<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc4IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSIyeWVhcnMiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycyAvPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A Noise-Robust Approach Using Dynamic Graph Neural Networks for Bus Passenger Flow Prediction</title>
      <link>https://trid.trb.org/View/2672839</link>
      <description><![CDATA[Short-term passenger flow prediction is critical for intelligent scheduling and efficient operation of public transportation systems. However, existing methods often struggle to maintain robustness and generalizability when facing complex and heterogeneous noise sources, such as passenger flow noise including sensor-induced missing values and abrupt ridership fluctuations caused by unexpected events, and graph structural noise resulting from dynamic changes in the network topology due to route modifications or stop closures. To address these challenges, this paper proposes a novel deep learning framework: Robust Dynamic Graph Neural Networks (RDGNN), that integrates noise cleaning, dynamic graph modeling, and more efficient prediction. The proposed noise cleaning employs K-Nearest Neighbor imputation and Gaussian Mixture Models with a penalty function to mitigate data-level noise, while a graph structure denoising module is introduced to correct topological anomalies in the transit network and enhance the reliability of spatial representation. For feature modeling, the framework constructs a dynamic graph neural network from a subgraph perspective to capture both spatial dependencies and temporal dynamics, particularly suited to modeling interactions among transfer stations. A Liquid Neural Network is adopted as the prediction module, leveraging its strong adaptability and memory capacity to handle irregular and non-stationary time series, while remaining computationally efficient. The RDGNN model was trained and validated on real-world bus passenger flow data from Ames, Iowa, and further evaluated on the large-scale EXO dataset. Compared to state-of-the-art models, RDGNN consistently achieves better prediction accuracy, higher robustness to noise and missing data, and greater computational efficiency. Its strong and stable performance across both datasets, including in scenarios such as route disruptions and seasonal variation, demonstrates excellent generalization capability in diverse urban transit systems. The code is available at: https://github.com/XinyiZhou0318/A-Noise-Robust-Approach-Using-Dynamic-Graph-Neural-Networks-for-Bus-Passenger-Flow-Prediction]]></description>
      <pubDate>Fri, 11 Sep 2026 15:51:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672839</guid>
    </item>
    <item>
      <title>Service design of shared first- and last-mile transit systems</title>
      <link>https://trid.trb.org/View/2727419</link>
      <description><![CDATA[This paper explores the design and optimization of shared first- and last-mile transit systems (SFLMTS) as an essential component of public transport systems. It addresses the gap between the demand for efficient connectivity from suburban and rural areas to main transit hubs and the current underperformance of traditional public transportation systems in providing comprehensive last-mile solutions in sparsely populated areas. By focusing on demand-responsive transit (DRT) services, this study presents a novel framework for designing SFLMTS that leverages mobility-on-demand (MoD) principles to enhance the accessibility and efficiency of regional and national rail networks. The paper introduces a comprehensive framework that encompasses design and operational decisions, including passenger commitment, trip consolidation, and service-level considerations, which are crucial for optimizing the performance and economic viability of DRT services. Through a detailed case study, the paper illustrates the application of this framework using realistic data and simplified operational models (SOMs), demonstrating the potential of DRT to significantly reduce the number of required vehicles, decrease operational costs, and improve service levels compared to traditional fixed-route services. The findings highlight the importance of strategic design decisions in maximizing the efficiency and sustainability of first- and last-mile transit solutions, offering valuable insights for transportation planners, policymakers, and researchers.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727419</guid>
    </item>
    <item>
      <title>An approach for assessing bus service reliability based on GNSS and bus occupancy data</title>
      <link>https://trid.trb.org/View/2727417</link>
      <description><![CDATA[Bus transportation offers an effective alternative to private vehicles, promoting a more sustainable service and playing a crucial role in urban mobility. However, different factors can affect bus reliability in service operations. The difference between actual and scheduled arrival times at each stop is used to assess reliability. It accounts for both bus delays, when the vehicle runs behind schedule, and early arrivals, when the vehicle runs ahead of schedule. This paper proposes an approach to assess bus service reliability based on Global Navigation Satellite System (GNSS) data from connected vehicles. The initial step includes estimating bus arrival time at each bus stop. The second step consists of comparing the actual bus arrival time with the expected scheduled time. The study area is located at the main campus of the University of Campinas in Brazil. GNSS data were obtained from a monitoring system called Main Module IoT (MMIoT), which is responsible for providing bus trajectory information. In addition, occupancy data from a bus passenger counting (BPC) system installed in the same buses are used to evaluate the impact of bus occupancy on travel time reliability. Results from input data analysis revealed significant bus delays, especially during peak hours. On-time performance (OTP) is used as an indicator of bus service reliability. Moreover, Pearson correlation coefficients are used to examine the association between service reliability and bus occupancy on campus. Finally, the results provide insights into improving bus service reliability and efficiency on campus, thereby potentially increasing passenger use and satisfaction.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727417</guid>
