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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>Unveiling the impacts of on-demand transit service on ridership and its growth</title>
      <link>https://trid.trb.org/View/2697867</link>
      <description><![CDATA[On-demand transit has demonstrated promising potential to tackle challenges related to extended wait times, connectivity gaps, and operational inefficiencies, particularly in low-density urban environments. From the passengers’ perspectives, the service has the potential to improve accessibility with reduced travel times and transfers, whereas transit agencies can benefit from the opportunity of improvement in ridership and operations. As such, this study proposes a framework to analyze the impacts of on-demand transit on ridership and its trend leveraging a combination of transit activities, land use and operation data. The case study is conducted with a multi-year pilot program of on-demand transit service operated in the City of Regina, Canada considering nighttime services and low-demand transit stops. The impacts of on-demand transit on ridership at stop level as well as the cross-year growth are assessed with difference-in-difference methods for both the overall transit network and different land use zones including residential, commercial and public service areas. Density and distance of various amenities within the transit stops are also considered such as food, school and health amenities. Results provide evidence of positive relationships between ridership and on-demand transit service on the entire transit network, while the benefits vary in different land use zones. Results also show increasing impacts of the service on ridership across years which contribute to the ridership growth. The developed method and findings can provide planning and policy references for both the existing service evaluation and long-term development for on-demand transit.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697867</guid>
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    <item>
      <title>Scheduling passenger and freight co-modal transport for a high-speed rail reservation platform: a column-and-row generation-based modular framework</title>
      <link>https://trid.trb.org/View/2725264</link>
      <description><![CDATA[High-speed rail (HSR) corridors operating at high frequencies within urban agglomerations often exhibit uneven temporal distribution of passenger volume and imbalanced passenger flows along train trajectories, creating opportunities to expand freight services. To leverage the capacity, we redesign the off-peak ticketing mechanism into a time-slot-based HSR reservation (TRS) platform, through which both passengers and carriers reserve travel sections and receive exact departure times after scheduling. The problem is formulated as a two-layer decision model that integrates line planning and scheduling: the first decision level aligns reservation demand with train stop patterns, while the second decision level refines detailed schedules by accounting for differences in train speeds and priorities. A column-and-row generation–based framework decomposes the problem into modular “carriage-train-schedule” components, dynamically generating candidate carriages and violated constraints to approximate feasible train routes. Numerical experiments across 47 scenarios on both a small-scale corridor and the Yangtze River Delta HSR network show that the proposed algorithm solves five-station instances using only 9.7% of the CPU time required by the Gurobi solver. It also efficiently handles large-scale 37-station network cases involving over 38,065 passenger trips and 2,907 tons of freight in total. Furthermore, under a “weak” passenger-priority principle, TRS enables efficient co-modal transport by increasing operating revenue by 4.88% and passenger seat utilization by 13.05%, while delay penalties account for less than 1% of total revenue. At the same time, operating multi-mode trains during off-peak periods does not result in severe network congestion.]]></description>
      <pubDate>Wed, 22 Jul 2026 09:07:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725264</guid>
    </item>
    <item>
      <title>Late-night and early-morning train scheduling with non-traffic hour maintenance window in urban rail transit systems</title>
      <link>https://trid.trb.org/View/2689850</link>
      <description><![CDATA[Regular maintenance during non-traffic hours (NTH) is vital for the resilience of urban rail transit (URT) systems, yet an insufficient NTH maintenance window poses a challenge for URT systems in various cities. For instance, the Hong Kong MTR Corporation has noted that the required NTH maintenance time often exceeds the available window, prompting service adjustments such as earlier late-night closures and/or later early-morning starts. To address this challenge, this study develops an optimal scheduling framework that links late-night and early-morning URT services through the NTH maintenance window requirement to maximize public welfare. A Decoupled Optimization Model (DOM) first derives closed-form solutions for the number of train services and each train headway in both periods by relaxing the NTH maintenance window constraint. These results determine the last and first train departure times, service duration, and the available NTH maintenance window, accordingly. Building on the DOM, a Coupled Adjustment Model (CAM) addresses cases where the required NTH maintenance window exceeds the available NTH maintenance window by adjusting train services through four schemes: canceling services or reducing headways in either period. Analytical results and numerical experiments for the Chengdu Metro show that optimal headways increase (decrease) over time with declining (rising) passenger demand during the late-night (early-morning) period, and this pattern persists after service duration adjustment. Service cancellation (headway reduction) is more effective when the NTH shortage gap is large (small). The framework generates optimal schedules for any feasible exogenous NTH maintenance window requirement, offering practical guidance for URT operations and integrating the timetabling of the last-train and first-train services.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689850</guid>
