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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
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    <item>
      <title>Research on the Connection of Through Trains Based on the Deadline of Goods Transportation</title>
      <link>https://trid.trb.org/View/2581367</link>
      <description><![CDATA[The deadline for goods delivery represents a critical milestone in railway freight transportation, reflecting the railway’s unwavering commitment to shippers. Prompt delivery is not only a matter of punctuality but also a testament to the industry’s dedication to maintaining high-quality service and competitiveness. Especially in the complex realm of through train operations, where trains seamlessly transition between stations and operational sections, timely delivery becomes an even more critical factor. This study, centered on the concept of streamlined integration, delves into the nuances of two pivotal scenarios: the intricate connection of through trains at a single technical station and the complex interlinking across multiple operational sections. By formulating 0–1 programming models that incorporate practical constraints like operating time standards and delivery time limits, we aim to optimize these scenarios and enhance the efficiency of railway freight transportation. Through validation using data from existing literature examples, we demonstrate the practical significance of our approach in improving the overall performance of the railway industry.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581367</guid>
    </item>
    <item>
      <title>Modeling Crowdedness at Public Transport Stations During Special Events: A Comparative Study of Eleven Cities</title>
      <link>https://trid.trb.org/View/2611284</link>
      <description><![CDATA[Special events significantly disrupt normal crowding patterns in urban public transport (PT) systems, yet our understanding of these impacts remains limited. Accurate prediction of crowdedness during special events could enhance PT passenger flow forecasting when integrated into unified frameworks, ultimately improving operations and crowd management—critical components of PT demand management. This study models PT station crowding patterns during planned special events using publicly available opportunistic data, specifically leveraging the Google Popular Times (GPT) popularity index as a proxy for station-level crowdedness. The extensive spatiotemporal coverage of GPT data in urban areas provides a high-quality data source for modeling station crowdedness and inferring trip generation and attraction capabilities of the surrounding areas. To capture the temporal dynamics specific to special events, we propose a time interval index that encodes event-driven variations in crowdedness. Recognizing PT networks’ natural graph structure, we developed a graph neural network augmented with an attention mechanism and a positional embedding-enhanced temporal convolutional network model, named APT-GCN, for station crowdedness prediction. We evaluated our model using football matches as case studies, conducting comparative analyses across 15 clubs in 11 cities. The results demonstrated the model’s precision in forecasting station crowdedness, with the event indicator substantially improving prediction performance during special events.]]></description>
      <pubDate>Thu, 23 Oct 2025 13:11:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611284</guid>
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    <item>
      <title>The Simulation Analysis of Passenger Evacuation in One Metro Station Based on Exodus</title>
      <link>https://trid.trb.org/View/2203754</link>
      <description><![CDATA[In Shanghai, the Metro is a major part of a modern urban transit system. Essential to the safe operation of the metro station is the design for emergency evacuations. Based on the comparative study of different simulation theories and methods of group evacuation behavior, Exodus V4.06 and Smartfire V4.1 were employed to simulate the evacuation under different risks in one metro station in Shanghai. The computation result shows that people's distribution in the station has significant influence on the evacuation time and the exits used; the intervention on evacuation route, such as the proper guidance from the working staff, is essential to ensure the right choice of evacuation route, evacuation time saving, and personnel safety.]]></description>
      <pubDate>Wed, 15 Oct 2025 09:36:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203754</guid>
    </item>
    <item>
      <title>Operational Resilience Evaluation of a Comprehensive Transportation Hub System Based on the SEM-AEWM-Extension Cloud Model</title>
      <link>https://trid.trb.org/View/2596696</link>
      <description><![CDATA[To reduce the safety risks in the operation of large comprehensive transportation hubs and ensure efficient orderly operation, this study introduces the concept of resilience into operational management based on the WSR (“Wu-Shi-Ren”) theory. Existing methods, such as the Delphi method, literature survey, and field survey, primarily construct an evaluation index system for the comprehensive transportation hub operation system from a resilience perspective. Combining structural equation modeling (SEM) with the anti-entropy weighting method (AEWM) based on game theory, this paper employs extension cloud theory to accurately assess the resilience levels of the hub operation system. The obstacle degree model is introduced to analyze the factors constraining the enhancement of hub operational resilience. Taking Xi’an North Railway Station as an example, the results show that the operational resilience of the station has increased annually from 2014 to 2023, elevating from lower resilience (level II) to higher resilience (level IV). Passenger flow and environmental factors are recognized as the key constraints on the improvement of hub operational resilience. Finally, optimization measures are proposed to provide theoretical support and a decision-making reference for guiding the resilience operation of the Xi’an North Railway Station, as well as other large comprehensive transportation hubs domestically and in other countries.]]></description>
