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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Effects of station-area built environment on urban rail transit passenger flow</title>
      <link>https://trid.trb.org/View/2785347</link>
      <description><![CDATA[The spatiotemporal mismatch between urban rail transit (URT) stations and surrounding urban functions challenges operational efficiency worldwide. Existing research predominantly focuses on average daily ridership, offering limited insights into how built environment influences vary across station types, time periods, and spatial scales. This study develops an integrated framework combining station classification with multiscale geographically weighted regression (MGWR) to investigate the mechanisms linking built environment to passenger flows. Using Changsha, China as a case study, stations are classified into residential, employment, and mixed-use types via K-Means clustering. The MGWR model is compared with OLS model and GWR model. Results reveal three key findings: built environment effects exhibit spatiotemporal heterogeneity, functional-type dependence, and spatial scale variability; factors such as office density have divergent effects across station types and between morning and evening rush hours. The framework effectively identifies local and global operational scales, supporting a paradigm shift towards station-specific, refined governance strategies.]]></description>
      <pubDate>Mon, 05 Oct 2026 09:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2785347</guid>
    </item>
    <item>
      <title>Explaining COVID-19 Tokyo metro station-level ridership disruption and recovery: pre-pandemic demand, built environment and centrality</title>
      <link>https://trid.trb.org/View/2778179</link>
      <description><![CDATA[COVID-19 caused severe and uneven metro ridership declines. This study examines station-level annual ridership change in Tokyo from 2020 to 2023 relative to 2019, integrating pre-pandemic demand, built environment, and network centrality. Its main contribution is to identify how these factors shaped ridership resilience from acute disruption to recovery using ordinary least squares (OLS) and geographically weighted regression (GWR). Results show that high pre-pandemic demand and Production and Service intensity are associated with larger proportional losses, especially in 2020–2021. Road density is consistently linked to smaller declines and stronger recovery, indicating the importance of local access networks. Land-use mix becomes increasingly positive during recovery, suggesting that functionally diverse station areas better supported localized daily activities. Destination accessibility and centrality show more spatially uneven and temporally unstable effects. These findings suggest that resilience-oriented planning should combine targeted operations for vulnerable high-demand stations with local access improvements and mixed-use station-area development.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2778179</guid>
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    <item>
      <title>Investigating Nonlinear Causal Effects and Determinants of Urban Rail Transit on Commercial Vitality</title>
      <link>https://trid.trb.org/View/2781255</link>
      <description><![CDATA[As gateways to urban rail transit networks, stations not only facilitate population mobility but also stimulate adjacent commercial development and enhance commercial vitality. Nevertheless, existing research has not yet elucidated this mechanism comprehensively, with most studies remaining at the level of correlation analysis rather than rigorously examining the causal link between station openings and improvements in commercial vitality. Therefore, this study leverages diverse socioeconomic and transportation data to explore the causal impact of opening stations on commercial vitality in the surrounding areas. First, the propensity score matching method was employed to address potential confounding bias. Additionally, we develop a Causal XGBoost algorithm, integrating geographic weights and time-fixed effects to capture spatiotemporal dependencies, thus analyzing the heterogeneity of causal effects. Finally, we employ the Shapley additive explanations (SHAP) tool to enhance result interpretability. An empirical case study in Shanghai, China, demonstrates that station opening has positive causal effects on commercial vitality, with effects varying spatially across a 1,500 to 2,000 m range. This causal relationship is evident in the significant enhancement of positive effects for population density, Gross Domestic Product (GDP), and road network density on regional commerce driven by station openings. Notably, station opening enhances commercial vitality only when located in a sufficient population base area; otherwise it exerts a negative impact. Furthermore, GDP and road network density enhance commercial vitality more strongly beyond 2,000 m from stations. These results offer insights into transportation planning for urban economic development, emphasizing strategies to improve the efficiency and sustainability of urban rail transit.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:35:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781255</guid>
    </item>
    <item>
      <title>Identifying detour patterns of rail feeder bus routes: A city-scale analysis</title>
      <link>https://trid.trb.org/View/2775578</link>
