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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>
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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>Express Charging Lanes for Electric Vehicle Evacuation</title>
      <link>https://trid.trb.org/View/2701379</link>
      <description><![CDATA[The long-distance evacuation of electric vehicles (EVs) presents significant challenges for disaster management owing to their limited driving range and constrained charging infrastructure. EVs with long charging times can create negative externalities for all other EVs waiting in queues, especially during evacuation scenarios. This study investigates the use of express charging lanes to reduce overall evacuation delays. We propose optimization models to optimize the allocation of charging plugs for express and regular charging, considering both user-equilibrium and system-optimal scenarios. To account for heterogeneities and uncertainties, such as stochastic EV arrival patterns and variable charging demands, we further develop numerical simulation models to quantify the delay distribution. We found that separating EVs with lower charging demand had the potential to minimize total system delay. The proposed models identified the optimal level of express charging plug allocation to minimize the total charging delay without centralized enforcement of traffic distribution. In addition, the models could enable government agencies to estimate the required charging resources to fulfill an evacuation within a given time window. The insights generated by the proposed theoretical models were validated using agent-based simulation, in which uncertainties could be flexibly represented.]]></description>
      <pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701379</guid>
    </item>
    <item>
      <title>Probabilistic modeling of delays for train journeys with transfers</title>
      <link>https://trid.trb.org/View/2643827</link>
      <description><![CDATA[Reliability plays a key role in the experience of a rail traveler. The reliability of journeys involving transfers is affected by the reliability of the transfers and the consequences of missing a transfer, as well as the possible delay of the train used to reach the destination. In this paper, we propose a flexible method to model the reliability of train journeys with any number of transfers. The method combines a transfer reliability model based on gradient boosting, responsible for predicting the reliability of transfers between trains, and a delay model based on probabilistic Bayesian regression, which is used to model train arrival delays. The models are trained on delay data from four Swedish train stations and evaluated on delay data from another two stations, in order to evaluate the generalization performance of the models. We show that the probabilistic delay model, which models train delays following a mixture distribution with two lognormal components, allows to much more realistically model the distribution of actual train delays compared to a standard lognormal model. Finally, we show how these models can be used together to predict the arrival delay distribution at the final destination of the journey. The results indicate that the method accurately predicts the reliability for nine out of ten tested journeys. The method could be used to improve journey planners by providing reliability information to travelers. Further applications include timetable planning and transport modeling.]]></description>
      <pubDate>Wed, 13 May 2026 17:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643827</guid>
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    <item>
      <title>Extended Security Control and Delay Propagation in Air Cargo Transport Operations: Implications for Supply Chain Continuity</title>
      <link>https://trid.trb.org/View/2686204</link>
      <description><![CDATA[Prior research on security controls in air cargo terminals has primarily focused on protecting passengers, crews, and airport infrastructure, while largely overlooking the maintenance of supply chain continuity. The present study addresses this gap by analysing how the configuration, spatial placement, and scheduling of screening procedures affect the stability of cargo flows, as well as the incidence and propagation of delays in air freight operations. Evidence was collected at 2 terminals – a regional facility in southern Poland and a large international terminal in southern Europe, which enabled a comparative assessment that accounts for organisational and structural differences. The analysis mapped screening procedures onto the operational timeline of cargo-handling. Standard screening consisted of radiographic inspection of palletised consignments using an X-ray system. A negative result triggered an extended screening path comprising, in sequence, canine inspection, chemical screening using reactive swabs, and manual inspection of the load unit after opening by a qualified specialist. The total delay was computed as the sum of the times associated with the additional screening steps and the waiting time for the substitute uplift. Findings for 2022–2024 indicate pronounced differences between terminals in both the scale and effectiveness of controls. At the regional terminal, 3…6% of shipments were rout-ed to extended screening, the average duration of additional actions was 1…2 h, and the final delay was 14…20 h. At the international terminal, the corresponding values were 12…15%, 5…7 h, and 84…95 h. The most significant delays were generated by procedures requiring external specialists, such as crate-opening technicians, and by the organisation of replacement transport. Where specialist support was provided periodically, the waiting time for inspection could reach up to 7 days, whereas smaller facilities operated with near-immediate response times. Based on these results, several operational improvements are indicated. Recommended actions include maintaining specialists on-call, issuing immediate notifications of adverse X-ray outcomes to planning teams, and selectively automating repetitive steps. Implementing these measures is expected to reduce inspection-related delays, improve on-time delivery performance, and enhance the resilience of air cargo supply chains.]]></description>