    </item>
    <item>
      <title>Double-discharge platforms to ease crowding at the suburban railway stations in India: explaining the policy intervention using a multi-method analysis based on game theory and pedestrian simulation</title>
      <link>https://trid.trb.org/View/2727416</link>
      <description><![CDATA[The overcrowding scenarios in suburban railway stations in India during peak hours have necessitated the need for an intervention by the Indian Railways. To address this problem, the Indian Railways put forward a change in the design of the platforms by introducing double-discharge platforms to handle arriving and departing commuters. The intended impact of the revised design was to reduce overcrowding in platforms and footbridges and mitigate potential accidents which can occur as a result of overcrowding. We perform a multi-method analysis of this policy intervention to explain the implications of the double-discharge platforms on crowd management in suburban railway stations. In this regard, a game-theoretic analysis of the double-discharge platform theoretically illustrates how overcrowding is managed with the intervention. Further, a pedestrian-simulation analysis is also performed to understand how the crowding scenarios evolve in suburban railway stations with the intervention. The analysis brings about the effectiveness of the double-discharge platform solution using multiple lenses.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727416</guid>
    </item>
    <item>
      <title>Mobility deserts, socioeconomic inequality, and emerging mobility services in U.S. metropolitan areas</title>
      <link>https://trid.trb.org/View/2762107</link>
      <description><![CDATA[Urban mobility is central to the environmental, economic, and social sustainability of cities, yet access to transportation resources remains uneven across urban neighborhoods. This study develops a mobility index that captures neighborhood-level mobility constraints arising from the combined lack of public transportation and limited access to private vehicles. Integrating demographic data with geospatial information on bus and Metrorail infrastructure, we construct the index for census block groups across the nine largest U.S. metropolitan areas and identify “mobility deserts” as areas in the highest quartile of mobility deprivation within each region. The results reveal pronounced socioeconomic disparities between mobility deserts and non-mobility deserts, with mobility deprivation concentrated in socioeconomically vulnerable communities. By extending the transit desert framework, our approach provides a behaviorally grounded and scalable measure of mobility inequality that captures household mobility constraints. Our further assessment of emerging mobility services, such as bike-sharing systems, suggests that they may provide supplementary mobility options in selected mobility-deprived areas. However, their role is constrained by their tendency to co-locate with existing transit infrastructure rather than extend into the most underserved neighborhoods. Together, the findings underscore the importance of integrated, equity-oriented transportation policies that address structural mobility constraints while supporting environmentally sustainable urban transport systems, contributing to ongoing debates on accessibility, decarbonization, and inclusive transport planning.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762107</guid>
    </item>
    <item>
      <title>Capacity of multimodal public transport systems to mitigate urban heritage island effect: Insights from Wuhan, China</title>
      <link>https://trid.trb.org/View/2762103</link>
      <description><![CDATA[Urban heritage is increasingly threatened by marginalization and isolation amid rapid urbanization, hindering its systematic conservation and adaptive reuse. Rebuilding network linkages between heritage sites is essential to overcome limitations of traditional site-based protection. While public transport is recognized as key to activating the potential of urban heritage, there is a lack of empirical evidence and model-based support regarding the role of multimodal public transport systems in reshaping heritage connectivity. This study integrates topological network analysis with an island-identification model to assess the impact of major public transport systems on the connectivity of heritage nodes in Wuhan—a city heavily reliant on public transit. Specifically, it examines the capacity of the network based on a multimodal public transport system (NMPTS) to mitigate the urban heritage island effect and validates its robustness using percolation theory. The results show that NMPTS significantly enhances heritage connectivity across multiple temporal scenarios and exhibits strong robustness, thereby playing a positive role in alleviating the heritage island effect. Only 8% of heritage nodes remain isolated. The proposed framework quantifies the capacity of multimodal systems to reduce heritage isolation and reveals variations in performance across transport modes and potential risks of centralization. These findings offer insights into promoting heritage equity and sustainability through coordinated optimization of transport systems.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762103</guid>
    </item>
    <item>
      <title>When and where do university-related travelers use ride-hailing? Evidence from 75 universities in Tianjin, China</title>
      <link>https://trid.trb.org/View/2752541</link>