      <description><![CDATA[Feeder bus services are essential for extending the service coverage of urban rail transit systems, yet feeder-route detour and its supply–demand matching remain insufficiently understood at the city scale, particularly in terms of spatial heterogeneity, underlying mechanisms, and implications for feeder service design. This study develops an integrated framework for feeder-route detour identification, typology, and supply–demand diagnosis by incorporating bus network topology, multidimensional route indicators, deep embedded clustering, and spatially explicit mismatch analysis. A case study is conducted in Nanjing, China, using rail and bus network data, land-use data, and smart card data. The results reveal five distinct feeder-route types with different physical meanings. Supply–demand mismatch varies systematically across route types. Relatively direct routes are not always associated with strong demand, while highly detoured routes may reflect either genuine inefficiency or deliberate coverage-oriented service provision. High-demand routes are concentrated in and around the city center, whereas low-demand routes are more widely distributed across intermediate and peripheral areas. Feeder-route detour is not a uniform manifestation of poor design, but a spatially differentiated outcome shaped by urban form, barrier conditions, and service strategy. By moving beyond conventional directness measures toward typology-based and spatially explicit diagnosis, this study provides new evidence for understanding feeder bus detour and offers practical implications for feeder-route optimization, station-area accessibility improvement, and transit equity.]]></description>
      <pubDate>Fri, 25 Sep 2026 10:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775578</guid>
    </item>
    <item>
      <title>Developing an Urban Quality Metric for Transit Access and Safety</title>
      <link>https://trid.trb.org/View/2775962</link>
      <description><![CDATA[To make a successful transit-oriented community, the walkability and bikeability of the landscape and access to destinations must be addressed alongside the construction of transit services. This research trials a formula using these criteria to produce a Transit Access and Safety Score (TASS), and applies it to San Diego County, California. This study draws on previous research to create a grading metric for public transit stations and their surroundings. Regression analysis using U.S. Census Journey to Work metrics and traffic-related casualty rates is employed to determine the relationship of the walkability/ bikeability aspect of the TASS grades to patterns of human movement and behavior in response to the built environment. The measurement developed in this research equitably, efficiently, and adequately grades the walkability, bikeability, and proximity of destinations to transit stations. The research could not establish a relationship between TASS scores and measures of human activity such as ridership, casualty rates between people and motor vehicles, and commuting patterns. The TASS can assist decision-makers in identifying aspects of the built environment to encourage density and transit use.]]></description>
      <pubDate>Thu, 24 Sep 2026 17:08:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775962</guid>
    </item>
    <item>
      <title>How do water networks shape e-bike sharing near metro? A spatiotemporal analysis in Shaoxing, China</title>
      <link>https://trid.trb.org/View/2718215</link>
      <description><![CDATA[Urban geography and topographic environments are pivotal factors shaping residents’ travel behavior. While existing studies have predominantly focused on the influence of mountainous terrain, the systematic impact of dense water networks on residents’ short-distance travel and metro-access behavior has not been sufficiently examined. Building on this foundation, this study aims to address the following core questions: In a geographically dense water network environment, how do built environment factors spatiotemporally and heterogeneously influence the usage of shared e-bikes around metro stations? To answer this question, this study takes Shaoxing, China as a case study. By extending the traditional “5D” built environment framework, three water-related variables—water system density, bridge density, and detour index—are incorporated to construct an expanded “5D+W” analytical framework. The walking-based catchment area is used to identify shared e-bike trips around metro stations. Geographically and Temporally Weighted Regression (GTWR) model is applied to examine the spatial and temporal variations in the relationship between the built environment and shared e-bike usage. Compared with traditional methods such as Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), the GTWR model demonstrates better fitting accuracy and can effectively capture the spatiotemporal variations of shared e-bike trips, revealing distinct morning and evening peak patterns. The results also show that water-related variables exert significant, time-sensitive effects on commuting behavior, emphasizing the necessity of introducing additional variables. These findings confirm the applicability of the “5D+W” framework in water towns, and provide theoretical and practical guidance for transport authorities and operators to optimize shared e-bike deployment, metro connectivity, and mobility strategies under water network constraints.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2718215</guid>
    </item>
    <item>
      <title>Numerical and Experimental Investigation of the Impact of Close-Range Construction of Large-Section Tunnels on Existing Subway</title>
      <link>https://trid.trb.org/View/2772508</link>
