,076 482,647,799 Daejeon 25,991,531 - 26,459,542 30,652,906 Dongiksan 463,158,058 82,656,567 - 21,796,957 Dongsan 515,148,923 95,756,157 21,796,957 - Seismic Functionality Loss Prediction of Railway Network Functionality Loss for Each Structural Component In this study, seismic fragility curve data from Kim et al. (2025) 29 were utilized to derive fragility curves for railway bridges. The aforementioned study constructed analytical models based on representative PSC box cross-sections extensively applied to Korean high-speed and conventional railway systems. Furthermore, as it incorporates the Korean Peninsula's ground conditions, material properties, damping ratios, and short-period seismic characteristics, the data were deemed suitable for deriving functionality loss curves in the present study. For embankments, data from Argyroudis and Kaynia (2015) 30 were employed. Given the limited availability of actual seismic damage data for domestic railway embankment structures and the difficulty in developing empirical fragility curves, this study presents a systematic fragility assessment methodology based on 2D numerical analysis that considers Eurocode 8 ground classification (Soil types C and D) and various embankment heights (2m, 4m, 6m), making it appropriate for deriving functionality loss curves in this research. For tunnels, data from Kwon et al. (2024) 31 were utilized. This study performed dynamic analyses based on representative cross-sections of domestic underground stations, with ground conditions, bedrock depth, and burial depth as parameters, and applied input ground motions reflecting the seismic characteristics of the Korean Peninsula, making it suitable for application in the present study. The restoration curve data utilized the data for railway track, railway bridge, and railway tunnel provided by FEMA (2024) 19 . The provided restoration curve data are standard models constructed based on engineering characteristics common across nations, transcending structural damage types and restoration procedures, and are sufficiently valid for application to Korean railway infrastructure. Railway structures on the Korean Peninsula are substantially identical to the structural types assumed by FEMA (2024) 19 , such as concrete tracks, PSC box girder bridges, and concrete lining tunnels, and the damage mechanisms and restoration methods due to earthquakes also have international consistency. Particularly, since actual earthquake damage and restoration cases on the Korean Peninsula are very limited, utilizing verified international standard restoration statistics like FEMA (2024) 19 is appropriate for rationally simulating the time-functionality recovery of structures. For these reasons, this study constructed restoration functions for each component of the railway network based on the restoration curve data provided by FEMA (2024) 19 . The seismic fragility curves and restoration curves for bridges derived based on these previous studies are shown in Fig. 7 . The derived seismic fragility curve and restoration curve data for each component were applied to Eq. ( 5 ) through Eq. (8) to derive functionality curves and functionality loss curves. The functionality curve and functionality loss curve for bridges are shown in Fig. 8 . Functionality Loss for Railway Network Through this approach, this study calculated the functionality loss of each structure component comprising the railway network. Subsequently, by comparing these across sections of each line, the maximum value among the functionality losses of components appearing in a specific section was defined as the representative functionality loss for that section. Defining the representative functionality loss for each section as the maximum value is based on the operational structure of railway infrastructure, which has series system characteristics of railway networks. The operational capacity of railway networks is governed by the performance of the most vulnerable element among individual structural components, and significant functionality degradation of a single component directly causes operational constraints such as speed restrictions, single-track operations, or service suspension for the entire section. For this reason, this study adopted the maximum value among functionality losses of components as the representative functionality loss of sections to conservatively evaluate the functionality degradation of railway networks after earthquakes. Additionally, this study selected a restoration evaluation period of 100 days to identify functionality loss for each component. This is to reflect the characteristic that functionality degradation of structures after earthquakes continuously affects over a certain period beyond the short-term emergency restoration stage. In fact, seismic vulnerability studies and infrastructure resilience analyses report that even if initial emergency measures are completed within several days, structural repairs, inspections, and operational stabilization processes proceed over several months. Furthermore, FEMA (2024) 19 suggests that most railway components from slight damage to extensive damage recover their functionality within approximately 30 to 90 days, which can be utilized as an important reference value for setting restoration periods. Accordingly, this study set the restoration evaluation period to 100 days, extended beyond 90 days, to reflect these international standards and the mid- to long-term recovery characteristics of structures. Through this, the extent to which structures lost functionality during the 100 days after the earthquake can be intuitively confirmed. The calculated functionality loss rate (%) is utilized as an important criterion for evaluating the actual operational impact of structures and future restoration needs by clearly presenting the degree of performance degradation during the restoration evaluation period. Transportation Volume Loss and Transportation Revenue Loss Prediction for Railway Network Transportation Volume Loss for Railway Network As a result of estimating the economic damage for each line of the railway network, both High-Speed lines and Conventional lines showed significant reductions in transportation volume and transportation revenue during seismic events. The transportation volume loss for High-Speed Line 1 is shown in Table 9 , and the transportation volume loss for Conventional Line 2 is shown in Table 10 . For High-Speed Line 1, the passenger transportation volume (person/day) decreased from 864,091 to 345,107, representing a loss of 518,984 passengers (60.06%). For Conventional Line 2, which transports freight, the freight transportation volume (ton/day) decreased from 489,213 to 184,840 after the earthquake, representing a loss of 304,373 tons (62.22%). Similarly, for Conventional Line 3, the freight transportation volume (ton/day) decreased from 297,326 to 146,696 after the earthquake representing a loss of 150,629 tons (50.66%). Table 9 Passenger transportation volume loss of High-Speed Line 1 (after the earthquake) Alighting Boarding Osong Gongju Iksan Jeongeup Gwangju Songjeong Osong - 2,154 102,906 21,769 112,576 Gongju 1,214 - 1,870 396 2,046 Iksan 74,675 2,408 - 7,605 39,329 Jeongeup 12,325 398 5,934 - 3,388 Gwangju Songjeong 81,689 2,635 39,327 4,342 - SUM 518,984 [person/day] Table 10 Freight transportation volume loss of Conventional Line 2 (after the earthquake) Generation Attraction Osong Daejeon Dongiksan Dongsan Osong - 12,652 49,754 50,305 Daejeon 4,323 - 4,283 4,331 Dongiksan 53,104 13,380 - 22,510 Dongsan 53,693 13,528 22,510 - SUM 304,373 [ton/day] Transportation Revenue Loss for Railway Network Next, the transportation revenue loss for each line of railway network was calculated based on the derived transportation volume. For High-Speed line 1 which transports passengers, the passenger transportation revenue (KRW/day) decreased from 6.66 billion to 2.75 billion, resulting in a loss of approximately 3.9 billion (58.75%), as shown in Table 12 . For Conventional line 2 which transports freight, the freight transportation revenue (KRW/day) decreased from 2.27 billion to 0.79 billion after the earthquake, resulting in a loss of approximately 1.48 billion (65.23%). In addition, for Conventional line 3, the freight transportation revenue (KRW/day) decreased from 1.93 billion to half level at 0.76 billion after the earthquake, resulting in a loss of 1.17 billion (60.67%). The transportation revenue loss matrix for Conventional line 2 is shown in Table 12 , and based on the derived results, it was confirmed that the daily economic loss of High-Speed line responsible for passenger economic value was larger than Conventional Line transporting freight. Table 11 Passenger transportation revenue loss for High-Speed Line 1 (after the earthquake) Alighting Boarding Osong Gongju Iksan Jeongeup Gwangju Songjeong Osong - 31,128,750 222,175,950 336,437,867 450,699,784 Gongju 22,234,821 - 133,305,570 247,567,487 361,829,404 Iksan 158,697,107 95,218,264 - 101,924,444 203,848,889 Jeongeup 266,611,140 203,132,297 96,261,975 - 53,189,468 Gwangju Songjeong 374,525,173 311,046,330 192,523,950 50,234,498 - SUM 3,912,593,169 [KRW /day] Table 12 Freight transportation revenue loss for Conventional Line 2 (after the earthquake) Generation Attraction Osong Daejeon Dongiksan Dongsan Osong - 21,428,413 288,887,712 324,877,276 Daejeon 7,321,456 - 17,615,080 20,632,919 Dongiksan 308,341,185 55,027,486 - 14,671,850 Dongsan 346,754,257 64,454,866 14,671,850 - SUM 1,484,684,350 [KRW /day] Through this, the vulnerable sections and key bottleneck sections of the railway network can be clearly identified, and the possibility of serious disruption of national logistics network and passenger mobility in case of earthquake can be quantitatively confirmed. Also, by calculating the quantitative economic damage, it can be directly utilized for selection of recovery priority of specific sections and facilities, budget allocation, and feasibility of pre-reinforcement investment. Economic Loss for Railway Network Finally, based on the restoration evaluation period of 100 days set in this study, the economic loss caused by the degradation of passenger and freight transportation function of railway network after earthquake occurrence was quantitatively analyzed in terms of actual operation. In the case of Table 13 organized for High-Speed line 1, it was analyzed that if a daily loss of 3.9 billion KRW occurs, the cumulative loss of approximately 391.3 billion KRW occurs during the restoration evaluation period of 100 days. In the case of Table 14 organized for Conventional line 2, if a daily loss of 1.48 billion KRW occurs, the cumulative loss of approximately 148.5 billion KRW occurred during 100 days. Table 14 organized the freight transportation volume and transportation revenue for Conventional line 3, and it was analyzed that if a loss of 1.17 billion KRW per day occurs, the cumulative loss of approximately 117.4 billion KRW occurs during the restoration evaluation period of 100 days. Comparing only the transportation revenue loss, it was found that the passenger transportation revenue loss was approximately 2.5 to 3 times larger than the freight transportation revenue loss. However, considering that in the case of population, the existence of detour routes and utilization of alternative transportation methods are relatively easy when earthquake occurs, the actual economic ripple effect needs to be evaluated more importantly for the transportation revenue loss of freight. In other words, because freight transportation has low substitutability and delays and interruptions lead to direct economic damage to the overall industry, it is reasonable to focus on the impact of freight sector in the economic loss analysis of railway network during earthquake. These results show that the functional degradation due to earthquake in long-distance railway network sections responsible for large-scale freight transportation can have a significant impact on the national logistics system and socio-economically. Table 13 Passenger transportation volume loss of High-Speed Line 1 and 100-Day economic loss Passenger Transportation Volume (Before Earthquake) 864,091 Passenger Transportation Volume (After Earthquake) 345,107 Passenger Transportation Volume Loss Due to the Earthquake 518,984 (Loss Ratio: 60.06%) Table 14 Freight transportation volume loss of Conventional Line 2 and 100-Day economic loss Passenger Transportation revenue (Before Earthquake) 6,659,455,200 Passenger Transportation revenue (After Earthquake) 2,746,862,031 Passenger Transportation revenue Loss Due to the Earthquake 3,912,593,169 (Loss Ratio: 58.75%) 100-Day Economic Loss [KRW] 391,259,316,927 Freight Transportation Volume (Before Earthquake) 489,213 Freight Transportation Volume (After Earthquake) 184,840 Freight Transportation Volume Loss Due to the Earthquake 304,373 (Loss Ratio: 62.22%) Table 15 Freight transportation volume loss of Conventional Line 3 and 100-Day economic loss Freight Transportation revenue (Before Earthquake) 