      <pubDate>Wed, 10 Sep 2025 17:06:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596696</guid>
    </item>
    <item>
      <title>Features in Calculating the Operating Standards Non-linearly Related to the Station Activity Size</title>
      <link>https://trid.trb.org/View/2407849</link>
      <description><![CDATA[The article is devoted to the consideration of possible methods of operating standards’ calculation, non-linearly related to the station activity size. To determine the simplest method and evaluate the reliability of the calculation results obtained with its use. The paper outlines two approximate methods for taking into account fluctuations in the work content and throughput in determining the standards of the station activity. According to the proposed methods, calculations of the average values of wait time of unpacking and formation of trains at the marshal yard. Comparison of the calculation results showed that the differences in the obtained values of wait time for unpacking and formation of trains are insignificant. In view of this, a simpler method of accounting for instability in the transport process compared in the article is recommended for such calculations. The use of the method recommended in the article will significantly simplify the calculations of the operational standard, nonlinearly related to the station activity size.]]></description>
      <pubDate>Tue, 22 Jul 2025 10:32:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407849</guid>
    </item>
    <item>
      <title>Adaptive Federated Learning Framework for Predicting EV Charging Stations Occupancy</title>
      <link>https://trid.trb.org/View/2550320</link>
      <description><![CDATA[Forecasting the occupancy of electric vehicle (EV) charging stations is crucial for addressing key challenges in e-mobility, including charging inefficiency, traffic congestion, and drivers’ difficulty in locating available stations. Recent studies on predicting charging station occupancy have predominantly employed conventional centralized machine learning models. However, these models struggle to adapt to evolving data streams, limiting their accuracy and practical applicability. Furthermore, centralized approaches exacerbate these limitations by conflicting with privacy requirements, as they necessitate sharing sensitive data from charging stations and users. To address these limitations, this study introduces a novel federated learning framework designed to collaboratively predict EV charging station occupancy by leveraging data from all participating stations without sharing any raw data. The framework addresses critical challenges, including concept drift, data heterogeneity among clients, and model generalization. It clusters stations with similar drift patterns and leverages personalized models, updated using incremental learning, to ensure adaptive and precise forecasting. This adaptability allows the system to respond to dynamic environments characterized by changing traffic patterns, user behaviors, and station utilization trends. Three datasets with diverse characteristics were used to validate the proposed framework, demonstrating its robustness, adaptability, and scalability in improving forecasting accuracy. This work provides a robust, privacy-preserving, and scalable framework to enhance the reliability and efficiency of EV infrastructures, promoting broader adoption of sustainable transportation.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550320</guid>
    </item>
    <item>
      <title>Relational Analysis of User Density, Number of Train Services and Population around Rail Stations on Local Railways in Japan</title>
      <link>https://trid.trb.org/View/2493189</link>
      <description><![CDATA[In recent years, the number of cities aiming at compact cities that centers on public transportation and concentrate residence and urban functions around them has increased. However, there are very few cities that are actively promoting policies to improve the convenience of railways, which should play a central role in such cities. It is possible that there are some relationships between the convenience of railways and change in urban structure. Therefore, this study examines the influence of local railways on urban structure by analyzing changes in the user density overtime, the number of train services and population around rail stations, and their mutual relationship, focusing on railway and tramway in local cities. In addition, local railways and tramways have been classified by two business operators, and the relationships between the user density, the number of train services, and the population around rail stations have been analyzed and examined by business operators.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493189</guid>
    </item>
    <item>
      <title>Development of Public Transit Measures to Mitigate the Impact of COVID-19 on Pedestrians and Station Performance using PTV Vissim Simulation</title>
      <link>https://trid.trb.org/View/2493182</link>