      <description><![CDATA[Large-section tunnel construction using the mining method can significantly affect the operational safety of existing metro lines and ground stability, while their non-uniform settlement remains challenging to monitor comprehensively. In this study, the post-station section of a metro project in Chengdu was investigated to elucidate the vertical displacement and ground settlement behavior induced by a large-section tunnel undercrossing an existing metro line. A displacement reconstruction method integrating sparse-point monitoring with a radial basis function neural network (RBFNN) was developed to fit the full-field settlement distribution of both the existing line and the ground surface. A finite element model incorporating the existing shield tunnels, station structures, and the newly constructed mined tunnel was established, and the construction process was simulated. The numerical results indicated maximum settlements of 4.42 mm for the existing line and 4.37 mm for the ground surface, with fitting errors below 5.8% and 6.1%, respectively. Physical model tests further validated the approach, yielding maximum settlements of 0.86 mm and 1.01 mm for the existing line and ground surface, respectively, and an average fitting error below 3.5%. Both numerical and experimental findings confirmed that the induced displacements were within a controllable range and that the surrounding strata remained stable. The proposed method enables accurate and intuitive reconstruction of displacement distribution during under-crossing tunnel construction, reducing the number of required monitoring points while maintaining high fitting accuracy.]]></description>
      <pubDate>Fri, 18 Sep 2026 16:01:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772508</guid>
    </item>
    <item>
      <title>A mobile data-enhanced framework for spatial-temporal analysis of subway competitiveness and equity implications</title>
      <link>https://trid.trb.org/View/2681184</link>
      <description><![CDATA[Conventional subway analyses relying on static IC card data or limited surveys fail to capture the complex, dynamic interactions between passenger flows, land use patterns, and multimodal transportation choices in high-density urban environments. This study overcomes these methodological limitations by developing an innovative analytical framework that synergistically integrates large-scale mobile phone positioning data with traditional transport surveys and automated fare collection records. Using Guangzhou’s extensive metro system (247 stations across 531 km) as a representative case study, we employ advanced data fusion techniques and machine learning algorithms to reconstruct complete travel chains and dynamically delineate station service areas with unprecedented spatial-temporal resolution. Our hybrid methodology combines multinomial logit modeling with random forest classification to systematically quantify subway competitiveness across different urban contexts, revealing three key findings: First, we identify distinct spatial thresholds for effective service areas (800 m radius in central business districts vs. 1.4 km in suburban corridors). Second, the analysis uncovers an inverted U-shaped association between parking supply-demand ratios and mode share optimal balance at 1.75. Finally, and most critically from an equity perspective, the significant disparities suggesting that low-income suburban commuters experience are associated with 2 times higher spatiotemporal costs than central city residents. The proposed framework provides urban planners with a robust, scalable tool for transit network optimization, offering particular value for rapidly urbanizing megacities in Asia and other developing regions. By effectively bridging cutting-edge big data analytics with established transportation modeling approaches, the proposed framework provides a robust tool for transit optimization. Ultimately, this study answers critical questions regarding the performance and equity of subway systems in high-density environments, bridging cutting-edge big data analytics with established transportation modeling approaches.]]></description>
      <pubDate>Fri, 18 Sep 2026 12:03:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681184</guid>
    </item>
    <item>
      <title>Uncovering Travel Rhythms: A Spatiotemporal and Data-Driven Typology of Urban Rail Transit Stations</title>
      <link>https://trid.trb.org/View/2681165</link>
      <description><![CDATA[Smart card data (SCD) from Automatic Fare Collection (AFC) systems provide fine-grained insights into urban mobility rhythms. Yet most existing studies focus on static ridership, with limited attention to the relationship between station-level travel rhythms and the surrounding built environment. This study develops a spatiotemporal and data-driven framework to classify urban rail transit (URT) stations and examine their land-use determinants. Using two weeks of AFC data from 128 stations in Nanjing, China, a two-stage approach is implemented. First, a Gaussian Mixture Model (GMM) is applied to cluster stations based on weekday ridership profiles, yielding six distinct categories: residential oriented, employment oriented, hub comprehensive, spatial mismatched, predominantly residential mixed use, and predominantly employment mixed use. These categories reveal a clear transition from mixed and hub functions in the city center to residential-dominated stations in suburban areas. Second, a Random Parameter Logit (RPL) model is employed to assess the influence of socio-demographic, land-use, and amenity variables, capturing heterogeneity more effectively than the conventional Multinomial Logit (MNL) model. Results highlight the decisive roles of population density, housing prices, and employment land in shaping employment- and hub-oriented stations, while community-oriented facilities, such as healthcare and daily services, exert stronger effects on residential-oriented stations. These findings enrich theoretical understanding of station heterogeneity and provide empirical evidence for transit-oriented development (TOD), land-use coordination, and multimodal integration. The proposed framework is transferable to other rapidly urbanizing cities, offering practical guidance for building efficient and sustainable URT systems.]]></description>
      <pubDate>Fri, 18 Sep 2026 12:03:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681165</guid>
    </item>
    <item>
      <title>Impact of real-time crowding information on mass-transit passengers’ platform placement choices</title>
      <link>https://trid.trb.org/View/2711687</link>
