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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Temporal instability of highway pedestrian crash severity: Comparative analysis of machine learning models</title>
      <link>https://trid.trb.org/View/2681700</link>
      <description><![CDATA[Globally, pedestrian-involved crashes on national highways are considered unacceptable due to the clearly defined functional hierarchy of roadways. This study utilizes advanced machine learning (ML) techniques to investigate the severity of pedestrian injuries in traffic crashes, providing a comprehensive analysis of factors influencing crash severity across evening peak and other hours from 2016 to 2023. Statistical analysis reveals a higher fatality rate during evening peak hours (6:00–9:00P.M.) than other time periods. A chi-square test confirms a statistically significant difference in severity proportions between these periods (p < 0.01), supporting the concept of temporal instability—where the influence of contributing factors may vary over time. The study further evaluates the predictive performance of five ML algorithms: Support Vector Machine (SVM), Gradient Boosting (GB), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost). The best-performing model is interpreted using SHapley Additive exPlanations (SHAP), offering transparent insights into the relative importance of key predictors. CatBoost’s results highlight temporal differences in crash frequency and contributing factors, emphasizing the need to account for time-sensitive variations in crash severity. They highlight the importance of incorporating temporal dynamics into risk assessments and demonstrate the value of interpretable ML tools for guiding targeted, time-specific pedestrian safety interventions. This approach provides a robust foundation for developing data-driven policies that better address the complexities of pedestrian crash severity in varying temporal conditions.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:41:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681700</guid>
    </item>
    <item>
      <title>Deep Reinforcement Learning Based on Spatiotemporal Information for Network-Wide Traffic Signal Coordination Control</title>
      <link>https://trid.trb.org/View/2658722</link>
      <description><![CDATA[Graph neural network-based deep reinforcement learning (GNN-DRL) algorithms have been widely applied in traffic signal coordination control of urban road networks. However, current traffic signal control methods based on GNN-DRL focus on integrating historical and current spatiotemporal data to represent features of traffic networks, without utilizing predicted future information to enhance traffic efficiency. This work proposes a novel spatiotemporal information-based deep reinforcement learning method for traffic signal coordination control of urban road networks. It implements a heterogeneous subgraph representation method to model spatial structures among closely related intersections, strengthening subgraph feature representations while reducing computational complexity. Additionally, a multi-scale spatiotemporal heterogeneous graph feature aggregation technique is designed. The proposed method incorporates traffic signal timing scheme, vehicle states and road network topology as graph node features. By applying a graph neural network, it captures multistep spatiotemporal information from historical, current, and predicted data, thereby enhancing network feature representation and foresight. Furthermore, a novel reward function is designed to perceive the spatiotemporal information of a road network. The function uses the betweenness centrality to evaluate the spatial importance of intersections. It introduces total number of vehicles and predicted traffic flow to dynamically assess the current traffic state and future traffic demand in the lanes. It improves the agent’s ability to perceive and use spatiotemporal information to make decisions. We evaluated our proposed method through experiments under three different traffic scenarios: low, medium, and high flows. The results clearly demonstrate that the proposed method outperforms existing state-of-the-art methods, by reducing average queue length by 34.12%-59.45%, maximum queue length by 25.31%-47.83%, lane occupancy rates by 27.22%-51.56%, and vehicle count by 27.29%-51.92%. Meanwhile, experiments on computational overhead and real-road networks further confirm that SIDRL offers advantages in terms of low cost and high performance. This presents new technical insights for the real-time deployment and resource optimization of urban traffic signal control.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658722</guid>
    </item>
    <item>
      <title>Multi-period hub-and-spoke network design considering flow-dependent economies of scale</title>
      <link>https://trid.trb.org/View/2649627</link>