      <description><![CDATA[Ride-hailing has emerged as a primary mobility alternative for university-related travelers, particularly in areas underserved by metro and bus services. However, existing studies mainly targeted the general population, overlooking university-related travelers as a specific user group and neglecting their representative mobility patterns. To address the gaps, this study addresses university-related ride-hailing demand, as well as reveals its spatiotemporal patterns and determinants, utilizing city-scale ride-hailing data with textual addresses across 75 universities in Tianjin, China. Multiple machine learning models were applied to explore the nonlinear relationships between university-related ride-hailing demand and explanatory variables of built environment, transport, and university characteristics across spatiotemporal contexts, with the Gradient Boosting Decision Tree (GBDT) yielding the best predictive performance (R2 = 0.746–0.824). Subsequently, SHAP analysis was employed to determine significant influencing variables and to interpret their contribution patterns to university-related ride-hailing demand. The analysis is systematically conducted within a spatiotemporal comparative framework that spatially distinguishes between university-based origins and destinations, and temporally differentiates weekdays from weekends. Our results reveal that most university-related ride-hailing trips last 10–15 min and cover 4–6 km. Both university-origin and university-destination trips exhibit three daily demand peaks, although university-destination trips show an earlier morning peak and a substantially delayed evening peak. Total population and institutional expenditure emerge as dominant determinants and demonstrate similar saturation effects. Daily activities-related factors largely determine weekday ride-hailing demand, while land-use attributes dominate on weekends. The empirical findings provide valuable insights into designing equitable, efficient, and dynamic ride-hailing services tailored to universities.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752541</guid>
    </item>
    <item>
      <title>Timetable synchronization between bridging bus and subway in response to subway disruption</title>
      <link>https://trid.trb.org/View/2752537</link>
      <description><![CDATA[Coordination between bus bridging services and unaffected subway lines is essential for subway disruption management in megacities around the world. Previous studies have primarily focused either on the spatial design of bus–subway evacuation route networks or on spatiotemporal synchronization between a single disrupted subway line and its parallel bridging routes, thereby overlooking network-level spatiotemporal optimization. This study develops a two-stage stochastic optimization model considering network-level coordination and the uncertainty of passenger demand. In the first stage, the allocation of buses across candidate bridging routes is determined; in the second stage, the departure times of bus bridging services considering synchronization between bus bridging services and subway timetables are optimized. The scenario generation and reduction approach is used to solve the proposed mixed-integer linear program. Conditional Tabular Generative Adversarial Network (CTGAN)–based scenario generation is used to generate demand scenarios, and K-medoids clustering is then employed to reduce the number of scenarios. The proposed method is verified through a case study in Shanghai. The results show that spatiotemporal coordination at the subway–bus network level can reduce the number of bridging bus trips by 18.7% relative to optimizing only the timetables of parallel bus routes. Compared with spatial coordination alone, it also reduces average passenger waiting time by 25.3%. The proposed model may help enhance urban transport system resilience and provide a better passenger experience during metro disruptions.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752537</guid>
    </item>
    <item>
      <title>Space–time accessibility supports participation in after-work leisure activities</title>
      <link>https://trid.trb.org/View/2742987</link>
      <description><![CDATA[Understanding how accessibility shapes participation in leisure activities is central to promoting inclusive and vibrant urban life. Conventional accessibility measures often focus on potential access from fixed home locations, overlooking the constraints and opportunities embedded in daily routines. In this study, we apply a space–time accessibility (STA) metric rooted in the capability approach, capturing feasible leisure opportunities between home and work given a certain time budget, individual transport modes, and urban infrastructure. Using high-resolution GPS data from 2415 working residents in the Paris region, we assess how STA influences leisure participation during weekdays, measured as the diversity of leisure locations visited and activity duration. Observed destination choices confirm that most individuals select leisure locations within their STA-defined opportunity sets, validating the metric as a proxy for capability sets. Structural equation modeling shows that STA exerts a significant positive total effect on leisure participation (β=0.14, p<.001), driven by a significant direct effect (β=0.18, p<.001) that is only modestly offset by an indirect pathway through reduced travel time (β=−0.04, p<.01). Individual attributes also directly shape participation: active mode use and higher education promote leisure engagement, while local poverty and caregiving responsibilities constrain it. These findings highlight the value of person-centered, capability-informed accessibility metrics for understanding inequalities in urban mobility and informing transport planning strategies that expand real freedoms to participate in social life across diverse population groups.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742987</guid>
    </item>
    <item>
      <title>Train scheduling with variable running speeds and multiple train compositions</title>
      <link>https://trid.trb.org/View/2762688</link>