2,276,074,402 Freight Transportation revenue (After Earthquake) 791,390,052 Freight Transportation revenue Loss Due to the Earthquake 1,484,684,350 (Loss Ratio: 65.23%) 100-Day Economic Loss [KRW] 148,468,434,953 Freight Transportation Volume (Before Earthquake) 297,326 Freight Transportation Volume (After Earthquake) 146,696 Freight Transportation Volume Loss Due to the Earthquake 150,629 (Loss Ratio: 50.66%) Freight Transportation revenue (Before Earthquake) 1,935,674,138 Freight Transportation revenue (After Earthquake) 761,328,274 Freight Transportation revenue Loss Due to the Earthquake 1,174,345,864 (Loss Ratio: 60.67%) 100-Day Economic Loss [KRW] 117,434,586,354 Discussion This study quantitatively confirmed the process by which damage to railway structure components due to earthquakes propagates into functional degradation across the entire network, leading to transportation volume loss and economic loss of passengers and freight. To this end, seismic functionality loss curves were constructed by integrating seismic fragility curves and restoration curves, and by linking these with OD matrix-based transportation volume and transportation revenue calculations, the process by which damage to individual structures leads to economic loss was systematically presented. The methodology proposed in this study was applied to a simulated railway network based on the existing Korean Peninsula railway system to derive socioeconomic losses for High-Speed lines and Conventional lines. For this purpose, an earthquake scenario with a magnitude of 6, similar to the Gyeongju earthquake, was established, and the focal depth was set at 7 km, similar to the Pohang earthquake. In addition, to reflect the ground characteristics of the Korean Peninsula, Korean attenuation equations and short-period amplification factors were applied to quantitatively confirm the functionality loss of individual structures in the railway network. Additionally, to reflect the serial system characteristics of railway networks, the representative functionality loss for each segment was defined as the maximum functionality loss value among the structure components included in that segment. Although railways consist of various structures such as bridges, embankments, tunnels, and cuts arranged consecutively within a given section, the actual operability of trains is determined by the most vulnerable element among them. When significant functional degradation occurs in any single bridge or tunnel, strong operational restriction measures such as speed limits, reduced train formations, single-track operation, or service suspension become unavoidable across the entire segment to ensure structural stability. From this perspective, adopting the maximum value as the representative functionality loss for the segment can be interpreted as a conservative approach to project the damage occurring in a single structure to the actual level of operational disruption. The high transportation volume loss rate shown in the analysis results clearly demonstrates the structural vulnerability where such bottleneck structures determine the operational capacity of the entire network. Next, the decision to set the restoration evaluation period at 100 days also holds important interpretive significance. The impacts following an earthquake do not simply dissipate during the emergency recovery phase of a few days immediately after occurrence, but often persist over several months as they extend into structural repairs, detailed inspections, equipment replacement, and operational stabilization. While FEMA (2024) 19 suggests recovery periods ranging from approximately 30 to 90 days depending on the damage level of structure components, this study set the evaluation period at 100 days, which encompasses this range while being slightly extended, in order to confirm the period during which structural functional degradation continues to affect actual operations even after short-term emergency measures. By accumulating the functionality loss rate and daily transportation revenue loss over the entire restoration evaluation period, it becomes possible to more realistically estimate the scale of long-term economic damage caused by a single earthquake. As a result, of applying the seismic fragility-based railway network economic loss assessment methodology proposed in this study, functional degradation of railway networks following earthquakes was found to lead to substantial economic damage. Over a 100-day restoration period, cumulative losses were estimated at KRW 391.3 billion for High-Speed Line 1 passenger transport, KRW 148.5 billion for Conventional Line 2 freight, and KRW 117.4 billion for Conventional Line 3 freight. Loss rates reached 50–62% for transportation volume and 59–65% for transportation revenue. In terms of monetary value, it was found that the economic loss of passenger economic value was approximately 2.5-3 times larger compared to freight transportation loss. However, these results need to be interpreted considering the substitutability characteristics of each transportation type. In the case of passenger transportation lines, even if railway service in a specific section is limited due to earthquake, there is room for loss mitigation as demand is dispersed to alternative transportation methods such as roads, buses, and aviation. On the other hand, freight transportation has remarkably low substitutability due to the characteristics of mass transportation. In addition, delays and interruptions of freight economic value cause chain economic ripple effects across the overall industry such as manufacturing and distribution industries. Therefore, in terms of practical economic impact, the loss of freight transportation should be evaluated more importantly. The results of this study quantitatively demonstrate that the functional degradation of railway network sections responsible for long-distance freight economic value in case of earthquake can have serious impact on the national logistics system and overall socio-economic system. Through the proposed methodology, vulnerable sections and key bottleneck sections on the network can be clearly identified, and it can be directly utilized for establishing earthquake disaster response strategies. First, by quantifying the transportation volume loss rate and economic loss scale of each line under earthquake scenario, vulnerable sections within the network can be clearly identified. Even under the same seismic intensity, the magnitude of economic loss suffered by passenger transportation High-Speed line, short-distance freight transportation conventional line, and long-distance freight transportation conventional line are different, and by evaluating this quantitatively, the sections where restoration budget, manpower, and equipment should be prioritized can be objectively selected. Second, by quantifying the relationship between functional loss and economic loss of railway network, it provides a cost-benefit analysis basis for seismic reinforcement investment for specific structures (bridges, embankments, tunnels). For example, if a specific bridge on long-distance conventional line is confirmed to cause a significant portion of the entire network loss, the investment feasibility can be evaluated by comparing the seismic reinforcement cost of the bridge with the 100-day cumulative economic loss expected to be reduced through reinforcement. Third, for restoration period reduction policies, the degree to which restoration speed improvement leads to actual economic loss reduction can be quantitatively estimated, so it can also be utilized for efficiency evaluation of restoration strategies. As a result, the quantitative evaluation results of this study are expected to be utilized as scientific evidence in the policy decision-making process for establishing earthquake disaster response strategies such as selection of restoration priority, efficient allocation of limited budget, and feasibility evaluation of pre-seismic reinforcement investment. This study has limitations that it was conducted based on specific earthquake scenario, network structure, and existing demand data. In the case of actual earthquake occurrence, the economic loss can vary depending on earthquake magnitude, epicenter location, ground conditions, structural deterioration, etc. Nevertheless, this study has significance in that it quantified the economic loss of railway network after earthquake occurrence and presented a systematic methodology for restoration priority decision-making and vulnerable section identification. In future research, we plan to establish a comprehensive and robust economic evaluation system for railway network by considering various earthquake scenarios and ground conditions more precisely. Declarations Competing interests The authors declare no competing interests. Funding This research was supported by a grant from R&D Program (PK2502A2) of the Korea Railroad Research Institute. Author Contribution J.J.: writing–original draft, Methodology, J.H.K.: writing-original draft, conceptualization, simulation, M.Y.: writing-review and editing, administration, Methodology. The authors confirm that this work has not been published before, and its publication has been approved by all co-authors. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data sources, collection methods, and processing techniques are summarized below.Data Sources and Collection: This study utilized data provided by KORAIL and KOTI, seismic fragility curve data obtained from previous studies, and restoration curve data provided by FEMA.Data Types: The data provided by KORAIL include station-specific passenger boarding and alighting volumes, as well as tariff rates based on freight tonnage and transport distance (ton-km). The data provided by KOTI present regional freight movement volumes. The data obtained from previous studies and FEMA were used to derive seismic fragility curves and restoration curves appropriate for conditions on the Korean Peninsula.Data Processing and Analysis: To estimate the transportation volume at each station, an OD matrix was constructed using station-specific boarding and alighting data, in which the passenger flow at each station was interpreted as the station’s transportation volume. The seismic fragility curve and restoration curve were derived by applying standard normal distribution functions based on data from previous studies and FEMA. These two curves were then integrated to develop a seismic functionality loss curve, which was combined with the transportation volume matrix or the transportation revenue matrix. Through this process, the overall economic loss of the railway network was evaluated.Data Repository: The data was stored in a local repository within our research laboratory for the duration of the study.Data Access and Usage Conditions: Access to the data and the terms of its utilization are outlined as follows. The data's accessibility for use, whether open to everyone or subject to limitations, is specified.Contact Information: For inquiries, please contact [email protected] . References Kim, K. H. et al. The 2017 ML 5.4 Pohang earthquake sequence, Korea, recorded by a dense seismic network. Tectonophysics 774 , 228306 (2020). Kim, Y. et al. The 12 September 2016 Gyeongju earthquakes: 1. Observation and remaining questions. Geosci. J. 20 (6), 747–752 (2016). Cui, Q., Nakamura, H., Mizui, Y. & Fujiwara, H. Estimation of Direct Damage Caused by the Nankai Trough Earthquake Considering Hazard and Social Characteristics. J. Disaster Res. 19 (1), 192–203 (2024). 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Y., Kim, J., Kwak, D., Yang, S. & Yoo, M. Development of seismic fragility function for underground railway station structures in Korea. Buildings 14 (5), 1200 (2024). 