      <pubDate>Tue, 12 May 2026 16:56:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686204</guid>
    </item>
    <item>
      <title>Border Crossing Delay Cost Analysis: Integration of Various Data Sources in the Direct Cost Estimation Tool</title>
      <link>https://trid.trb.org/View/2697866</link>
      <description><![CDATA[While previous studies have estimated border delay costs at specific locations or for limited vehicle types, there has been a lack of comprehensive, U.S.-wide tools that integrate multiple data sources to quantify direct economic impacts for both commercial and passenger vehicles. This study presents the findings of the Direct Cost Estimation Tool (DCET), a comprehensive framework for quantifying the direct economic impact of delays at U.S. land ports of entry. The research integrates multiple data sources to calculate delay costs for both commercial and passenger vehicles at 49 major border crossings. The methodology employs an approach that considers commodity-specific costs for commercial vehicles and value of time calculations for passenger drivers and passengers. Using 2024 data, the analysis reveals that, for U.S.-bound traffic at the selected crossings, border delays cost more than $1.5 billion annually, with $337 million attributed to commercial vehicles and $1.25 billion to privately owned vehicles. California experienced the highest passenger delay costs (58% of the national total), while Texas accounted for the largest share of commercial vehicle delay costs (61%). DCET serves as a valuable decision support tool for transportation planners, policymakers, carriers, and shippers to evaluate infrastructure investments and operational improvements at international border crossings. By quantifying these costs, stakeholders can better understand the economic implications of border inefficiencies and make data-driven decisions to enhance cross-border transportation.]]></description>
      <pubDate>Tue, 05 May 2026 10:16:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697866</guid>
    </item>
    <item>
      <title>A comparison of the accumulation-based, trip-based and time delay macroscopic fundamental diagram models</title>
      <link>https://trid.trb.org/View/2663003</link>
      <description><![CDATA[Macroscopic fundamental diagram (MFD) is widely applied in network modelling and management, such as route guidance and vehicle relocation, which are formulated as generalised dynamic traffic assignment (DTA) problems. MFD can effectively reduce the spatial dimension thus making the generalised DTA problems computationally efficient. In the literature, three MFD models, the accumulation-based model, the trip-based model, and the time delay model, were proposed to capture the traffic flow propagation under different traffic conditions and demand scenarios. However, no consensus has been reached on their computational efficiency and which model should be chosen under certain traffic conditions and demand scenarios. In this paper, we revisit these models regarding two important theoretical properties regarding flow propagation in the DTA, i.e. the first-in-first-out (FIFO) principle and causality. Corresponding dynamic network loading algorithms are designed to compare their numerical accuracy and computational efficiency. Numerical comparisons with Lighthill-Whitham-Richards (LWR) model and a micro simulator confirm that the accumulation-based model is valid in saturation, the trip-based model is valid in free-flow, while the time delay model provides a good approximation in both free-flow and saturation scenarios. On the other hand, violation of strict causality is observed in the accumulation-based and trip-based models, rendering it hard to pursue analytical DTA. This issue is not observed in the time delay model. Overall, the time delay model is a promising alternative for dynamic network loading in large-scale network applications.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663003</guid>
    </item>
    <item>
      <title>A synchronization-constraints-based dual bands method of traffic signal optimization</title>
      <link>https://trid.trb.org/View/2662999</link>
      <description><![CDATA[This paper presents a synchronization-constraints-based dual bands method of traffic signal optimization for multiple traffic flows. The synchronization coefficient, traffic volume ratio, synchronization benefit, and their thresholds of traffic flow pair are proposed to select synchronized traffic flow pairs for progression. Then, progression bands of multiple traffic flows can be achieved by merging time windows, combining equivalent phase, and optimizing benefits evaluation index of the synchronized traffic flow pair. The proposed method adopts step-by-step optimization processing, and the combinatorial optimization of signal cycle, phase sequence, and offset is decomposed by performing synchronization and progression. A real-world case is presented to illustrate the application of the proposed method with VISSIM simulations. The results show that compared with the MULTIBAND model and TRANSYT, this method significantly decreased the average delay and average stops along the arterial of the entire traffic system composed of buses and cars.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662999</guid>