      <description><![CDATA[Crowding in public transport systems affects both passenger comfort and network efficiency. Besides operational solutions, interventions targeting passenger behaviour could address this issue efficiently. Real-Time Crowding Information (RTCI) systems, which display coach-level occupancy on platform screens, can help passengers avoid the most congested coaches or trains. However, existing research on RTCI’s impact on passenger behaviour, relies predominantly on stated preference surveys, where respondents declare a single dominant strategy. Such designs cannot capture the trade-offs passengers make when competing cues – entrance proximity, exit knowledge, and crowding information – simultaneously influence their choice, nor can they reveal dynamic effects arising when passengers confront actual crowding after receiving RTCI.Here, using two online behavioural experiments – a schematic click-map task (N = 186) and an active snapshot navigation task (N = 397) – we quantified how passengers integrate these competing placement cues into hybrid positioning strategies.We found RTCI massively shifted positioning away from crowded coaches (79–91%), but crowd avoidance did not function as an independent strategy: it emerged only when crowding conflicted with a preferred position, producing compromise behaviours. For instance, when both exit and crowding were near the entrance, 63% of participants positioned themselves just past the crowded zone, to remain close to the entrance and exit. Additionally, the crowding level indicated by RTCI also modulated passengers’ reaction to actual on-board density upon train arrival: at equivalent real density, passengers who had been shown a high-crowding signal on their platform zone were significantly more likely to make a last-minute coach change, suggesting that RTCI sets expectations that amplify perceived crowding.These findings demonstrate that behavioural measures reveal a complexity of positioning strategies that stated preference approaches can miss. This has direct implications for the design and deployment of RTCI systems, as unanticipated passenger redistribution and rushed platform behaviour must be accounted for to ensure operational efficiency.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711687</guid>
    </item>
    <item>
      <title>Transit-oriented land use planning for regional metro networks via deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2713628</link>
      <description><![CDATA[Transit-oriented development (TOD) mitigates traffic congestion and emissions by promoting high-density, mixed-use development around transit stations. While extensive research exists on single-station catchments, TOD planning at the metro network level remains underexplored. Recent studies emphasize the importance of coordinating land use across stations, yet a decision-support framework for network-scale TOD optimization is still absent. This study introduces a 𝐝eep 𝐫einforcement 𝗹earning framework for 𝐫egional 𝐦etro 𝐧etwork TOD planning (DRL-RMN). It formulates network-level land use planning as a sequential decision-making process, incorporating a graph neural network to encode spatial dynamics and a masked hierarchical action mechanism to account for development constraints. Multiple planning objectives are simultaneously optimized, including population coverage, network opportunity accessibility, and spatial equity. Results from Shanghai and Beijing reveal that DRL-RMN achieves a superior balance between efficiency and equity compared to heuristic benchmarks. An interpretable machine learning model is applied to understand the planning results, revealing that recommended office development tends to cluster around stations with high closeness centrality, whereas the pattern of residential growth is more city-specific. We further demonstrate the flexibility of DRL-RMN by accommodating varied planning objectives (e.g., prioritizing efficiency vs. equity) and budget constraints (e.g., high vs. low). Altogether, our approach offers planners a scalable, interpretable tool for integrating land use and transport systems at the network level.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713628</guid>
    </item>
    <item>
      <title>Transit Access, but at What Cost? Affordability Outcomes Across California’s Transit-Oriented Development Typologies</title>
      <link>https://trid.trb.org/View/2777816</link>
      <description><![CDATA[As California continues to expand its investment in high-quality transit infrastructure, the need to align transit access with housing affordability has become increasingly urgent. In this study, we develop a replicable framework for classifying and evaluating over 60,000 high-quality transit station areas based on built environment characteristics and affordability outcomes. Using a 1.5-mi network-based pedestrian buffer, we apply 𝘒-means clustering to categorize station areas into four typologies: transit-oriented development (TOD), transit-supportive development, limited TOD, and transit-adjacent development (TAD). We then assess affordability using housing and transportation cost burdens across income levels, availability of income-restricted units, and statistical models, including random forest and Pearson correlation analysis. Results reveal that only 14% of station areas meet the criteria for full TOD, while TADs, comprising over 25%, exhibit the highest cost burdens and the lowest levels of affordable housing. Strong inverse correlations between TOD scores and affordability burdens indicate that transit-oriented urban form is closely linked to household cost efficiency. This framework offers a valuable decision-support tool for aligning land use, housing, and transportation policies to target resources where transit access and affordability are most misaligned.]]></description>
      <pubDate>Tue, 15 Sep 2026 09:48:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2777816</guid>