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-8373543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586580833,"identity":"516ec3d7-3123-4f08-a8e6-8e7ae89c43e9","order_by":0,"name":"Jiyun Jeon","email":"","orcid":"","institution":"Gachon University","correspondingAuthor":false,"prefix":"","firstName":"Jiyun","middleName":"","lastName":"Jeon","suffix":""},{"id":586580834,"identity":"6d6619c3-c70f-4461-8319-a0fbbc2b2864","order_by":1,"name":"Ji Hyeon Kim","email":"","orcid":"","institution":"Korea Railroad Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Hyeon","lastName":"Kim","suffix":""},{"id":586580835,"identity":"f2257306-d29f-4563-a4d7-accb1ab7ca41","order_by":2,"name":"Mintaek Yoo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZjCZAGIdABIHoMIHsKmFa2FsgGhhSyBSCwNcC48BcVp023mPP/hRkZbYz97z+cWPmjty5gzMDz8wnLmHU4vZYb7Exp4zOYkze85us+w59szYsoHNWILhRjEeLTyGDbxtFYkbbuRuM+BtOJy44QCDGQPDhwS8Whr//gNpyXlm+Beshf0bQS3NvA05IC3MjyG28ABtuYFfy2yZY2nGM3uOmTHLHDtsbHCYp1gi4QweLefPGHx8U5Ms28/e/BjIOCxncLx944cPx3BrgQHHBmBcSoCZoPRAWAMDgz1I7QciFI6CUTAKRsEIBABASmCz249SBwAAAABJRU5ErkJggg==","orcid":"","institution":"Gachon University","correspondingAuthor":true,"prefix":"","firstName":"Mintaek","middleName":"","lastName":"Yoo","suffix":""}],"badges":[],"createdAt":"2025-12-16 08:08:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8373543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8373543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102042748,"identity":"802473d4-31e5-4dcc-8b0f-3d2a8936564b","added_by":"auto","created_at":"2026-02-06 13:16:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95499,"visible":true,"origin":"","legend":"\u003cp\u003eFragility curve for cut \u0026amp; cover tunnels subject, data: FEMA (2024)\u003csup\u003e 19\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/0656dce414951ca387f4eca6.jpeg"},{"id":102295499,"identity":"5ca4cd26-57a8-469e-86a2-6bea11c5e53b","added_by":"auto","created_at":"2026-02-10 10:11:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122594,"visible":true,"origin":"","legend":"\u003cp\u003eRestoration curve for highway bridges, data: FEMA (2024)\u003csup\u003e 19\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/07f4d822ee60c3a26d665424.jpeg"},{"id":102295547,"identity":"eee6756d-d87d-4c7b-84a9-34e3d456d50d","added_by":"auto","created_at":"2026-02-10 10:12:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159389,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical framework for transportation volume loss prediction\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/2a8ad2aae49652c66edf3874.jpeg"},{"id":102042749,"identity":"c89c564f-7629-4127-b640-c36ad76cb85b","added_by":"auto","created_at":"2026-02-06 13:16:50","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":247519,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure for deriving the seismic functionality loss curve\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/dc5d53de54c2ac79a2cadff1.jpeg"},{"id":102042754,"identity":"0e43eb28-b6d9-4210-b7b7-5ba19e90dca6","added_by":"auto","created_at":"2026-02-06 13:16:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75136,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic drawing of seismic functionality loss, when PGA=0.4g, restoration evaluation period: 100 days\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/cc5b9ae1a5e111f038151861.png"},{"id":102295593,"identity":"277623af-d5b6-4322-b1da-9261fb269514","added_by":"auto","created_at":"2026-02-10 10:12:58","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":154543,"visible":true,"origin":"","legend":"\u003cp\u003eRailway network for an earthquake scenario\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/1b8fd29db3aa07224efaed82.jpeg"},{"id":102042752,"identity":"3c6f5f0b-2769-4f46-8d39-9dfc4db0e067","added_by":"auto","created_at":"2026-02-06 13:16:50","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":240397,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic fragility curve and restoration curve for bridge\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/731b1f79de578a89e9170a29.jpeg"},{"id":102042755,"identity":"842c7b3a-ee32-4524-ade4-a2b90d0dc8d7","added_by":"auto","created_at":"2026-02-06 13:16:50","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":251408,"visible":true,"origin":"","legend":"\u003cp\u003eSeismic functionality curve and seismic functionality loss curve for bridge\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/6e64fbb3e5fbd1e46475876c.jpeg"},{"id":105032557,"identity":"24c64e3d-3255-4773-8e85-ad3668e0fafa","added_by":"auto","created_at":"2026-03-20 07:01:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2688830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8373543/v1/96e8bb15-c73b-42e6-a696-9a9013c4afc1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Methodology Framework for Predicting Economic Loss in Korean Peninsula Railway Network","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Korean Peninsula has been shown to no longer be a seismically safe region, as evidenced by large-magnitude earthquakes such as the 2017 Pohang earthquake (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{L}\\)\u003c/span\u003e\u003c/span\u003e 5.4) and the 2016 Gyeongju earthquake (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{L}\\)\u003c/span\u003e\u003c/span\u003e 5.8) (Kim et al. (2020) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and Kim et al. (2016) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). In addition, with the recurrence interval of the Nankai earthquake approaching, its potential impacts on the Korean Peninsula cannot be ruled out, emphasizing the need for establishing appropriate preparedness strategies. Cui et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e reported that the maximum direct losses associated with the Nankai earthquake are estimated to reach approximately 167.8 trillion (JPY), representing an astronomically large scale of damage. These losses were found to be concentrated in highly urbanized and industrialized regions such as Tokyo, Osaka, and Shizuoka, indicating the potential for not only substantial economic impacts but also significant human casualties. Accordingly, the necessity of preparing in advance for potential economic losses arising from large-scale disasters such as earthquakes is further emphasized. In particular, railway networks function as a critical axis for population movement and industrial logistics, and due to their concentrated distribution along major industrial and urban areas, they are likely to cause widespread and significant impacts in the event of a disaster.\u003c/p\u003e \u003cp\u003eRailway networks are systems in which multiple structural components are connected in series, characterized by the propagation of damage to a single structural element during seismic events into functional degradation of the entire network. However, existing seismic evaluation frameworks focus on performance verification of individual structures, and research on economic damage prediction at the network level is limited. In the initial response phase following an earthquake, recovery resources (equipment, personnel, and budget) are distributed across multiple damage points; therefore, determining restoration priorities based on economic loss and establishing reinforcement plans for vulnerable structures are essential for recovering network functionality. Moreover, since functionality loss of networks results in economic losses such as reduced transportation volume and logistics delays, the development of predictive models capable of quantifying these losses in advance is required. In this context, this study proposes an analytical framework for quantitatively estimating the economic loss of railway networks following earthquakes and establishing restoration strategies and reinforcement plans based on these estimations.\u003c/p\u003e \u003cp\u003eRegarding previous research utilizing OD matrix, Basso et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e proposed a methodology for estimating comprehensive freight vehicle OD matrices through a multi-data-based approach that integrates freight vehicle traffic data from urban highways with GPS samples. Ceccato et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e integrated Floating Car Data (FCD) with traditional traffic data through various methods to investigate the correlation between OD matrix accuracy and data collection costs, and proposed an optimal cost-effective data combination. Additionally, Galliani et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e combined ticket sales data with Automatic Passenger Counting (APC) data and applied the IPF algorithm to present a dynamic OD matrix estimation method that can be updated weekly in real railway networks.\u003c/p\u003e \u003cp\u003eMeanwhile, regarding previous component-level studies on bridges, embankments, and tunnels comprising railway networks, Bakhtiari et al. (2020) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e conducted IDA incorporating nonlinear dynamic analysis and SSI for high-speed railway bridges and presented seismic fragility curves according to pier height variations. Crespi et al. (2025) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e developed seismic fragility curves for major damage modes targeting 1,182 highway bridges in Italy, and Annad et al. (2023) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e improved the assessment accuracy through vulnerability analysis of shallow foundation bridges considering combined hazards of scour and earthquake and fuzzy-based damage classification. Additionally, Nielson et al. (2007) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e proposed a component-level approach that assesses the seismic fragility of entire bridges by integrating the damage probabilities of individual components comprising the bridges. Regarding previous studies on embankments, Shinoda et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e practically evaluated the fragility of Japanese railway embankments using three seismic intensity measures: PGA, PGV, and Arias Intensity. Hübner et al. (2020) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e analyzed the correlation between embankment displacement and seismic intensity measures through 1D and 2D ground response analyses, and based on this, presented lognormal seismic fragility functions. Mohammadi et al. (2023) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e also evaluated the settlement and deformation behavior of railway embankments through probabilistic dynamic analysis applying nonlinear soil model and multiple ground motion records, and developed fragility curves for each damage state. Petala et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e numerically simulated the effects of fault rupture on embankments and evaluated the seismic fragility of highway embankments during fault rupture propagation. Regarding previous studies on tunnels, Yang et al. (2023) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e compared and classified existing fragility models for bored tunnels according to burial depth and ground conditions, and reconstructed a weighted combined representative fragility function based on this. Huang et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e proposed a time-dependent seismic fragility function for circular tunnels embedded in soft ground, reflecting long-term deterioration and time-dependency of soil-tunnel interaction. Liu et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e conducted nonlinear numerical analysis for circular tunnels in saturated sandy soil and presented fragility curves that quantified damage probabilities under various seismic loading conditions, and Qi et al. (2026) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e evaluated the seismic response of shield tunnel segments and developed seismic fragility curves based on the probability of damage occurrence.\u003c/p\u003e \u003cp\u003eThe HAZUS guidelines by the U.S. Federal Emergency Management Agency FEMA (2024) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e also present an assessment framework centered on seismic fragility curves and recovery curves for individual infrastructure components such as bridges, embankments, and tunnels, but do not address the changes in functionality across the entire railway network or the evaluation of socioeconomic impacts during earthquakes. Most existing studies have also focused on developing fragility curves at the individual component level comprising railway networks and analyzing damage and functionality degradation characteristics. Yoo et al. (2025) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e proposed a model that calculates component functionality loss by combining seismic fragility curves and recovery curves, and evaluates the functionality degradation of railway networks based on this, but quantitative analysis of railway network transportation revenue loss has still not been sufficiently conducted.\u003c/p\u003e \u003cp\u003eAccordingly, this study aims to quantitatively predict transportation revenue losses in railway networks following earthquake events. Based on these predictions, a foundational framework is established for rational restoration prioritization and seismic retrofitting strategies for vulnerable structural components. To achieve this, seismic functionality loss curves are derived by combining seismic fragility curves and restoration curves. These curves are then linked with OD matrix-based population and freight transportation volumes and revenues to present a consistent analytical framework that traces the damage propagation process from functionality loss of individual structural components to overall functionality loss of the railway network, ultimately resulting in transportation revenue loss. Furthermore, the proposed methodology is applied to a simulated railway network constructed based on the Korean Peninsula railway system to estimate transportation revenue loss for High-Speed lines and Conventional lines. By identifying vulnerable segments and critical bottleneck sections based on a restoration evaluation period of 100 days, this study proposes an economic loss prediction methodology that quantitatively supports future restoration strategies and pre-emptive reinforcement investment directions.