      <description><![CDATA[This study explores a novel and generalized uncapacitated hub location problem (HLP) by integrating time-dependent decisions and flow-dependent economies of scale into the design of multi-allocation hub-and-spoke networks. Substantial investment and long-term strategic planning are required to construct such networks, during which market share often fluctuates significantly out of external factors like global economic dynamics and government policies. Flexible decision-making still lacks sufficient theoretical guidance despite its recognized importance in the timely deployment of hubs and the effective adjustment of transportation routes. Instead of a one-time implementation, this study investigates a budget-constrained phased network design that incorporates flow-dependent economies of scale to improve economic efficiency. This model captures the cost-sharing effects on inter-hub arcs as flow demand fluctuates across time periods. A mixed-integer linear programming model with a piecewise-linear concave cost function is formulated for the problem, posing a computational difficulty. To solve this challenging model, a specialized Benders decomposition algorithm is developed, which incorporates the Benders multi-cuts technique, the idea of approximate Pareto-optimal cuts, and a rolling horizon heuristic strategy. Through extensive numerical experiments, the proposed algorithm outperforms benchmark approaches. Findings include: (1) the impacts of budget and economies of scale are more significant in longer planning horizons but gradually diminish; (2) the flow-dependent approach enables more effective route consolidation and greater cost savings through economies of scale, an advantage that becomes more apparent over time.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649627</guid>
    </item>
    <item>
      <title>Assessing the spatially heterogeneous transportation impacts of recurrent flooding in the Hampton roads region on auto accessibility</title>
      <link>https://trid.trb.org/View/2622274</link>
      <description><![CDATA[Recurrent flooding has increased rapidly in coastal regions due to sea level rise and climate change. A key metric for evaluating transportation system degradation is accessibility, yet the lack of temporally and spatially disaggregate data means that the impact of recurrent flooding on accessibility—and hence transportation system performance—is not well understood. Using crowdsourced WAZE flood incident data from the Hampton Roads region in Virginia, this study examines changes in the roadway network accessibility for travelers residing in 1,113 traffic analysis zones (TAZs) across five time-of-day periods. Additionally, a social vulnerability index framework is developed to understand the socioeconomic characteristics of TAZs that experience high accessibility reduction under recurrent flooding. Results show that TAZs experience the most accessibility reduction under recurrent flooding during the morning peak period (6 to 9am) with large differences across different zones, ranging from 0 % to 49.6 % for work trips (with population-weighted mean reduction of 1.71 %) and 0 % to 87.9 % for non-work trips (with population-weighted mean reduction of 0.81 %). Furthermore, the social vulnerability analysis showed that zones with higher percentages of lower socio-economic status, unemployed, less educated, and limited English proficiency residents experience greater accessibility reduction for work trips. In contrast to previous studies that aggregate the effects of recurrent flooding across a city, these results demonstrate that there exists large spatial and temporal variation in recurrent flooding’s impacts on accessibility. This study also highlights the need to include social vulnerability analysis in assessing impacts of climate events, to ensure equitable outcomes as investments are made to create resilient transportation infrastructure.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:57:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622274</guid>
    </item>
    <item>
      <title>Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation</title>
      <link>https://trid.trb.org/View/2553458</link>
      <description><![CDATA[In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time overhead, some models based on the low-rank completion only embed the temporal information and discard the spatial information, thereby resulting in low imputation accuracy; 3) Missing traffic data carries complex spatio-temporal information, requiring multi-faceted analysis to extract valuable insights. To address these issues, the authors propose an efficient fourth-order dimension preserved tensor completion (FDPTC) with temporal constraint model. It works based on the proposed fourth-order dimension preserved (FDP) tensor decomposition model to capture the spatio-temporal information of traffic data from a high-dimensional perspective by extending dimensions. Additionally, the authors embed a temporal constraint into FDP tensor decomposition model to ensure consistency of MTD and introduce a non-negative constraint to accelerate convergence speed. By constructing this model, the authors successfully avoid direct operations on the extra information matrix/tensor, thereby optimizing efficiency. Experimental results on four real traffic datasets demonstrate that the proposed model achieves significantly higher imputation accuracy at an affordable computational burden compared with state-of-the-art models.]]></description>
      <pubDate>Mon, 29 Sep 2025 08:35:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553458</guid>
    </item>
    <item>
      <title>Experimental Analysis of the Temporal Variability of the V2V Channel at 5.9 GHz</title>
      <link>https://trid.trb.org/View/2553397</link>