    </item>
    <item>
      <title>Time-weighted ensemble long–short-term memory for delay prediction in railway systems</title>
      <link>https://trid.trb.org/View/2689397</link>
      <description><![CDATA[Railway systems are complex and consist of many subsystems working together to ensure smooth and timely operation. Delays that cause deviations from the scheduled operations can lead to cascading issues throughout the railway network. These delays can be a result of a large number of causes, from adverse weather to random equipment failure. Delay prediction is crucial in the mitigation of delay effects. This paper proposes a data-driven approach to the prediction of railway delays. Applying Recursive Neural Networks in this data-driven approach allows it to exploit the sequential nature of train operations without having to make intermediate predictions common in event-driven approaches. Raw scheduling data are processed to compute features relevant to the analysis of delay and its propagation. In the proposed method, each row of data is weighted according to its temporal distance to the prediction horizon, assigning increased importance to more recent events. These time-weighted data are analysed at different scales via the component long–short-term memory (LSTM) models in the proposed ensemble model. Data of a 1-year time period from the Historical Service Records of the British Railway were used to train and test the proposed time-weighted ensemble LSTM architecture. The proposed architecture showed an improved performance compared to other data-driven models implemented as benchmarks, outperforming them by achieving a root mean squared error of 0.27 min, a mean absolute error of 0.17 min as well as a coefficient of determination (R2) value of 0.9875.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689397</guid>
    </item>
    <item>
      <title>Mitigating General Transit Feed Specification Message Delays Using Time Series Prediction for Transit Signal Priority</title>
      <link>https://trid.trb.org/View/2696109</link>
      <description><![CDATA[As a preferential treatment at signalized intersections, Transit Signal Priority (TSP) remains a key technology for enhancing transit performance. Recently, TSP systems based on General Transit Feed Specification (GTFS) Realtime have gained traction in the market, mainly because of their low implementation and maintenance costs. However, leveraging GTFS Realtime messages for TSP presents significant challenges, particularly because of two types of message delays: (1) high latency; and (2) long update intervals. Building on previous work that introduced regression models to compensate for message latency, three new machine learning models are proposed to more accurately predict future vehicle locations while mitigating these delays. To overcome the limitations of earlier regression approaches, a long short-term memory architecture for single-step prediction was developed, a long short-term memory architecture for multistep prediction was developed, and a Transformer-based architecture for multistep time series prediction was developed, which can address interval updating issues. The experimental results show that all three proposed models significantly outperform both previous regression models and five baseline statistical methods. These advancements improve the reliability and accuracy of GTFS-based Automatic Vehicle Location, reinforcing its role as a dependable data source for cloud-based TSP systems.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:19:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696109</guid>
    </item>
    <item>
      <title>Delay Measurement and Analysis of Pedestrian Behavior</title>
      <link>https://trid.trb.org/View/2671571</link>
      <description><![CDATA[Understanding pedestrian behavior and its interaction with transportation infrastructure is crucial for ensuring safe and efficient urban environments. This study presents a comprehensive analysis of delay measurement techniques and their correlation with pedestrian behavior within the context of urban mobility systems. The study begins by reviewing existing literature on delay measurement methodologies, encompassing traditional approaches and emerging technologies such as GPS tracking and computer vision. It identifies key metrics for delay measurement, including travel time, waiting time at intersections, and overall journey delay. Furthermore, the study examines various factors influencing pedestrian behavior, ranging from individual characteristics such as age and gender to environmental factors such as weather conditions and built environment features. It explores how these factors impact pedestrian movement patterns, decision-making processes, and overall travel experience. The analysis integrates delay measurement techniques with observations of pedestrian behavior to identify correlations and causal relationships. It investigates how variations in delay affect pedestrian choices, such as route selection, crossing behavior, and mode of transportation. Additionally, it explores the role of infrastructure design and traffic management strategies in mitigating delays and enhancing pedestrian safety and satisfaction. The findings of this study contribute to the development of effective urban planning and transportation policies aimed at improving pedestrian mobility and enhancing the overall quality of urban life. By integrating delay measurement with insights into pedestrian behavior, policymakers and urban planners can design more pedestrian-friendly environments and optimize transportation systems to better serve the needs of all road users.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671571</guid>