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eProcess of Economic Loss Prediction\u003c/p\u003e\u003cp\u003eTo derive the economic damage of railway networks, it is necessary to develop a model that can quantitatively calculate transportation volume loss. First, the transportation volume of passengers and freight for each station in the railway network must be identified, and the functionality loss of structure components during seismic events must be quantitatively assessed. In this study, the OD Matrix, a data analysis matrix, was utilized to determine the transportation volume for each station, and seismic functionality loss curves were employed to assess the functionality loss of structure components. The process of deriving the economic loss of railway networks is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eTransportation Volume and Transportation Revenue Prediction\u003c/p\u003e\u003cp\u003eThis study sequentially applied proportional distribution, proportional renormalization, and the Fratar method to estimate the passenger and freight transportation volume of the railway network. The proportional distribution method constructs the initial OD Matrix based on the total production and attraction of origins and destinations, while the proportional renormalization process compensates for total volume inconsistencies and cumulative errors that may occur during the transportation volume distribution process. The Fratar method iteratively satisfies the total volume constraints of each origin and destination while maintaining the distribution pattern of the existing OD Matrix, thereby ensuring consistency with actual data. The formulas for the proportional distribution and proportional renormalization processes are shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e3\u003c/span\u003e)-(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{OD}_{ij}=\\:\\frac{Origi{n}_{i}\\:\\times\\:\\:Destinatio{n}_{j}}{\\sum\\:Destination}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{OD}_{ij}^{{\\prime\\:}}={OD}_{ij}\\times\\:\\:\\frac{Origi{n}_{i}\\:}{{\\sum\\:}_{i}O{D}_{ij}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eIn addition, by utilizing the transportation revenue calculated by combining passenger count and freight transportation volume with passenger fares or freight rates, the proportion of passenger and freight movement before the earthquake can be identified, and the economic value can also be determined.\u003c/p\u003e\u003cp\u003eSeismic Functionality Loss Prediction for Structure Component\u003c/p\u003e\u003cp\u003eTo quantitatively derive the economic damage to railway networks, the development of seismic functionality loss curves is essential, and these curves can be derived by combining seismic fragility curves and restoration curves. This study derived seismic functionality loss curves based on the formulas presented by Huang et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to obtain functionality loss values for each component of the railway network. The process of deriving seismic functionality loss curves is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eTo derive the seismic functionality loss curve, deriving the functionality curve is necessary, and the equation for deriving this curve is shown in Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Through the functionality curve, the functionality of a structure over time for a specific seismic intensity can be probabilistically evaluated, and it is constructed in a manner that comprehensively considers the functionality recovery characteristics for each damage state and the probability of damage occurrence. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q\\left[{ds}_{i}\\right|t]\\)\u003c/span\u003e\u003c/span\u003e represents the level of functionality that the structure has recovered at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e after the earthquake for each damage state. This is calculated based on the restoration curve, which expresses the restoration process of the structure as a time-functionality curve, and is derived by reflecting the average restoration period, restoration rate, and restoration speed for each damage state.\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:Q\\left(t\\right)=\\:\\sum\\:_{i=0}^{4}Q\\left[d{s}_{i}|t\\right]P\\left[d{s}_{i}|IM\\right]$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eNext, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left[{ds}_{i}\\right|IM]\\)\u003c/span\u003e\u003c/span\u003e represents the probability that the structure will experience damage state given a specific seismic intensity (IM), and is calculated using the formulas for each damage state of the seismic fragility curve as shown in Eq.\u0026nbsp;(6)-(8). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left[{ds}_{i}\\right|IM]\\)\u003c/span\u003e\u003c/span\u003e derived through these formulas quantitatively expresses the damage level that may occur in the structure as seismic intensity varies, and allows the likelihood of each damage state to be identified.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left[{ds}_{i}|IM\\right]=1-P\\left[ds\u0026gt;d{s}_{i+1}|IM\\right],\\)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:when\\:i=0\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6)\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\\(\\:P\\left[{ds}_{j}|IM\\right]=P\\left[ds\u0026gt;{ds}_{j}|IM\\right]-P\\left[ds\u0026gt;{ds}_{j+1}|IM\\right],\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:when\\:j=1,\\:2,\\:3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\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\\(\\:P\\left[{ds}_{k}|IM\\right]=P\\left[ds\u0026gt;{ds}_{k}|IM\\right],\\)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:when\\:k=4\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing the seismic functionality curve \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e defined above, the functionality loss from the time of earthquake occurrence (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{0}\\)\u003c/span\u003e\u003c/span\u003e) to the restoration evaluation period (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{f}\\)\u003c/span\u003e\u003c/span\u003e) can be derived for each seismic intensity using Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In other words, it is a concept that calculates how much functionality the structure maintained on average during the restoration evaluation period (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{f}\\)\u003c/span\u003e\u003c/span\u003e) at a specific seismic intensity (IM), and considers the shortfall as functionality loss.\u003c/p\u003e\u003cp\u003eFor example, given a specific structure's functionality curve as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, if the structure's functionality during the restoration evaluation period of 100 days appears to be approximately 0.7 under an earthquake scenario with PGA of 0.4g, the functionality loss becomes 0.3. This means that the structure operated with approximately 30% functionality loss for 100 days after the earthquake, and the shaded area in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e corresponds to the functionality loss.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:Functionality\\:Loss\\:\\left(L\\right)=1-\\frac{{\\int\\:}_{{t}_{0}}^{{t}_{f}}Q\\left(t\\right)}{{t}_{f}-{t}_{0}\\:}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhen PGA = 0.4g, restoration evaluation period: 100 days\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Schematic drawing of seismic functionality loss,\u003c/p\u003e\u003cp\u003eAfter evaluating the functionality loss of individual structure components using this method, the overall functionality loss of the railway network is estimated by assigning, to each line segment, the maximum functionality loss among the structure components composing that segment.\u003c/p\u003e\u003cp\u003eTransportation Volume Loss and Transportation Revenue Loss Prediction\u003c/p\u003e\u003cp\u003eTo evaluate transportation volume loss in railway networks during seismic events, the baseline transportation volume of the entire network is first calculated using the OD Matrix, boarding and alighting data for each station, and generation-attraction data. The calculated transportation volume is then combined with the functionality loss of each structure component based on functionality loss curves, ultimately deriving the transportation volume loss of the railway network. This enables quantitative assessment of how the performance degradation of individual structures affects the overall transport capacity of the railway network.\u003c/p\u003e\u003cp\u003eIn addition, by linking passenger fares and freight rates to passenger and freight transportation data and utilizing the OD Matrix along with the calculated economic value, a transport revenue matrix for the network can be constructed. Economic loss can also be combined in the same manner to predict transportation revenue loss caused by earthquakes, enabling comprehensive evaluation of how structural damage to the railway network impacts not only transportation volume but also economic value.\u003c/p\u003e\u003cp\u003eThe transportation volume and economic loss prediction procedure proposed in this study can serve as essential foundational data for quantitatively assessing the economic damage caused by functional degradation of railway networks due to earthquakes. Through the derived transportation volume loss and transportation revenue loss, the actual economic impact scale of service disruptions and functional degradation on society during seismic events can be identified, providing crucial evidence for determining restoration priorities, establishing emergency response strategies, and estimating restoration costs. Economic losses include passenger and freight delay costs, revenue losses due to transport unavailability, alternative operation costs, and restoration costs.\u003c/p\u003e\u003cp\u003eFurthermore, by analyzing the relationship between functionality loss and transportation volume loss for each line, the impact of specific structure damage on the overall network operational efficiency can be quantitatively evaluated. This enables vulnerability assessment from a network-wide perspective beyond the individual structure level, making it possible to establish efficient disaster response and restoration strategies that consider systemic resilience.\u003c/p\u003e\u003cp\u003eIn conclusion, this study establishes an integrated evaluation framework linking functionality degradation–transportation volume loss–economic loss of railway networks, thereby providing practical and quantitative decision-making criteria for policy decisions such as seismic reinforcement investments and restoration planning.\u003c/p\u003e"},{"header":"Related works","content":"\u003cp\u003eOD Matrix\u003c/p\u003e\u003cp\u003eThe OD (Origin-Destination) Matrix is a data analysis matrix for quantifying spatial travel demand within transportation networks, representing the number of passengers and freight volume traveling from origin zone \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e to destination zone \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e. Each matrix element \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the travel volume between Origin and Destination, and the OD Matrix is utilized as key input data for trip distribution, capacity assessment, and system performance analysis under normal and bridge conditions by comprehensively describing the movement patterns of railway network transportation volume.\u003c/p\u003e\u003cp\u003eAdditionally, the Fratar method of proportional fitting was employed to iteratively calibrate transportation volumes between each Origin-Destination pair in the transportation network. In the Fratar method, the total production of each origin zone and the total attraction of each destination zone are defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{O}_{i}={\\sum\\:}_{j}{T}_{ij}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{j}={\\sum\\:}_{i}{T}_{ij}\\)\u003c/span\u003e\u003c/span\u003e, respectively, and must satisfy the conservation condition of total traffic flow \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i}{O}_{i}={\\sum\\:}_{j}{D}_{j}={\\sum\\:}_{i,j}{T}_{ij}\\)\u003c/span\u003e\u003c/span\u003e. In actual transportation networks, directional imbalances exist, so generally \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{ij}\\ne\\:{T}_{ji}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSeismic Fragility Curve\u003c/p\u003e\u003cp\u003eA seismic fragility curve is a statistical function that expresses the probability of a structure component reaching or exceeding a certain damage state at a specific seismic intensity level (e.g., PGA or Sa), and generally, fragility curves are standard cumulative distribution functions that express the exceedance probability as a logarithmic function of intensity.