      <description><![CDATA[In this study the authors explored the possible changes in passenger behavior on transit stations due to disruptions such as COVID-19 and the impact on station performance. A trade-off is observed between the reduced risk of virus transmission through increased physical distancing and subsequent negative impact on a station's performance. To evaluate this trade-of, a simulation model of Marlborough station in Calgary, Canada was developed using PTV Vissim. Passenger behavioral changes were implemented by manipulating the Social Force Model (SFM) parameters within the simulation model. The impact from these changes were measured by the developed model and was simultaneously validated with the theoretical expectations derived from equations on the SFM parameters. Alternative station designs were simulated and tested to allow separated flow of passengers in different parts of station such as pedestrian bridges and stairways. The results from the study found that pedestrian physical distancing had a profound negative impact on the transit station's performance. However, these effects can be addressed through simple low-cost station modifications. Ultimately, the results of this study can be used as a reference for transit authorities to develop mitigation strategies against possible resurgences of COVID-19 or other infectious diseases.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2493182</guid>
    </item>
    <item>
      <title>Planning charging stations for mixed docked and dockless operations of shared electric micromobility systems</title>
      <link>https://trid.trb.org/View/2491511</link>
      <description><![CDATA[Dockless electric micro-mobility services (e.g., shared e-scooters and e-bikes) have been increasingly popular in the recent decade, and a variety of charging technologies have emerged for these services. The use of charging stations, to/from which service vehicles are transported by the riders for charging, poses as a promising approach because it reduces the need for dedicated staff or contractors. However, unique challenges also arise, as it introduces docked vehicles at these stations to the existing dockless systems, and now riders can pick up and drop off e-scooters at both random locations and fixed charging stations. This requires incentives for riders to drop off vehicles at the stations and management strategies to efficiently utilize the vehicles at the stations. This paper focuses on such mixed operations of docked and dockless e-scooters as an example. It develops a new aspatial queuing network model for vehicle sharing and charging to capture the steady-state e-scooter service cycles, battery consumption and charging processes, and the associated pricing and management mechanisms in a region with uniform demand. Building upon this model, a system of closed-form equations is formulated and incorporated into a constrained nonlinear program to optimize the deployment of the service fleet, the design of charging stations (i.e., number, location, and capacity), user-based charging price promotions and priorities, and repositioning truck operations (i.e., headway and truck load). The proposed queuing network model is found to match very well with agent-based simulations. It is applied to a series of numerical experiments to draw insights into the optimal designs and the system performance. The numerical results reveal strong advantages of using charging stations for shared dockless electric micro-mobility services as compared to state-of-the-art alternatives. The proposed model can also be used to analyze other micromobility services and other charging approaches.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491511</guid>
    </item>
    <item>
      <title>Development of Building Information Model–Enabled Facility Management for Metro Rail Stations</title>
      <link>https://trid.trb.org/View/2485210</link>
      <description><![CDATA[The facility management (FM) of the metro rail is a complex and challenging task due to numerous station facilities, their operational interdependencies, and limited historical maintenance data. While there have been a few studies on building information modeling (BIM) implementation for metro rail projects (MRPs), BIM–enabled FM for MRP studies are still limited. This study aims to develop a BIM–enabled FM workflow to transform the traditional project delivery process (TPDP) into a digital project delivery process (DPDP) for MRP leveraging open standards from the Industry Foundation Class (IFC) as a data exchange format. The workflow developed was validated using a case study of a metro rail station and its application was evaluated. The findings of the evaluation highlight that the workflow developed was highly applicable for maintenance tracking and document management of station facilities. The main contribution of this research is that the workflow developed addresses the challenges associated with TPDP, enables digital data transfer and interoperability, and enhances facility information retrieval and bidirectional communication within the BIM–enabled FM platform.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485210</guid>
    </item>
    <item>
      <title>Simulation Analysis to the Operation of Station Track on High Speed Railway</title>
      <link>https://trid.trb.org/View/2264089</link>
      <description><![CDATA[In order to ensure each station has enough capacity for train reception and departure with limited number of tracks, the operation of station track needs to be optimized. According to the characteristics of station operation, the system of train reception and departure can be regarded as a queuing system. Through analyzing the work process of queuing system, the approach to work out track use plan is designed. Traffic disorder is an important factor affecting station track operation, how to adjust track use plan when disorders occur is further studied to check the dynamic capacity of station track. At last, at the background of the proposed Jing-Hu high-speed railway (hereinafter referred to HSR), this paper makes a lot of simulation analysis to the number of station track based on the optimal station operation, and proposes some valuable suggestions to the design of station track.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264089</guid>