      <description><![CDATA[Vehicle-to-vehicle (V2V) wireless communication systems are crucial for intelligent transportation applications such as traffic load control, autonomous vehicles, and collision avoidance. Therefore, it is essential to thoroughly understand the realistic V2V channel propagation conditions to develop suitable V2V communication systems and standards. However, due to high costs, most existing V2V studies focus on characterizing the vehicular channel theoretically and comparing it with simulation data or studying the empirical V2V channel in a specific scenario. In this work, the authors investigate the time variability of the received signal based on experimental data collected in an extensive measurement campaign at 5.9 GHz in a large set of propagation environments: urban-high traffic density, urban-low traffic density, suburban, highway, and rural environments. The time-variability of the received signal is characterized by time and frequency. The relationship between coherence time and the terminal velocity, coherence time and transmitter-receiver separation distance, and the relationship between Doppler spread and both effective velocity and separation distance, have been investigated. Furthermore, the correlation between coherence time and separation distance and effective velocity was investigated, as well as the correlation between Doppler spread and separation distance and effective velocity. The results provide a deeper understanding of the temporal variability of the channel and how it is influenced by factors such as traffic density, unique/multiple driving directions, terminal speed, separation distance, characteristics of the propagation environment, and visibility conditions. This knowledge is crucial for developing vehicular communication systems and can be applied to design strategies that reduce temporal variability in received signals.]]></description>
      <pubDate>Fri, 26 Sep 2025 09:06:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553397</guid>
    </item>
    <item>
      <title>A spatio-temporal node-place-ridership model for classifying metro station areas: The case of Shenzhen, China</title>
      <link>https://trid.trb.org/View/2586776</link>
      <description><![CDATA[The node-place model has been widely applied for uncovering the coordination between transit network and land use but overlooks the critical role of ridership and its temporal variations. Focusing on the dynamic nature of urban activities and ridership, this study develops a spatio-temporal node-place-ridership model for evaluating and classifying metro station areas. The extended model emphasizes ridership as a third dimension in addition to the node and place dimensions and focuses on intra-week (weekday versus weekend) and intra-day (day-time versus night-time) temporal variations. Using a case study in Shenzhen, China, results show that ridership is more associated with the place values (i.e., land-use pattern) than with the node values (i.e., network accessibility). The variation in ridership between weekday and weekend is related to non-work activities and land-use types. As for intra-day variation, station areas with a high proportion of commuting ridership face imbalance between node and place values and between job and housing functions. This study highlights the importance of the incorporation of ridership dynamics in understanding the transit and land-use integration and assists urban planners and policymakers in making more informed, flexible, and responsive urban development strategies. The extended model is transferable and valuable for other cities.]]></description>
      <pubDate>Wed, 17 Sep 2025 08:28:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2586776</guid>
    </item>
    <item>
      <title>Measuring the readiness to become a “30 km/h city”</title>
      <link>https://trid.trb.org/View/2571318</link>
      <description><![CDATA[This paper proposes a macroscopic analysis to evaluate the “readiness” of cities to limit the street speed to 30 km/h. Floating Car Data (FCD) from anonymous Global Positioning System (GPS)-based measurements are analysed for the urban areas of the major Italian cities to investigate the actual vehicle speed distributions. Ad-hoc indicators based on the average speed in the road network, the percentage of the network length with an average travel speed lower than 30 km/h, the average travel time comparing peak and off-peak hours are computed and compared for the selected cities. Analysing the percentage increase in travel times compared to free-flow speeds reveals a clear distinction between cities with uniform travel patterns and those with highly variable travel times. Results show how, for some cities, the average vehicle speed is below 30 km/h in the majority of road segments even during off-peak hours, so reductions of speed limits for road traffic would not significantly penalise the actual travel times, while for other cities a significant discrepancy in the detected speed exists between peak and off-peak hours, suggesting that low speed values are due to traffic congestion rather than street design and widespread traffic calming measures may be needed to reduce drivers’ speed. This exploratory study could represent a supporting tool for decision-makers and traffic planners to identify, prioritize and monitor the effectiveness of traffic calming measures and speed limits.]]></description>
      <pubDate>Wed, 10 Sep 2025 09:22:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571318</guid>
    </item>
    <item>
      <title>Identifying representative weeks: A clustering analysis of urban dynamics based on public transport data</title>
      <link>https://trid.trb.org/View/2562595</link>