\u003c/p\u003e\u003cp\u003eThere are two main methods for deriving seismic fragility curves: the numerical method, which statistically calculates damage probabilities by applying ground motions to structures, and the empirical method, which calculates probabilities based on observed data from past earthquakes. In practice, when observational data are insufficient, numerical results or literature values are referenced to construct the final curves. Since this study applied the methodology to a simulated railway network, the numerical method was utilized to evaluate transportation revenue loss.\u003c/p\u003e\u003cp\u003eThe seismic fragility function is shown in Eq.\u0026nbsp;(1), and the seismic fragility curves provided in FEMA (2024) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left[{ds\\ge\\:ds}_{i}|IM\\right]\\)\u003c/span\u003e\u003c/span\u003e represents the probability of exceeding a given damage state for a specific seismic intensity measure (IM), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varPhi\\:(\\cdot\\:)\\)\u003c/span\u003e\u003c/span\u003e denotes the cumulative distribution function of the standard normal distribution. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e represent the mean and standard deviation of the natural logarithm of the intensity measure, respectively, and the damage states of structure components are classified into five levels: none (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ds}_{0}\\)\u003c/span\u003e\u003c/span\u003e​), minor (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ds}_{1}\\)\u003c/span\u003e\u003c/span\u003e​), moderate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ds}_{2}\\)\u003c/span\u003e\u003c/span\u003e​), extensive (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ds}_{3}\\)\u003c/span\u003e\u003c/span\u003e​), and complete (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ds}_{4}\\)\u003c/span\u003e\u003c/span\u003e​).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\left[{ds\\ge\\:ds}_{i}|IM\\right]=\\:\\varPhi\\:\\left(\\frac{ln\\left(\\right(IM)-{\\mu\\:}_{i})}{{\\beta\\:}_{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eRestoration Curve\u003c/p\u003e\u003cp\u003eA restoration curve is a curve that represents how the functionality level of a component changes over time after an earthquake, allowing the identification of expected damage extent and expected restoration time caused by the earthquake. Additionally, it is utilized as a key indicator for quantitatively evaluating the time required for the functionality of a component to return to a fully functional state, and provides important basis for establishing disaster response plans and analyzing the restoration speed to normal operation of structures. The standard normal distribution function Eq.\u0026nbsp;(2) was utilized to derive restoration curves, and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the restoration curves provided by FEMA (2024) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In this formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q\\left[{ds}_{i}\\right|t]\\)\u003c/span\u003e\u003c/span\u003e denotes the time-dependent probability of restoration for each damage state, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e represents the restoration evaluation period. The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varPhi\\:\\left(\\cdot\\:\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eis the cumulative distribution function of the standard normal distribution, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e correspond to the mean and standard deviation, respectively, of the lognormal distribution governing the restoration time.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Q\\left[{ds\\ge\\:ds}_{i}|t\\right]=\\:\\varPhi\\:\\left(\\frac{\\text{l}\\text{n}\\left(\\right(t)-{\\delta\\:}_{i})}{{\\gamma\\:}_{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Results and Application","content":"\u003cp\u003eApplication of the Proposed Methodology to Simulated Railway Network\u003c/p\u003e\n\u003cp\u003eIn this study, the Korean Peninsula railway network was selected as the research subject to quantitatively evaluate the economic damage to railway networks during seismic events, and appropriate earthquake scenarios were established. The railway network under study was constructed to include both high-speed lines and conventional lines in order to derive economic loss for passengers and freight.\u003c/p\u003e\n\u003cp\u003eFor the high-speed line, the Honam High-Speed Railway section was adopted, and the actual operating segment consisting of five stations was reflected in the model. This segment is a representative high-speed rail axis that plays a critical role in regional and long-distance passenger transport. The conventional lines were based on the actual routes of the Gyeongbu Line, Honam Line, and Jeolla Line, and a network consisting of a total of 38 stations was constructed using schematic diagrams, railway route maps, and latitude-longitude coordinates for each station. Through this, a dual railway network structure was implemented in which high-speed lines primarily handle passenger transport and conventional lines primarily handle freight transport.\u003c/p\u003e\n\u003cp\u003eTo derive the PGA for each structure component, GIS-based ground classification data were utilized across the entire Honam High-Speed Railway section to subdivide bridge, embankment, tunnel, and cut segments, and the length and ground characteristics of each segment were determined. Using ground classification (S1\u0026thinsp;~\u0026thinsp;S5), shear wave velocity (Vs), and ground composition information, the calculation of structure component lengths for the Honam High-Speed Railway confirmed that it consists of 19 bridges, 18 embankments, 7 tunnels, and 8 cut segments. For conventional lines, the structure component lengths for each segment were also calculated based on latitude-longitude data for each station, resulting in 39 bridges, 42 embankments, and 15 tunnel segments. This data is utilized as foundational data for calculating functionality loss for each component and evaluating the functionality degradation of the entire network. The railway network modeling image for which transportation volume loss was evaluated in this study based on the given data is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, and the target lines are as follows:\u003c/p\u003e\n\u003cp\u003e(1) High-Speed Line 1: Osong\u0026ndash;Gongju\u0026ndash;Iksan\u0026ndash;Jeongeup\u0026ndash;Gwangju Songjeong\u003c/p\u003e\n\u003cp\u003e(2) Conventional Line 2: Osong \u0026ndash;Daejeon\u0026ndash;Dongiksan\u0026ndash;Dongsan\u003c/p\u003e\n\u003cp\u003e(3) Conventional Line 3: Osong \u0026ndash; Daejeon \u0026ndash;Hanam\u003c/p\u003e\n\u003cp\u003eDefinition of Earthquake Scenarios\u003c/p\u003e\n\u003cp\u003eThe earthquake scenario was established based on the Korean Peninsula seismic risk assessment map proposed by Lee et al. (2022) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e to consider conditions that could cause actually significant damage to the railway network proposed in this study. The epicenter location was set at a point approximately 13 km from Iksan station (35.99\u0026deg;N, 127.08\u0026deg;E), and the earthquake magnitude was set to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{L}\\)\u003c/span\u003e\u003c/span\u003e = 6.0, similar to the 2016 Gyeongju earthquake. The focal depth was selected as 7 km for conservative application in this study, based on the report by Grigoli et al. (2018) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e that the focal depth of the Pohang earthquake was 3 to 7 km. This is to simulate realistic earthquake disaster scenarios that domestic railway infrastructure may face by reflecting moderate-scale earthquake cases observed on the Korean Peninsula.\u003c/p\u003e\n\u003cp\u003eNext, an attenuation relationship considering epicentral distance and ground conditions is required to calculate the spatial distribution of seismic dynamic loads. This study calculated the bedrock peak ground acceleration (PGA) at each component location by applying the ground motion attenuation equation Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) and model coefficients Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e from Emolo et al. (2015) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, which reflect the ground characteristics of the Korean Peninsula. Subsequently, the final surface PGA was derived by applying the short-period amplification factors by ground type Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e from the Korean seismic design code KDS 17 10 00 (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In other words, the amplified surface acceleration at the point where the actual structure is located was calculated by reflecting the short-period amplification factors according to the ground classification (S1 to S5) of each section to the PGA of each component. The surface PGA calculated in this manner is subsequently utilized as the input seismic intensity (Intensity Measure, IM) for calculating functionality loss curves for each component such as railway bridges, embankments, and tunnels.\u003c/p\u003e\n\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ5\" class=\"mathdisplay\"\u003e$$\\:{ln}PGA=a+bM+c\\:ln\\:[\\:\\sqrt{{\\:R}_{epi}^{2}+{h}^{2}}\\:]+\\:{dR}_{epi}+e$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAttenuation equation parameters for Korean Peninsula, data: Emolo et al. (2015) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ee\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePGA (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{m}/{\\text{s}}^{2}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eShort-period amplification factors, data: KDS 17 10 00 (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGround Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\le\\:0.1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}=0.2\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\ge\\:0.3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}}_{5}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTransportation Volume and Transportation Revenue Prediction of Railway Network\u003c/p\u003e\n\u003cp\u003eTo determine the transportation volume for High-Speed Line 1, which is responsible for passenger transport, station-by-station transportation performance data provided by KORAIL (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e was utilized. The boarding and alighting passenger counts for each station are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Additionally, to estimate freight volumes on Conventional Line 2 and Conventional Line 3, which handle freight transportation, urban freight data provided by KOTI (2020) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e were utilized. The transportation data for Conventional Line 2 are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Based on the provided information, the derived transportation volume matrices for High-Speed Line 1 and Conventional Line 2 are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively.\u003c/p\u003e\n\u003cp\u003eNext, to estimate the passenger transport economic value for High-Speed Line 1, the monetary value of one KTX train set was first calculated as 10,059,600 KRW based on 998 standard seats and 127 special seats. Subsequently, the daily number of KTX operations based on a specific date was compiled for each station in both upward and downward directions, and this was multiplied by the monetary value per train set to calculate the average daily train transportation revenue (KRW/day) for each inter-station segment. The matrix for the passenger transport economic value of High-Speed Line 1 is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, to calculate freight transportation revenue on Conventional Line 2 and Conventional Line 3, a unit price of 45.9 KRW per km was applied based on freight fare information from KORAIL (2017) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A matrix for transportation revenue (KRW/day) was calculated by reflecting the actual travel distance for each segment, and the transportation revenue matrix for Conventional Line 2 is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. Through this, the passenger and freight transportation volume and transportation revenue under normal operating conditions before the earthquake were quantitatively derived from the perspective of the simulated railway network.