    </item>
    <item>
      <title>Modeling train arrival variability: Methodological approaches and data-driven insights for railway systems</title>
      <link>https://trid.trb.org/View/2682781</link>
      <description><![CDATA[This study presents a statistical analysis of train delays in the Swedish railway system. The focus of the study is to identify the best-fitting probability distributions for train arrival times across different stations and travel directions. Using the Kolmogorov-Smirnov (K-S) test, we evaluate the goodness of fit for common distributions—gamma, log-normal, and inverse Gaussian—to capture delay patterns at ten stations. Our findings reveal significant variability across stations, with the log-normal distribution providing the best fit for 70% of cases. However, some stations exhibited direction-specific deviations, emphasizing the need for localized analysis. Traditionally, train delays in Sweden have been assumed to be uniformly distributed across the network, an oversimplification frequently used in generating synthetic datasets for AI-based timetable rescheduling systems. This study challenges that assumption, demonstrating that delay distributions vary by station and direction. By incorporating station- and direction-specific modeling, our results contribute to the development of more accurate synthetic datasets. These insights support data-driven approaches to predictive modeling, operational efficiency improvements, and increased reliability in railway networks. Based on the best-fitting distributions identified through statistical testing, we generate synthetic data using maximum likelihood estimates and direct sampling. Our study systematically assesses the distributional characteristics of train arrivals across stations and directions in the southern Swedish railway network, aiming both to understand operational variability and to generate realistic synthetic data for AI-based rescheduling. Building on this analysis, our method produces datasets that preserve the statistical characteristics of real train delays, ensuring they are more suitable for training and evaluating AI-based rescheduling algorithms.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682781</guid>
    </item>
    <item>
      <title>Integrating demand data with train delay models: A socio-economic evaluation for maintenance planning</title>
      <link>https://trid.trb.org/View/2682118</link>
      <description><![CDATA[Railway punctuality remains a critical measure of service quality and operational efficiency. Traditional performance metrics, such as on-time performance and delay increments, inform about punctuality goals and guide maintenance planning, but they often overlook the passenger experience due to limited access to disaggregated demand data. This study integrates forecasted ridership data with delay evaluation models to assess passenger delays and their socio-economic impacts. By combining passenger-centric delay contributions with the Swedish framework for socio-economic evaluations, we enable a more informed prioritisation of maintenance interventions. A case study on the Southern Main Line in Sweden illustrates the methodology’s potential to improve maintenance planning, highlighting its relevance for achieving data-driven improvements in train service reliability.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682118</guid>
    </item>
    <item>
      <title>Agent-based simulation of passenger-centric disruption management for multimodal airport access</title>
      <link>https://trid.trb.org/View/2682098</link>
      <description><![CDATA[Efficient and seamless airport access is a critical yet often overlooked process of airport operations. Strong connectivity, especially during disruption periods, significantly reduces passenger delays and potential revenue losses. Tackling these challenges demands coordinated disruption management strategies. To that end, we model coordination in a system comprising two traffic orchestrators, each responsible for managing their respective domains: airside and landside. The airside orchestrator can implement tactical flight delays, while the landside orchestrator can apply rerouting to assist passengers at-risk of missing their flights. Through negotiation between these orchestrators, the approach aims to minimize missed flights and passenger delays, while also exploring a fair distribution of costs. The negotiation process is structured using a game-theoretic framework, and an agent-based simulation is used to evaluate the effects on airport operations. A case study demonstrates the effectiveness of these measures in enhancing airport operations while balancing costs.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682098</guid>
    </item>
    <item>
      <title>Real-Time Information-Based Combined Control Method for Bus Delay</title>
      <link>https://trid.trb.org/View/2646842</link>