      <description><![CDATA[Transportation is key to understanding urban dynamics, with public transport bus systems shaping mobility patterns, accessibility, and activity, especially in developing countries where this mode accounts for a significant share of trips. Passive data like GPS and smart card records can reveal these patterns when properly processed. This study proposes a clustering-based methodology to analyze public transport bus data collected over an arbitrarily long period (at least a week) to identify time blocks with similar dynamics without predefined structures. If the dynamics for each time block in a week is the same over time, a representative week can be constructed, showing the most likely dynamics for each time block in a week. The analysis considers three dimensions: demand (passenger counts), supply (distance traveled by buses), and level of service (bus speeds). Cluster results generate a representative week in terms of mobility indicators and transport operations, enabling analysis and comparison of dynamics across different city zones. Using data from Santiago de Chile's bus system for August 2019 and April 2020, the methodology was applied to 10 city zones. Results highlighted distinct dynamics across zones and the need to incorporate all three dimensions for representative weeks. Regular application of this approach is crucial, as cluster characteristics evolve over time. While promising venues for future development remain, this methodology provides a flexible, robust data-driven foundation for understanding urban transport dynamics, adaptable to different cities and supporting evidence-based decisions.]]></description>
      <pubDate>Tue, 12 Aug 2025 16:21:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562595</guid>
    </item>
    <item>
      <title>Evaluating efficiency determinants of rural bus routes in northern Taiwan: A network DEA with temporal data aggregation</title>
      <link>https://trid.trb.org/View/2573565</link>
      <description><![CDATA[This study evaluates the production, service, and overall operational efficiency of 19 rural bus routes in northern Taiwan, focusing on the challenges of delivering transport services in remote and low-density areas. A two-stage analytical framework was adopted. In the first stage, multi-period Network Data Envelopment Analysis (NDEA) was employed to assess stage-specific efficiencies across time. Particular attention was given to ensuring consistency between data-level and efficiency-level aggregation, addressing a critical methodological issue in multi-period efficiency measurement. In the second stage, Bootstrap Truncated Regression (BTR) was applied to examine the influence of external factors beyond conventional inputs and outputs. The findings identify route length, operating mode, and taxi service integration as significant determinants of efficiency. Further, quadrant-based importance-performance analysis reveals critical mismatches between route efficiency and strategic relevance, offering a basis for targeted improvement. This study provides actionable recommendations for policymakers and transit operators, including optimizing route design to reduce inefficiencies, integrating flexible transport modes, tailoring subsidy mechanisms, and institutionalizing multi-period evaluation frameworks to support data-driven governance in rural transportation systems.]]></description>
      <pubDate>Wed, 23 Jul 2025 09:15:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573565</guid>
    </item>
    <item>
      <title>Periodic low emission zones: balancing urban transport efficiency and reducing traffic emissions</title>
      <link>https://trid.trb.org/View/2540221</link>
      <description><![CDATA[The study aimed to examine how implementing the operation of the low emission zone (LEZ) in Łódź for specific time periods would affect the reduction of CO emissions and changes in the load on the urban road transport system. The LEZ was analysed in two spatial variants for the year 2032, with restrictions introduced at specific times during the day. Multi-motivational macroscale traffic models for selected parts of the road network were built to achieve the adopted objective. The impact of the LEZ on the equilibrium of the urban road transport system was determined through changes in: road network load, average speed on the network under load, travel time and distance. It was found that activation of the LEZ during the two traffic peaks in Łódź may help reduce pollution, thus producing beneficial effects for those residing within the zone. However, strategies should be considered to reduce the resultant load on the network in the areas outside the zone, e.g., by promoting means of transport alternative to the car or by reducing the mobility of the population, e.g., by encouraging remote working. If traffic were allowed to shift to other areas of the city, this would deteriorate the quality of life for residents there.]]></description>
      <pubDate>Mon, 09 Jun 2025 14:48:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540221</guid>
    </item>
    <item>
      <title>Development of Transportation Processes in Russia as a Basis for Making Management Decisions</title>
      <link>https://trid.trb.org/View/2407722</link>