      <description><![CDATA[In recent years, frequent small-scale emergencies, such as extreme weather events, have often led to significant passenger flow fluctuations in metro systems.To better accommodate this fluctuated demand, the operators are required to design irregular timetables where both train arrival/departure times at each station and composition modes for each train service vary dynamically. Accordingly, this paper investigates an integrated train scheduling problem that simultaneously addresses (1) the train running speed optimization strategy that seeks to find the best train trajectories for a series of trains, and (2) the multiple train compositions optimization strategy that provides sufficient transport capacity with the least rolling stock units. To this end, we propose a novel nonlinear mixed integer optimization model for minimizing the total costs of energy consumption and service quality for passengers, where the train running speed is innovatively treated as a decision variable. A conic reformulation technique is proposed, such that the nonlinear parts of the developed model can be reformulated as second-order cone constraints, which can be efficiently handled by commercial solvers. Two computational experiments involving a relatively small- and large-sized cases derived from the subway line 17 in Shanghai are tested to assess the quality of our method. The results suggest that this approach can be used by operators to derive a high-quality energy-efficient train timetable.]]></description>
      <pubDate>Thu, 10 Sep 2026 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762688</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>User-Focused Microtransit for Students’ After-School Activities</title>
      <link>https://trid.trb.org/View/2775441</link>
      <description><![CDATA[This study investigates a user-focused microtransit service designed to transport students to after-school activities. The methodology includes generating a synthetic student population and the corresponding trip requests, determining the routes using a metaheuristic approach to solve a Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPPDTW), and evaluating system performance under a zero-rejection policy. The VRPPDTW includes scheduling drivers with mandatory breaks and maximum shift durations, while minimizing unassigned trips, waiting time, travel time, and route imbalance. The case study focuses on an on-demand transport service for students’ after-school activities in Limassol, Cyprus. Various scenarios are analyzed to assess the effect of fleet size, fare levels, and service parameters on performance. The results show that a fleet of 10 vehicles serving 144 trips for 60 students can reduce the number of cars required by 83%. Larger fleets achieve higher occupancy and better kilometer utilization. Of note, fleets of 20 vehicles or more surpass a system efficiency of one, indicating the distance traveled by the service is lower than individual trips. For service levels, increasing acceptable waiting time by 5 min or travel time by 15 min boosts capacity by 24%. Economically, a zero-rejection policy is sustainable only for large fleets with high fares. However, allowing a small rejection rate of 2% significantly improves viability, enabling the fleet of 10 vehicles to break even with fares 25% lower than local taxi rates.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775441</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>Understanding Influencing Mechanisms of Subway Station-Line Peak Deviation Time: Spatial Econometric Perspective</title>
      <link>https://trid.trb.org/View/2772987</link>
      <description><![CDATA[The peak deviation time (PDT) between subway stations and lines is a critical temporal misalignment in urban rail systems, yet it remains understudied. Accurately understanding PDT is essential for calibrating station-level peak-hour time (PHT) during planning, which directly informs station design ridership and facility configuration. Ignoring PDT can result in persistent supply–demand mismatches, exacerbating platform congestion and impairing operational efficiency. To address this gap, this study develops a spatial econometric framework to examine the spatiotemporal patterns and correlates of PDT. We implement a model selection procedure comparing the spatial lag model (SLM), spatial error model (SEM), and spatial Durbin model (SDM) with multiple network distance-based spatial weight matrices, enabling the characterization of spatial interdependencies among stations. Using multi-source data from the Xi’an subway system, the results, interpreted as conditional associations given the cross-sectional design, show that: (1) PDT exhibits significant spatiotemporal heterogeneity, with the SDM outperforming alternative models in capturing spatial autocorrelation; (2) the correlates of PDT are highly period- and direction-specific, with higher shares of recreational and medical land uses consistently associated with smaller temporal deviations across multiple peak periods, indicating their potential relevance as diagnostic indicators in station-area planning contexts; and (3) significant spatial spillover patterns are observed, with factors such as transport hub land ratio and terminal or transfer station attributes associated with both local and neighboring stations’ PDT. These findings underscore the importance of a network-wide perspective for station facility planning and operational management.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772987</guid>
    </item>
    <item>
      <title>Queen City Metro Media Habits Study</title>
      <link>https://trid.trb.org/View/2742514</link>
      <description><![CDATA[This report summarizes findings of Media Habits research conducted for Queen City Metro (QCM) during November 1978. The purpose of this research is to profile media habits--television viewership, radio listenership, and newspaper readership--among current riders. The objective of this study is to provide media direction in order to more efficiently and effectively utilize QCM communication dollars. Methodology, analytical framework, findings, and recommendations are presented in the report. Data tables and the survey questionnaire are included in the appendix.]]></description>
      <pubDate>Mon, 07 Sep 2026 10:57:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742514</guid>
    </item>
  </channel>
</rss>