    </item>
    <item>
      <title>Punctuality of Train Traffic in Dutch Railway Stations</title>
      <link>https://trid.trb.org/View/2264083</link>
      <description><![CDATA[Punctuality is a critical performance measure for passenger train services. A statistical analysis has been performed of train traffic at The Hague HS station, the Netherlands. In general, the arrival punctuality level is rather well, but the train delays increase for most lines at this station. Late arrival delays and excess dwell times generally fit well to an exponential and a normal distribution respectively. A stochastic delay propagation model is presented, which predicts reasonably well the departure punctuality level of late arrival trains at the station.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264083</guid>
    </item>
    <item>
      <title>Computation of Capacity at Marshalling Yard under Imbalanced Transportation Circumstance</title>
      <link>https://trid.trb.org/View/2264081</link>
      <description><![CDATA[This article examines factors that affect the capacity of marshalling yards after train speed increase in China, and finds out that the major factor is the imbalanced characteristic of freight train flows. According to the arrival and departure regularities of the freight train flows, a method of breaking a day into the busy, the free and the ordinary parts is presented. In addition, by using other research results for reference, a new system based on the changed operations and the devices factor is proposed to describe the capacity of marshalling yard. The computation result of Jinanxi marshalling yard shows that this method provides a more accurate and feasible way to analyze the capacity of marshalling yard.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264081</guid>
    </item>
    <item>
      <title>Vehicle routing in one-way carsharing service with ridesharing options: A variable neighborhood search algorithm</title>
      <link>https://trid.trb.org/View/2483459</link>
      <description><![CDATA[The widespread adoption of one-way carsharing systems faces a significant hurdle in the form of vehicle imbalance. To address this challenge, this study proposes a novel hybrid operator-user-based relocation scheme that integrates one-way carsharing systems with ridesharing options, enabling users to complete their trips by sharing carsharing vehicles with others. This integration necessitates the concurrent optimization of vehicle relocation routing and ridesharing matching strategies by carsharing operators. The underlying problem, termed the vehicle relocation and ridesharing matching problem, is formulated as a mixed-integer linear program with the objective of maximizing total system profit. Given the NP-hard nature of the vehicle relocation and ridesharing matching problem, a variable neighborhood search algorithm is developed for its solution. The algorithm incorporates an efficient route evaluation scheme to improve the efficiency of the algorithm. Numerical experiments demonstrate that the proposed solution method is capable of producing high-quality solutions within short computing time. The authors also show that the mutual benefits of the proposed integrated scheme for both carsharing operators and users, including increased profitability, reduced travel costs, and improved service quality.]]></description>
      <pubDate>Mon, 27 Jan 2025 15:39:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2483459</guid>
    </item>
    <item>
      <title>A Structured Data Resource Integration Approach Leveraging Data Graph for Subway Station Equipment Deployment and Maintenance</title>
      <link>https://trid.trb.org/View/2475629</link>
      <description><![CDATA[The judicious utilization and timely maintenance of subway station equipment are paramount for ensuring high-quality service to passengers and maintaining daily operations. Traditional method causes superfluous employment of equipment during off-peak hours, engendering augmented potential energy consumption and escalating maintenance expenditures. Ergo, an intelligent collaborative strategy for equipment deployment and maintenance is necessary. The conundrum lies in discerning the intrinsic synergies amongst equipment in their deployment and maintenance spheres. To this end, this study provided a Data Resource Integration approach predicated upon Data Graph (DG). Employing a changed 5W2H analytical framework, the authors retraced the impact of operational objectives to unearth these intrinsic connections. The authors also employed the time division optimization scheme of Automatic Gate Machine (AGM) as a case, which wants to satisfy changing subway ticket checking demands with passenger flow, and modeled its operation strategy, retroactively retraced its objectives via the 5W2H framework, and architected a DG-based integration model. The case model was stored in the Neoj4 graph database. The feasibility of the proposed method is substantiated, harnessing Structured Data Resource Integration as a conduit to refine and guide real-world operations.]]></description>
      <pubDate>Mon, 23 Dec 2024 10:37:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475629</guid>
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