    </item>
    <item>
      <title>Understanding off-hour deliveries in cities: a critical review of determinants and impacts</title>
      <link>https://trid.trb.org/View/2701247</link>
      <description><![CDATA[Freight delivery vehicles in urban areas have both advantages and disadvantages: they serve the urban economy but also contribute to significant negative externalities, including but not limited to environmental pollution and traffic congestion. To mitigate their negative impacts, the concept of off-hour delivery (OHD), which shifts delivery vehicles from daytime to nighttime, is gaining popularity worldwide. For successful implementation of the strategy and more informed decision-making by public sectors, this paper reviews academic studies on (a) determinants of OHD including business attributes, logistical factors, policy instruments, and stakeholder perspectives and (b) its impacts on traffic, energy and emissions, and operations. As a result, the review highlights the need for carefully designed OHD programmes that account for key determinants such as receiver participation and the role of incentives. The evidence confirms that OHD can substantially improve traffic conditions (e.g. increases in truck speeds of up to 200% and reductions in travel times of up to 52%) and reduce energy use and emissions (fuel savings of up to 17% and emission reductions of up to 70%), while noting the importance of mitigating potential nighttime noise. The review also identifies critical knowledge gaps, including limited empirical evidence beyond simulation studies and the need to assess OHD within the context of emerging trends such as electrification and home deliveries.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701247</guid>
    </item>
    <item>
      <title>Communications Reliability for Vehicle Grid Integration</title>
      <link>https://trid.trb.org/View/2685474</link>
      <description><![CDATA[Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.]]></description>
      <pubDate>Mon, 06 Apr 2026 16:59:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685474</guid>
    </item>
    <item>
      <title>Urban logistics, consolidation, and collaborative governance: Insights from a Malmö stakeholder meeting</title>
      <link>https://trid.trb.org/View/2684201</link>
      <description><![CDATA[Urban freight transport is undergoing rapid transformation as cities seek to reduce congestion, emissions, and operational inefficiencies while maintaining reliable supply chains and increased city attractiveness. Consolidation solutions, micro-hubs, and off-peak delivery systems are increasingly highlighted as promising strategies for achieving these objectives. Against this backdrop, researchers, property owners, logistics providers, and representatives from Malmö municipality convened for a roundtable meeting to discuss practical challenges and emerging opportunities for improving last-mile distribution. The meeting was held on December 4, 2025, at Malmö University in cooperation with CFFF (centrum för fastighetsföretagande) and TMG (transport and mobility research group). The meeting opened with a presentation by Jack Lu on recent findings from the HITS program (hållbara & integrerade urbana transportsystem) in Stockholm, followed by a panel discussion involving industry practitioners and public-sector representatives. This report synthesizes the key insights from both the presentation and the subsequent dialogue.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684201</guid>
    </item>
    <item>
      <title>A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption</title>
      <link>https://trid.trb.org/View/2663821</link>
      <description><![CDATA[Urban freight transport faces significant decarbonization pressure, yet existing strategies such as freight pooling and electric truck adoption often struggle with limited uptake due to operational complexities, costs, and infrastructure challenges. Critically, current research lacks an integrated, operational incentive framework specifically designed for multi-stakeholder participation in urban crowdsourced logistics, where task-level operational decisions across multiple stakeholders play a central role in system-level carbon reduction. This study introduces a Carbon Reduction Incentive Model (CRIM) that addresses this gap. The CRIM incentivizes individual shippers and independent carriers within a crowdsourced logistics system by assigning task-level rewards for freight pooling and electric truck usage. Rewards are quantified by tonne-kilometer savings relative to conventional individual diesel deliveries, further adjusted by a time-based factor to encourage off-peak operations. The CRIM is embedded within an enhanced pick-up and delivery model that explicitly accounts for stakeholder cost components, vehicle heterogeneity, charging requirements, and time-sensitive feasibility (PDPTW-HEC). To optimize the system’s complex trade-off between costs and carbon emissions, a customized heuristic algorithm is developed. Scenario-based case studies using real-world data and international carbon accounting standards validate the proposed incentive model’s performance. Results demonstrate that CRIM can achieve 9.5–38.1% higher electric truck adoption and an 8.4–28.7% reduction in total carbon emissions. This framework offers a practical and scalable approach for designing and evaluating task-level carbon reduction incentives in urban freight operations.]]></description>
      <pubDate>Thu, 19 Mar 2026 08:57:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663821</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes- Phase 1</title>
      <link>https://trid.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Investigating the Impact of Temporal and Directional Traffic Distribution on Crash Frequencies</title>
      <link>https://trid.trb.org/View/2562202</link>