      <description><![CDATA[The study of the features and patterns of transport processes occurring at the present time urgently requires knowledge and understanding of the operation of the transport complex as a whole and its elements in the previous periods of its development. Management decisions in transport for the implementation of transportation are also mainly explained by the dynamics of these indicators. The purpose of the study is to display the functioning of the main land transport modes, their role and importance in the implementation of the transportation process in different time periods. Also, to a large extent, it reveals the role of these types of transport in the economic complex in various accounting periods of the country's development. The key research methods were comparison and correlation, as the most acceptable methods in the calculation and analytical activities to identify and improve the existing features of the transportation process in the transport complex of Russia. The paper displays the performance indicators of the main land transport modes – rail, road and intracity, as well as pipeline transport, the routes of which are laid mainly across the territory of the country. Based on specific statistical material, the significance of these types of transport in the implementation of transportation in different time periods of the historical development of Russia is revealed and shown. The results of the study, presented graphically and in tabular form, confirm the reliability of the initial materials of the study and can serve as a scientific and practical basis for making management decisions.]]></description>
      <pubDate>Wed, 19 Mar 2025 10:12:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407722</guid>
    </item>
    <item>
      <title>Relationship Between Activities and Multimodal Travel in Everyday Life</title>
      <link>https://trid.trb.org/View/2493108</link>
      <description><![CDATA[Multimodal travel behavior plays a pivotal role in sustainable transport infrastructure design. Unlocking its potential requires a deeper understanding of the underlying mechanisms governing everyday travel. In this study, the authors investigate the association between activity variability and multimodal travel behavior in Germany using data from the German Mobility Panel. Through descriptive analyses and regression modeling, the authors explore the activity-related characteristics and contextual factors influencing the adoption of multimodal travel among employees. The authors' findings reveal that using multiple transport modes positively correlates with engaging in diverse activities. Notably, leisure and shopping activities exhibit a powerful influence on multimodal travel behavior. Moreover, complex travel needs, as indicated by high variations in distances traveled and a more significant number of linked trips, act as additional drivers of multimodal behavior. Furthermore, the authors' results suggest that multimodal travel behavior is more prominent during the transition from weekdays to weekends. These findings contribute to understanding multimodal travel patterns and can inform the development of strategies to promote sustainable and efficient transport systems.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493108</guid>
    </item>
    <item>
      <title>A study on sorting strategies in marshaling yards with a limited number of tracks and track capacity</title>
      <link>https://trid.trb.org/View/2480566</link>
      <description><![CDATA[In marshaling yards, freight wagons are sorted from inbound trains to outbound trains for further transport. To organize an efficient shunting process, sorting strategies are proposed in the literature. The application of sorting strategies is generally restricted by the number of classification tracks and their lengths. This can lead to difficult-to-implement or even inoperative sorting plans. To handle limited track capacity, the authors decompose the shunting process into a series of consecutive periods of time resembling timetables of inbound trains. A heuristic is used in every period to decide on the postponement of inbound trains when track capacity is scarce. This way, sorting strategies become applicable on a rolling time basis. A strategy is said to solve a shunting task when it enables building all outbound trains within a given time horizon. The authors examine the performance of five well-known sorting strategies for a large set of shunting tasks within a computational study. The simulation results indicate that the sorting strategies perform differently when numbers and lengths of classification tracks vary. In conclusion, the authors are able to determine the most reliable strategy among the set of considered sorting strategies for a marshaling yard of a certain size.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480566</guid>
    </item>
    <item>
      <title>Unraveling spatiotemporal dynamics of ridesharing potential: Nonlinear effects of the built environment</title>
      <link>https://trid.trb.org/View/2491449</link>
      <description><![CDATA[Ridesharing is a crucial component in optimizing urban mobility by alleviating traffic congestion and reducing emissions. However, the ridesharing potential remains underutilized, and the distribution characteristics of this potential have yet to be well quantified. This study leverages the shareability network method and interpretable machine learning techniques, utilizing high-precision taxi trajectory data, to analyze the spatiotemporal patterns of ridesharing potential in Shanghai and to elucidate the spatiotemporal dynamics of the factors influencing it. The findings reveal that ridesharing potential is highest during evening peaks, followed by weekends, and lowest during morning peaks, which contrasts with typical residential travel patterns. The results highlight the nonlinear, spatiotemporal heterogeneity of built environment impacts on ridesharing potential, with significant variations in the importance of residential, employment, and leisure factors across different time periods. These insights provide valuable guidance for urban planners and transportation network companies in enhancing operational efficiency and effectively promoting ridesharing.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:11:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491449</guid>
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