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of passenger movements by station on the High-Speed Line 1, data: KORAIL (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGwangju Songjeong\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e385,721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205,535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42,236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218,274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e377,066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203,501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43,050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222,625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of freight movements by station on the Conventional Line 2, data: KOTI (2020) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194,384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133,308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74,728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223,828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56,395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93,839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65,629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePassenger transportation volume of High-Speed Line 1 (before the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlighting\u003c/p\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGwangju\u003c/p\u003e\n \u003cp\u003eSongjeong\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178,401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118,339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69,869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGwangju\u003c/p\u003e\n \u003cp\u003eSongjeong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129,454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69,866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation volume of Conventional Line 2 (before the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003cp\u003eAttraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44,914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74,735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79,768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79,768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePassenger transportation revenue of High-Speed Line 1 (before the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlighting\u003c/p\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGwangju\u003c/p\u003e\n \u003cp\u003eSongjeong\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140,834,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352,086,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e533,158,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714,231,600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100,596,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211,251,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e392,324,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e573,397,200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251,490,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150,894,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181,072,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,2145,600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422,503,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321,907,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171,013,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181,072,800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGwangju\u003c/p\u003e\n \u003cp\u003eSongjeong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593,516,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492,920,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342,026,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171,013,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation revenue of Conventional Line 2 (before the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003cp\u003eAttraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76,071,931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e433,937,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482,647,799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,991,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26,459,542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30,652,906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463,158,058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82,656,567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,796,957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e515,148,923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95,756,157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,796,957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003ch2\u003eSeismic Functionality Loss Prediction of Railway Network\u003c/h2\u003e\n\u003c/div\u003e\n\u003ch3\u003eFunctionality Loss for Each Structural Component\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this study, seismic fragility curve data from Kim et al. (2025) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e were utilized to derive fragility curves for railway bridges. The aforementioned study constructed analytical models based on representative PSC box cross-sections extensively applied to Korean high-speed and conventional railway systems. Furthermore, as it incorporates the Korean Peninsula\u0026apos;s ground conditions, material properties, damping ratios, and short-period seismic characteristics, the data were deemed suitable for deriving functionality loss curves in the present study. For embankments, data from Argyroudis and Kaynia (2015) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e were employed. Given the limited availability of actual seismic damage data for domestic railway embankment structures and the difficulty in developing empirical fragility curves, this study presents a systematic fragility assessment methodology based on 2D numerical analysis that considers Eurocode 8 ground classification (Soil types C and D) and various embankment heights (2m, 4m, 6m), making it appropriate for deriving functionality loss curves in this research. For tunnels, data from Kwon et al. (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e were utilized. This study performed dynamic analyses based on representative cross-sections of domestic underground stations, with ground conditions, bedrock depth, and burial depth as parameters, and applied input ground motions reflecting the seismic characteristics of the Korean Peninsula, making it suitable for application in the present study.\u003c/p\u003e\n \u003cp\u003eThe restoration curve data utilized the data for railway track, railway bridge, and railway tunnel provided by FEMA (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The provided restoration curve data are standard models constructed based on engineering characteristics common across nations, transcending structural damage types and restoration procedures, and are sufficiently valid for application to Korean railway infrastructure. Railway structures on the Korean Peninsula are substantially identical to the structural types assumed by FEMA (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, such as concrete tracks, PSC box girder bridges, and concrete lining tunnels, and the damage mechanisms and restoration methods due to earthquakes also have international consistency. Particularly, since actual earthquake damage and restoration cases on the Korean Peninsula are very limited, utilizing verified international standard restoration statistics like FEMA (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e is appropriate for rationally simulating the time-functionality recovery of structures. For these reasons, this study constructed restoration functions for each component of the railway network based on the restoration curve data provided by FEMA (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The seismic fragility curves and restoration curves for bridges derived based on these previous studies are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe derived seismic fragility curve and restoration curve data for each component were applied to Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) through Eq.\u0026nbsp;(8) to derive functionality curves and functionality loss curves. The functionality curve and functionality loss curve for bridges are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctionality Loss for Railway Network\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThrough this approach, this study calculated the functionality loss of each structure component comprising the railway network. Subsequently, by comparing these across sections of each line, the maximum value among the functionality losses of components appearing in a specific section was defined as the representative functionality loss for that section. Defining the representative functionality loss for each section as the maximum value is based on the operational structure of railway infrastructure, which has series system characteristics of railway networks. The operational capacity of railway networks is governed by the performance of the most vulnerable element among individual structural components, and significant functionality degradation of a single component directly causes operational constraints such as speed restrictions, single-track operations, or service suspension for the entire section. For this reason, this study adopted the maximum value among functionality losses of components as the representative functionality loss of sections to conservatively evaluate the functionality degradation of railway networks after earthquakes.\u003c/p\u003e\n \u003cp\u003eAdditionally, this study selected a restoration evaluation period of 100 days to identify functionality loss for each component. This is to reflect the characteristic that functionality degradation of structures after earthquakes continuously affects over a certain period beyond the short-term emergency restoration stage. In fact, seismic vulnerability studies and infrastructure resilience analyses report that even if initial emergency measures are completed within several days, structural repairs, inspections, and operational stabilization processes proceed over several months. Furthermore, FEMA (2024) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e suggests that most railway components from slight damage to extensive damage recover their functionality within approximately 30 to 90 days, which can be utilized as an important reference value for setting restoration periods. Accordingly, this study set the restoration evaluation period to 100 days, extended beyond 90 days, to reflect these international standards and the mid- to long-term recovery characteristics of structures.\u003c/p\u003e\n \u003cp\u003eThrough this, the extent to which structures lost functionality during the 100 days after the earthquake can be intuitively confirmed. The calculated functionality loss rate (%) is utilized as an important criterion for evaluating the actual operational impact of structures and future restoration needs by clearly presenting the degree of performance degradation during the restoration evaluation period.\u003c/p\u003e\n \u003cp\u003eTransportation Volume Loss and Transportation Revenue Loss Prediction\u003c/p\u003e\n \u003cp\u003efor Railway Network\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eTransportation Volume Loss for Railway Network\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eAs a result of estimating the economic damage for each line of the railway network, both High-Speed lines and Conventional lines showed significant reductions in transportation volume and transportation revenue during seismic events. The transportation volume loss for High-Speed Line 1 is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, and the transportation volume loss for Conventional Line 2 is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor High-Speed Line 1, the passenger transportation volume (person/day) decreased from 864,091 to 345,107, representing a loss of 518,984 passengers (60.06%). For Conventional Line 2, which transports freight, the freight transportation volume (ton/day) decreased from 489,213 to 184,840 after the earthquake, representing a loss of 304,373 tons (62.22%). Similarly, for Conventional Line 3, the freight transportation volume (ton/day) decreased from 297,326 to 146,696 after the earthquake representing a loss of 150,629 tons (50.66%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePassenger transportation volume loss of High-Speed Line 1 (after the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlighting\u003c/p\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGwangju Songjeong\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102,906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112,576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGwangju Songjeong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81,689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518,984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[person/day]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation volume loss of Conventional Line 2 (after the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003cp\u003eAttraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50,305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304,373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[ton/day]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eTransportation Revenue Loss for Railway Network\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eNext, the transportation revenue loss for each line of railway network was calculated based on the derived transportation volume. For High-Speed line 1 which transports passengers, the passenger transportation revenue (KRW/day) decreased from 6.66\u0026nbsp;billion to 2.75\u0026nbsp;billion, resulting in a loss of approximately 3.9\u0026nbsp;billion (58.75%), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e. For Conventional line 2 which transports freight, the freight transportation revenue (KRW/day) decreased from 2.27\u0026nbsp;billion to 0.79\u0026nbsp;billion after the earthquake, resulting in a loss of approximately 1.48\u0026nbsp;billion (65.23%). In addition, for Conventional line 3, the freight transportation revenue (KRW/day) decreased from 1.93\u0026nbsp;billion to half level at 0.76\u0026nbsp;billion after the earthquake, resulting in a loss of 1.17\u0026nbsp;billion (60.67%). The transportation revenue loss matrix for Conventional line 2 is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, and based on the derived results, it was confirmed that the daily economic loss of High-Speed line responsible for passenger economic value was larger than Conventional Line transporting freight.