      <description><![CDATA[Safety Performance Functions (SPFs) are mathematical models that establish relationships between the frequency of various crash types and site-specific characteristics, serving as essential tools for traffic safety analysis and roadway design. Traditional SPFs, however, often overlook the temporal fluctuations in traffic flow (such as peak-hour surges) and directional imbalances between opposing traffic streams. These traffic patterns can exacerbate congestion, disrupt driver behavior, and create unexpected conflict points, potentially leading to increased crash frequencies and more severe accidents. In light of this gap, this study aims to explore the potential of incorporating K-factors (representing peak-hour traffic proportions) and D-factors (reflecting the imbalance of directional traffic) into the development of SPFs to assess whether these factors can effectively represent the impact of temporal and spatial traffic distribution on roadway safety. Using crash data from Pennsylvania urban-suburban collector roadways, it is found that the D-factor plays a significant role in predicting the frequency of total crashes, fatal + injury crashes, and angle crashes, with positive coefficient signs indicating that higher directional imbalances correspond to increased crash risks. Similarly, the K-factor emerges as a critical predictor for fatal + injury crashes and rear-end crashes, with negative coefficients suggesting that a more pronounced traffic peak is associated with a reduction in expected crash frequencies. These results highlight the importance of accounting for uneven traffic distribution in both time and direction when developing SPFs, offering deeper insights into crash patterns and supporting more effective safety interventions and roadway designs.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562202</guid>
    </item>
    <item>
      <title>A Real-Life Data-Based Temporal Analysis of Transit Accessibility in Fresno City</title>
      <link>https://trid.trb.org/View/2562128</link>
      <description><![CDATA[Public transportation is essential for connecting people to services, but accessibility often varies throughout the day, creating barriers for users. This study examines temporal variations in transit accessibility in Fresno City, California (as a case study), focusing on differences between peak and off-peak hours. Using data from 338 census block groups, we analyzed metrics such as transit access rates, travel times, walking distances, and transit-to-driving travel time ratios. Data was collected and processed using APIs and Python libraries. The analysis highlights significant accessibility fluctuations during off-peak hours, affecting transit reliability and convenience. These findings provide planners and policymakers with actionable insights to address temporal disparities, helping design a more consistent and equitable transit system that meets community needs.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562128</guid>
    </item>
    <item>
      <title>Can railway station areas become inclusive 24-h urban spaces? A chronotopic approach with empirical evidence from China</title>
      <link>https://trid.trb.org/View/2636409</link>
      <description><![CDATA[Many railway stations operate around the clock, particularly in densely populated regions where transport capacity remains inadequate. Yet planning practice still treats them as purely spatial entities, overlooking temporal dynamics, which risks overestimating theiravailability and efficiency. Framing railway station areas as chronotopes, this study applies Lefebvre's rhythmanalysis to capture 24-h space-time–behavior interactions and evaluate the 24-h inclusiveness. Based on a survey of ten major hubs in China, we first construct a typology of four train operational patterns that function as domain pacemakers. Then, through an in-depth case study of Nanjing Station Area, we uncover the misalignment between institutional/service rhythms and lived behaviors. The findings reveal that the station's rhythmic conflicts are not mere scheduling errors, but manifestations of three deeper systemic contradictions: 1) the logic of “node” and “place”, 2) the decoupling of national and local governance, and 3) the unequal power in temporal framing. To address these contradictions, we propose a set of planning implications. Strategically, we call for a paradigm shift in planning along three axes: from optimizing spatial allocation to coordinating rhythmic relationships; from pursuing universal synchronization to fostering differentiated coordination; and from a provision-oriented to a capability-oriented logic. Tactically, we offer a hierarchical toolkit of Rhythmic Choreographies with a Rhythmic Negotiation Framework to facilitate stakeholder coordination. The chronotopic framework enriches transport-geography theory on space-time convergence and offers guidance for urban planners and policymakers to pursue 24-h urban inclusiveness.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:53:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636409</guid>
    </item>
    <item>
      <title>Partnering with transportation network companies (TNCs) for low-demand service: is it viable and beneficial for transit agencies?</title>
      <link>https://trid.trb.org/View/2606313</link>
      <description><![CDATA[In low-demand areas or during off-peak hours, fixed-route bus services often show low productivity when maintaining regular headways. Reducing headways further decreases service quality, presenting a challenge for transit agencies. This paper proposes a novel approach to solve the low productivity issue, by forming a partnership between transit agencies and Transportation Network Companies (TNCs), where TNC vehicles substitute fixed-route buses in certain segments. The study introduces a decision-making framework to help transit agencies assess when and where such partnerships are operationally feasible and financially sustainable. It identifies key factors that influence the viability of TNC substitution, including vehicle hours, mileage, and passenger loads. Based on these factors, the paper explores various compensation schemes and determines the cost of TNC operations—a critical component of the framework. The proposed framework is evaluated using a real-world case study in Long Beach, California, USA. Findings suggest that in low-demand scenarios, specifically when the number of passengers per stop is fewer than 0.5, replacing buses with TNC services reduces operating costs. The results also indicate that transit agencies should consider both cost savings and passenger experience while making substitution decisions, as truncating longer route segments may yield lower savings but may improve service for additional passengers. Overall, this research provides valuable insights for transit practitioners seeking to reduce expenses and enhance service quality in low-demand areas and off-peak hours through innovative public–private partnerships with TNCs.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606313</guid>