    </item>
    <item>
      <title>Exploring the heterogeneous nonlinear effects of the built environment on metro ridership considering bike-sharing catchment areas</title>
      <link>https://trid.trb.org/View/2732812</link>
      <description><![CDATA[Existing research on the relationship between the built environment and metro ridership mostly uses pedestrian catchment areas (PCA) such as the 800 m buffer, ignoring bike-sharing catchment areas (BSCA). The heterogeneous nonlinear effects of the built environment in different types of stations also tend to be overlooked. Here, we used bike-sharing trip data to identify the BSCA of each metro station in Beijing. A Fuzzy C-means (FCM) clustering-based explainable machine learning framework was proposed to explore the nonlinear effects of the built environment, including land use, transport, accessibility and socio-economic factors on four kinds of metro ridership under PCA and BSCA, respectively. The heterogeneity of the nonlinear relationship among different types of stations was also investigated. We found that BSCA-based models outperform PCA-based models. The nonlinear effects of the built environment on metro ridership change with various catchment areas. These effects tend to have various trends and thresholds in different types of stations. Furthermore, residential and employment-oriented stations are more sensitive to catchment areas. This paper emphasizes the role of BSCA and heterogeneous planning targets in different types of stations in TOD (Transit-oriented development) planning.]]></description>
      <pubDate>Wed, 09 Sep 2026 09:03:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732812</guid>
    </item>
    <item>
      <title>Where Station-Area Effects Really Peak: Donut Gradients, Temporal Displacement, Spatial Dependence, and Vertical Walkability in Taipei, Seoul, and Tokyo</title>
      <link>https://trid.trb.org/View/2775077</link>
      <description><![CDATA[This conceptual and methodological synthesis examines why the location of the strongest station-area response is not stable across mature metro cities. Instead of estimating a pooled East Asian transit premium, it uses peer-reviewed evidence and official urban and rail indicators for Taipei, Seoul, and Tokyo to clarify how peak location depends on outcome type, project timing, spatial dependence, submarket structure, and station-front pedestrian integration. The Taipei evidence reviewed here is used to examine a temporal-displacement mechanism; the Seoul evidence reviewed here to examine a spatial-confounding mechanism; and the Tokyo evidence reviewed here to examine a core-reintegration mechanism. These are case-dominant mechanisms identified from the reviewed evidence, not exclusive labels for every station in each metropolitan system. The synthesis separates three layers: models estimated in the cited studies, transparent calculations performed in the present manuscript, and modeling templates proposed for future harmonized comparative research. It further specifies how vertical walkability can be measured through multilevel circulation, barrier-free access, entrance permeability, weather-protected continuity, transfer directness, and plaza legibility. The contribution is therefore to develop a comparative framework for identifying edge-centered, donut-shaped, model-sensitive, and re-centered station-area gradients, and for clarifying how different forms of evidence can inform the analysis of peak location across mature metro systems.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775077</guid>
    </item>
    <item>
      <title>Spatiotemporal stratified flow analysis: A framework detecting associations between urban facilities and stratified human mobility patterns</title>
      <link>https://trid.trb.org/View/2728289</link>
      <description><![CDATA[Urban studies must address the flow coupling between inhabitants and facilities, yet characterizing these patterns and their spatiotemporal boundaries remain a challenge. However, such flows are uneven, and some do not exhibit an obvious pattern. Hence, a method for verifying the hidden pattern of flow correlating to urban facilities is still needed. This study proposes a framework spatiotemporal stratified flow analysis (SSFA). Facility-related flow clusters can be detected easily via spatiotemporal stratification and the sliding window method. After stratification, compared with the data layer for off-peak hours and low-traffic areas, flow clusters always have both high flow volumes and a high ratio. Therefore, the SSFA proposes a stratified importance method to integrate such characteristics and identify facility-related flows. To validate its performance, this study presents two cases. The first case uses green space and metro travel in Nanjing. The SSFA can capture uneven flow patterns related to the large green space that traditional linear models seldom notice. This case finds that, due to their high-quality service, large green spaces in Nanjing are correlated significantly with long-distance flows for trips covering 10–15 stations during both daytime and nighttime off-peak hours. This result shows the SSFA's precise, stratified flow detection abilities. Then, in the business density–taxi case, the study extends the SSFA to spatiotemporal dimensions. The results show that the SSFA can describe accurately two distinct flow patterns associated with the same facility and identify their spatiotemporal layers. Commercial facilities in Beijing are correlated significantly with taxi flow within 2000 m for most periods, but during the evening, they are correlated negatively with flows at 4000–6000 m distances. In these cases, the SSFA demonstrates excellent robustness and flexibility, providing an ideal basis for facility-centered flow studies.]]></description>
      <pubDate>Wed, 02 Sep 2026 16:33:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728289</guid>
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