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePassenger transportation revenue loss for High-Speed Line 1 (after the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlighting\u003c/p\u003e\n \u003cp\u003eBoarding\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGwangju Songjeong\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31,128,750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222,175,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336,437,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450,699,784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGongju\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,234,821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133,305,570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247,567,487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e361,829,404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158,697,107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95,218,264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101,924,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203,848,889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJeongeup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266,611,140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203,132,297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96,261,975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53,189,468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGwangju Songjeong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374,525,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311,046,330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192,523,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50,234,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,912,593,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[KRW /day]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation revenue loss for Conventional Line 2 (after the earthquake)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneration\u003c/p\u003e\n \u003cp\u003eAttraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,428,413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288,887,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324,877,276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaejeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,321,456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17,615,080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,632,919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongiksan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308,341,185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55,027,486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,671,850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDongsan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346,754,257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64,454,866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,671,850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,484,684,350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[KRW /day]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThrough this, the vulnerable sections and key bottleneck sections of the railway network can be clearly identified, and the possibility of serious disruption of national logistics network and passenger mobility in case of earthquake can be quantitatively confirmed. Also, by calculating the quantitative economic damage, it can be directly utilized for selection of recovery priority of specific sections and facilities, budget allocation, and feasibility of pre-reinforcement investment.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEconomic Loss for Railway Network\u003c/h3\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eFinally, based on the restoration evaluation period of 100 days set in this study, the economic loss caused by the degradation of passenger and freight transportation function of railway network after earthquake occurrence was quantitatively analyzed in terms of actual operation.\u003c/p\u003e\n \u003cp\u003eIn the case of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e organized for High-Speed line 1, it was analyzed that if a daily loss of 3.9\u0026nbsp;billion KRW occurs, the cumulative loss of approximately 391.3\u0026nbsp;billion KRW occurs during the restoration evaluation period of 100 days. In the case of Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e organized for Conventional line 2, if a daily loss of 1.48\u0026nbsp;billion KRW occurs, the cumulative loss of approximately 148.5\u0026nbsp;billion KRW occurred during 100 days. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e organized the freight transportation volume and transportation revenue for Conventional line 3, and it was analyzed that if a loss of 1.17\u0026nbsp;billion KRW per day occurs, the cumulative loss of approximately 117.4\u0026nbsp;billion KRW occurs during the restoration evaluation period of 100 days. Comparing only the transportation revenue loss, it was found that the passenger transportation revenue loss was approximately 2.5 to 3 times larger than the freight transportation revenue loss.\u003c/p\u003e\n \u003cp\u003eHowever, considering that in the case of population, the existence of detour routes and utilization of alternative transportation methods are relatively easy when earthquake occurs, the actual economic ripple effect needs to be evaluated more importantly for the transportation revenue loss of freight. In other words, because freight transportation has low substitutability and delays and interruptions lead to direct economic damage to the overall industry, it is reasonable to focus on the impact of freight sector in the economic loss analysis of railway network during earthquake. These results show that the functional degradation due to earthquake in long-distance railway network sections responsible for large-scale freight transportation can have a significant impact on the national logistics system and socio-economically.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePassenger transportation volume loss of High-Speed Line 1 and 100-Day economic loss\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation Volume (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e864,091\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation Volume (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345,107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation Volume Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518,984\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 60.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab14\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation volume loss of Conventional Line 2 and 100-Day economic loss\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation revenue (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6,659,455,200\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation revenue (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,746,862,031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePassenger Transportation revenue Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,912,593,169\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 58.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100-Day Economic Loss [KRW]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e391,259,316,927\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e489,213\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184,840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304,373\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 62.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab15\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFreight transportation volume loss of Conventional Line 3 and 100-Day economic loss\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2,276,074,402\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e791,390,052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,484,684,350\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 65.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100-Day Economic Loss [KRW]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e148,468,434,953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e297,326\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146,696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation Volume Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150,629\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 50.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabf\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue (Before Earthquake)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1,935,674,138\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue (After Earthquake)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e761,328,274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreight Transportation revenue Loss\u003c/p\u003e\n \u003cp\u003eDue to the Earthquake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,174,345,864\u003c/p\u003e\n \u003cp\u003e(Loss Ratio: 60.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100-Day Economic Loss [KRW]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e117,434,586,354\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study quantitatively confirmed the process by which damage to railway structure components due to earthquakes propagates into functional degradation across the entire network, leading to transportation volume loss and economic loss of passengers and freight. To this end, seismic functionality loss curves were constructed by integrating seismic fragility curves and restoration curves, and by linking these with OD matrix-based transportation volume and transportation revenue calculations, the process by which damage to individual structures leads to economic loss was systematically presented.\u003c/p\u003e \u003cp\u003eThe methodology proposed in this study was applied to a simulated railway network based on the existing Korean Peninsula railway system to derive socioeconomic losses for High-Speed lines and Conventional lines. For this purpose, an earthquake scenario with a magnitude of 6, similar to the Gyeongju earthquake, was established, and the focal depth was set at 7 km, similar to the Pohang earthquake. In addition, to reflect the ground characteristics of the Korean Peninsula, Korean attenuation equations and short-period amplification factors were applied to quantitatively confirm the functionality loss of individual structures in the railway network.\u003c/p\u003e \u003cp\u003eAdditionally, to reflect the serial system characteristics of railway networks, the representative functionality loss for each segment was defined as the maximum functionality loss value among the structure components included in that segment. Although railways consist of various structures such as bridges, embankments, tunnels, and cuts arranged consecutively within a given section, the actual operability of trains is determined by the most vulnerable element among them. When significant functional degradation occurs in any single bridge or tunnel, strong operational restriction measures such as speed limits, reduced train formations, single-track operation, or service suspension become unavoidable across the entire segment to ensure structural stability. From this perspective, adopting the maximum value as the representative functionality loss for the segment can be interpreted as a conservative approach to project the damage occurring in a single structure to the actual level of operational disruption. The high transportation volume loss rate shown in the analysis results clearly demonstrates the structural vulnerability where such bottleneck structures determine the operational capacity of the entire network.