    </item>
    <item>
      <title>The Texas Congestion Index: Concept and Methodology</title>
      <link>https://trid.trb.org/View/2582671</link>
      <description><![CDATA[The metropolitan transportation organizations in the eight largest metro regions of Texas and the Texas Department of Transportation jointly developed a process to investigate the congestion resulting from a range of investment levels. Part of this process, the Texas Metropolitan Mobility Plan, was the development of a set of performance measures. The measures are based on calculations from the long-range planning model output data. The same procedures used in air quality analyses are applied to congestion estimates. The measures are based on travel time information and comparisons with free-flow travel speeds. The Texas Congestion Index is a comparison of the travel condition during the peak period to the travel time in free flow. The procedures provide a method to estimate benefits from a range of operational treatments to provide a more robust set of performance measures. A set of spreadsheets and training materials are also available.]]></description>
      <pubDate>Sun, 30 Nov 2025 16:39:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582671</guid>
    </item>
    <item>
      <title>Novel Method for Optimizing Emergency Response Facility Layouts in Gas Pipeline Networks</title>
      <link>https://trid.trb.org/View/2570628</link>
      <description><![CDATA[Effective management of gas accidents requires the rapid deployment of responders and resources from emergency response facilities to accident sites. In this study, a three-level optimization methodology of emergency response facility layout in a gas pipeline network is proposed. A set covering model is firstly established to determine the number of emergency response facilities. Meanwhile, the coverage regions are adjusted based on response timeliness, workload balance, and pipeline integrity. Then, a theoretical optimization model is developed to minimize the distance between the emergency response facilities and the gas accident hot spots (i.e., historical emergency points and high-consequence points). Finally, an adaptability evaluation model is constructed to determine the actual layout of the emergency response facilities according to the comprehensive index system, the analytic hierarchy process (AHP), and the technique for order preference by similarity to the ideal solution (TOPSIS). This methodology is applied to a typical gas pipeline network located in Chengdu, China. The results showed that the number of emergency response facilities could be reduced from 21 to 5 with a coverage radius of 13 km. It indicates that the emergency response costs could be decreased from the original CNY 5.25 million to 1.25 million per year, a reduction of 76.2%. In addition, the maximum response times of five different emergency response facility during the off-peak traffic period are 28, 26, 30, 29, and 23 min, respectively, and those during the peak traffic period are 42, 33, 41, 36, and 31 min, respectively, which could both be acceptable. Consequently, this method could greatly optimize the number, efficiency, costs, and loss prevention capability of emergency response facilities. It could further be applied to other fields, such as oil, mining, and chemical engineering.]]></description>
      <pubDate>Fri, 24 Oct 2025 16:53:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570628</guid>
    </item>
    <item>
      <title>Method for Calculating Adaptability between Capability Supply and Passenger Demand in Urban Rail Transit</title>
      <link>https://trid.trb.org/View/2608151</link>
      <description><![CDATA[The adaptability between capacity supply and passenger demand in urban rail transit is decreasing because of capability supply not accounting for the unbalanced spatial and temporal distribution of passenger flow. It is necessary to study a method for calculating adaptability to scientifically identify the spatial and temporal inconsistencies between capacity supply and passenger demand. We defined adaptability as consisting of four indicators indexes: the average load factor of the line, which is used to measure the level of capacity utilization, and the average ride comfort, the total number of people retention at the station, and the average waiting time, which are used to measure the level of passenger service. The criteria importance through intercriteria correlation (CRITIC)-technique for order preference by similarity to ideal solution (TOPSIS) method is used for a comprehensive assessment of adaptability. To calculate the above four indicators, a passenger-train interaction model is constructed to simulate the interaction process between passengers and trains. Validation is done with weekday train schedules and passenger flow data in the northbound direction of a line. Results indicate that the morning peak hours (6:00–7:00 and 9:00–10:00) and the evening peak hours (16:00–19:00 and 19:00–20:00) have an adaptation degree above 60%, indicating better adaptability. Other periods have an adaptation degree below 60%, indicating poorer adaptability. The paper proposes response strategies for the morning peak hours with lower adaptability, including adjusting train intervals and short turn mode, to improve adaptability between capacity and passenger flow.]]></description>
      <pubDate>Mon, 13 Oct 2025 11:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608151</guid>
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