      <description><![CDATA[As environmental pollution and energy consumption become increasingly serious concerns, more cities are opting for electric buses over traditional fuel buses. However, the stability and reliability of electric buses during operation are challenged by the unpredictability of traffic flow and passenger demand. Factors such as traffic congestion, weather conditions, and fluctuations in passenger numbers can compromise the punctuality of electric bus services, often resulting in delays. To address these challenges, a real-time dual-objective bus control model for mixed traffic scenarios has been proposed. This model aims to minimize both passenger time costs and company operational costs. Factors such as intersections and traffic flow are also considered. A combined control strategy, including speed control and a backup bus replacement strategy, has been proposed. Speed control is specifically aimed at managing intersection delays, allowing buses to adjust their speeds to pass through intersections optimally between queue dissipation and the end of the green-light period. The backup bus replacement strategy, on the other hand, is implemented at bus terminals, where a backup bus replaces a delayed one to maintain the schedule. A heuristic algorithm based on Particle Swarm Optimization (PSO) is incorporated into the model, enhancing its effectiveness by iteratively updating the positions and velocities of particles in the search space. Harbin City Road 96 was selected as a case study for model validation. In the off-peak case scenario, schedule deviation was reduced by 89% through the implementation of the proposed speed control strategy. Additionally, passenger waiting time was reduced by 8%, and passenger travel time was reduced by 14%. In the peak case scenario, the proposed control strategy effectively eliminated bus departure delays originating within the bus system. These results demonstrate the potential of the proposed model to significantly enhance the reliability and stability of public transportation systems, thereby improving the overall quality of public transport services.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646842</guid>
    </item>
    <item>
      <title>Formulation and Evaluation of Rail Transit Passenger Influx Control Schemes Based on Train-Passenger-Station Interactive Simulation</title>
      <link>https://trid.trb.org/View/2632984</link>
      <description><![CDATA[For the safe functioning of rail transit systems, effective management of passenger flow is crucial. Nevertheless, existing formulation models are inadequate for simulating the entire passenger travel process due to their reliance on simple factors. Meanwhile, the strategy for controlling passenger influx generally concentrates solely on the entrance gate, which restricts its impact. To guarantee the safety and dependability of station operations,fwe this research proposes a method for formulating and evaluating passenger influx control schemes for rail transit stations based on interactive simulations involving trains, passengers, and stations. Firstly, based on converted line-level passenger flow data, simulation intelligent agents for trains, passengers, and stations are constructed, and constraints of train capacity, station capacity, train interval, and arrival/departure time are presented. Then, the behavior and interactions of intelligent agents are described in detail, considering the diverse types of passenger flow. As a result, the micro travel simulation of passengers and passenger flow change display within the rail transit system are achieved. Next, a rolling adjustment method based on simulation results (RAM-BSR) is suggested to formulate and evaluate the passenger influx control schemes. Finally, the train delay of Shanghai Metro Line 13 on a certain workday is taken as a case study, where various passenger influx control schemes are comprehensively evaluated, validating the availability and reliability of the suggested simulation and formulation approach. The research results can well recreate the passengers’ travel process, formulate passenger influx control schemes, and provide rolling evaluation for the developed schemes.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632984</guid>
    </item>
    <item>
      <title>Determinants of the willingness to accept compensation for flight delays by low-cost airlines</title>
      <link>https://trid.trb.org/View/2663752</link>
      <description><![CDATA[Flight delays are one of the most common problems in the aviation industry. This study examines the determinants of the willingness to accept compensation offered by low-cost airlines for delayed flights. The willingness to accept using the contingent valuation method is used to investigate the amount of compensation for long delays before departure. Hypothetical scenarios involving flight delays are generated, and the payment card method is used to determine the range of starting bid values. Double-bounded dichotomous choice survey data, a bivariate probit model, and data collected from the Taoyuan International Airport in Taiwan are used to estimate compensation equations. The results indicate that the willingness to accept compensation is associated with sex, age, personal income, the purpose of the trip, nationality of the passengers, and preferences for alternative travel options. As the compensation offer increases, the probability that passengers will accept it also increases. The mean values of willingness to accept compensation range from US $34–$89 for a four-hour delay to US $131–$200 for an eight-hour delay. These estimates align with existing provisions, such as the JetBlue Airways customer protection program.]]></description>
      <pubDate>Fri, 24 Apr 2026 08:55:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663752</guid>
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