\u003c/p\u003e \u003cp\u003eNext, the decision to set the restoration evaluation period at 100 days also holds important interpretive significance. The impacts following an earthquake do not simply dissipate during the emergency recovery phase of a few days immediately after occurrence, but often persist over several months as they extend into structural repairs, detailed inspections, equipment replacement, and operational stabilization. While FEMA (2024) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e suggests recovery periods ranging from approximately 30 to 90 days depending on the damage level of structure components, this study set the evaluation period at 100 days, which encompasses this range while being slightly extended, in order to confirm the period during which structural functional degradation continues to affect actual operations even after short-term emergency measures. By accumulating the functionality loss rate and daily transportation revenue loss over the entire restoration evaluation period, it becomes possible to more realistically estimate the scale of long-term economic damage caused by a single earthquake.\u003c/p\u003e \u003cp\u003eAs a result, of applying the seismic fragility-based railway network economic loss assessment methodology proposed in this study, functional degradation of railway networks following earthquakes was found to lead to substantial economic damage. Over a 100-day restoration period, cumulative losses were estimated at KRW 391.3\u0026nbsp;billion for High-Speed Line 1 passenger transport, KRW 148.5\u0026nbsp;billion for Conventional Line 2 freight, and KRW 117.4\u0026nbsp;billion for Conventional Line 3 freight. Loss rates reached 50\u0026ndash;62% for transportation volume and 59\u0026ndash;65% for transportation revenue.\u003c/p\u003e \u003cp\u003eIn terms of monetary value, it was found that the economic loss of passenger economic value was approximately 2.5-3 times larger compared to freight transportation loss. However, these results need to be interpreted considering the substitutability characteristics of each transportation type. In the case of passenger transportation lines, even if railway service in a specific section is limited due to earthquake, there is room for loss mitigation as demand is dispersed to alternative transportation methods such as roads, buses, and aviation. On the other hand, freight transportation has remarkably low substitutability due to the characteristics of mass transportation. In addition, delays and interruptions of freight economic value cause chain economic ripple effects across the overall industry such as manufacturing and distribution industries. Therefore, in terms of practical economic impact, the loss of freight transportation should be evaluated more importantly.\u003c/p\u003e \u003cp\u003eThe results of this study quantitatively demonstrate that the functional degradation of railway network sections responsible for long-distance freight economic value in case of earthquake can have serious impact on the national logistics system and overall socio-economic system. Through the proposed methodology, vulnerable sections and key bottleneck sections on the network can be clearly identified, and it can be directly utilized for establishing earthquake disaster response strategies. First, by quantifying the transportation volume loss rate and economic loss scale of each line under earthquake scenario, vulnerable sections within the network can be clearly identified. Even under the same seismic intensity, the magnitude of economic loss suffered by passenger transportation High-Speed line, short-distance freight transportation conventional line, and long-distance freight transportation conventional line are different, and by evaluating this quantitatively, the sections where restoration budget, manpower, and equipment should be prioritized can be objectively selected. Second, by quantifying the relationship between functional loss and economic loss of railway network, it provides a cost-benefit analysis basis for seismic reinforcement investment for specific structures (bridges, embankments, tunnels). For example, if a specific bridge on long-distance conventional line is confirmed to cause a significant portion of the entire network loss, the investment feasibility can be evaluated by comparing the seismic reinforcement cost of the bridge with the 100-day cumulative economic loss expected to be reduced through reinforcement. Third, for restoration period reduction policies, the degree to which restoration speed improvement leads to actual economic loss reduction can be quantitatively estimated, so it can also be utilized for efficiency evaluation of restoration strategies.\u003c/p\u003e \u003cp\u003eAs a result, the quantitative evaluation results of this study are expected to be utilized as scientific evidence in the policy decision-making process for establishing earthquake disaster response strategies such as selection of restoration priority, efficient allocation of limited budget, and feasibility evaluation of pre-seismic reinforcement investment.\u003c/p\u003e \u003cp\u003eThis study has limitations that it was conducted based on specific earthquake scenario, network structure, and existing demand data. In the case of actual earthquake occurrence, the economic loss can vary depending on earthquake magnitude, epicenter location, ground conditions, structural deterioration, etc. Nevertheless, this study has significance in that it quantified the economic loss of railway network after earthquake occurrence and presented a systematic methodology for restoration priority decision-making and vulnerable section identification. In future research, we plan to establish a comprehensive and robust economic evaluation system for railway network by considering various earthquake scenarios and ground conditions more precisely.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by a grant from R\u0026amp;D Program (PK2502A2) of the Korea Railroad Research Institute.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.J.: writing\u0026ndash;original draft, Methodology, J.H.K.: writing-original draft, conceptualization, simulation, M.Y.: writing-review and editing, administration, Methodology. The authors confirm that this work has not been published before, and its publication has been approved by all co-authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data sources, collection methods, and processing techniques are summarized below.Data Sources and Collection: This study utilized data provided by KORAIL and KOTI, seismic fragility curve data obtained from previous studies, and restoration curve data provided by FEMA.Data Types: The data provided by KORAIL include station-specific passenger boarding and alighting volumes, as well as tariff rates based on freight tonnage and transport distance (ton-km). The data provided by KOTI present regional freight movement volumes. The data obtained from previous studies and FEMA were used to derive seismic fragility curves and restoration curves appropriate for conditions on the Korean Peninsula.Data Processing and Analysis: To estimate the transportation volume at each station, an OD matrix was constructed using station-specific boarding and alighting data, in which the passenger flow at each station was interpreted as the station\u0026rsquo;s transportation volume. The seismic fragility curve and restoration curve were derived by applying standard normal distribution functions based on data from previous studies and FEMA. These two curves were then integrated to develop a seismic functionality loss curve, which was combined with the transportation volume matrix or the transportation revenue matrix. Through this process, the overall economic loss of the railway network was evaluated.Data Repository: The data was stored in a local repository within our research laboratory for the duration of the study.Data Access and Usage Conditions: Access to the data and the terms of its utilization are outlined as follows. The data's accessibility for use, whether open to everyone or subject to limitations, is specified.Contact Information: For inquiries, please contact [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim, K. H. et al. The 2017 ML 5.4 Pohang earthquake sequence, Korea, recorded by a dense seismic network. \u003cem\u003eTectonophysics\u003c/em\u003e \u003cb\u003e774\u003c/b\u003e, 228306 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, Y. et al. The 12 September 2016 Gyeongju earthquakes: 1. Observation and remaining questions. \u003cem\u003eGeosci. 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Seismic fragility curve for railway track constructed in railway bridge based on numerical method. \u003cem\u003eKSCE J. Civ. Eng.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (10), 100216 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgyroudis, S. \u0026amp; Kaynia, A. M. Analytical seismic fragility functions for highway and railway embankments and cuts. \u003cem\u003eEarthq. Eng. Struct. Dynamics\u003c/em\u003e. \u003cb\u003e44\u003c/b\u003e (11), 1863\u0026ndash;1879 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwon, S. Y., Kim, J., Kwak, D., Yang, S. \u0026amp; Yoo, M. Development of seismic fragility function for underground railway station structures in Korea. \u003cem\u003eBuildings\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 1200 (2024).\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8373543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8373543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a quantitative assessment framework for evaluating the economic losses of simulated railway networks under seismic events. Accordingly, a simulated railway network was constructed based on the existing rail system of the Korean Peninsula, and the transportation volume for each line segment was quantified using the OD(Origin\u0026ndash;Destination) Matrix with passenger and freight movement data. Subsequently, the seismic fragility curve and the restoration curve were integrated to derive the seismic functionality loss curve, through which the functionality loss of major structure components, including bridges, embankments, and tunnels, was estimated. In addition, the seismic characteristics of the Korean Peninsula were incorporated by applying the Korean ground-motion attenuation equation and short-period amplification factors to the earthquake scenario. The derived functionality loss of each structure component was then integrated with the corresponding transportation volume to estimate the transportation volume loss for each line segment. The economic value loss was applied to the transportation volume to ultimately evaluate the transportation revenue for each segment.\u003c/p\u003e \u003cp\u003eAs a result, passenger transportation volume and transportation revenue in High-Speed lines decreased by 50\u0026ndash;60%, with cumulative losses reaching hundreds of billions of KRW over the restoration evaluation period of 100 days. Meanwhile, the two conventional lines responsible for freight transport also showed similar loss rates, with cumulative losses reaching tens of billions of KRW over the restoration evaluation period of 100 days. In terms of monetary value, passenger transport losses were 2.5-3 times greater than freight transport losses; however, considering the low substitutability of freight transport and its cascading ripple effects across industries, the practical economic impact of the freight sector is judged to be more significant.\u003c/p\u003e \u003cp\u003eThe methodology proposed in this study quantitatively demonstrates how the functional degradation of individual structures in a railway network during seismic events translates into actual economic loss. This framework provides a systematic foundation for supporting future decisions on restoration prioritization, evaluating the feasibility of seismic reinforcement investments, and establishing earthquake response strategies.\u003c/p\u003e","manuscriptTitle":"A Methodology Framework for Predicting Economic Loss in Korean Peninsula Railway Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 13:16:45","doi":"10.21203/rs.3.rs-8373543/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":"87c3086d-afed-43b5-863a-946f636033e0","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62411476,"name":"Physical sciences/Engineering"},{"id":62411477,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-02-18T09:41:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 13:16:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8373543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8373543","identity":"rs-8373543","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}