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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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    <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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      <title>Discrepancies in Pedestrian Crossing of Static vs. Dynamic Crowds: An Experimental Study</title>
      <link>https://trid.trb.org/View/2633422</link>
      <description><![CDATA[In this paper, we investigate disparities in pedestrian crossing behaviors within static and dynamic crowds through experimental analysis. First, qualitative trajectory observations revealed significant behavioral differences. To quantitatively assess these discrepancies, we introduced a metric termed the swarm factor. In static contexts, limited variations in speed and swarm factor were observed, which may contribute to the formation of cross-channels, a phenomenon of pedestrian self-organization (tactical level). In contrast, speed and swarm factor exhibited inverse synchronization in dynamic contexts, indicating density-dependent behavioral adaptation (operational level). Finally, orthogonal velocity analysis demonstrated a fundamental pattern in crossing motions: as global density increased, both instantaneous velocity and crossing velocity decreased, while transverse velocity remained stable.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633422</guid>
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    <item>
      <title>Applying hybrid dimension reduction and econometric model to investigate rider behaviors in roadway departure motorcycle crashes</title>
      <link>https://trid.trb.org/View/2633421</link>
      <description><![CDATA[This study analyzed 3,234 motorcycle roadway‑departure crashes recorded in Louisiana from 2017 to 2021. The dataset spans crash, vehicle, environmental, and location variables, enabling a comprehensive assessment of factors shaping injury severity. We used a two‑stage hybrid approach: cluster correspondence analysis first reduces dimensionality and uncovers latent patterns, then random parameters ordered probit models are fitted to the full sample and to each cluster. Six distinct clusters emerge, and the models capture both main effects and important interaction effects through random and fixed parameters. The combined method improves explanatory power and highlights high‑risk crash profiles, offering clear guidance for targeted countermeasures aimed at reducing fatal and serious injuries among Louisiana motorcyclists involved in roadway‑departure events.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633421</guid>
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    <item>
      <title>Optimal location planning for battery exchange and charging stations for electric scooters in leisure areas considering stochastic user dwell times</title>
      <link>https://trid.trb.org/View/2633420</link>
      <description><![CDATA[Stochastic factors significantly affect the optimal planning for locating electric scooter charging stations and scheduling battery exchanges in real operations. A plan that does not account for stochastic factors might turn out to be overly optimistic when implemented in real-world operations, where such stochastic disturbances are common. In this study, we apply mathematical programming and network flow techniques to develop a model for determining the optimal locations of battery exchange and charging stations for electric scooters, while accounting for stochastic user dwell times in leisure areas. The proposed stochastic model, designed to address a location-scheduling problem, is formulated as a mixed-integer network flow problem with side constraints. We develop a simulation-based evaluation method and conduct computational tests to assess the model’s performance. Computational tests using real data from a city in Taiwan show that the stochastic model outperforms the deterministic model and has significant potential for practical applications.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633420</guid>
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    <item>
      <title>An optimization method of high-speed train rescheduling based on PER-D3QN</title>
      <link>https://trid.trb.org/View/2633419</link>
      <description><![CDATA[This paper addresses delay problems from moderate and slight disturbances in high-speed train operation by constructing an optimization model that minimizes total train delay time, solved using reinforcement learning. The research introduces a linear programming method to balance rescheduling frequency and total delay time, proposing a dynamic adjustment method of state transfer step size based on reinforcement learning’s greedy strategy and train operation constraints. This approach shortens calculation time and improves solution quality. The simulation environment is on a single direction of the double-track railway from Beijing South Station to Jinan West Station, and three experimental scenarios are designed. The experimental results show that dynamic adjustment of state transfer step size reduces average algorithm computation time by 30.5% and average total train delay by 8.2%. Compared to the CPLEX solver-based strategy, the approach decreases average timetable generation time by 7.4%.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633419</guid>
    </item>
    <item>
      <title>Evaluation of conflict classification methods in pedestrian safety analyses: a comparative study with practical guidance</title>
      <link>https://trid.trb.org/View/2633418</link>
      <description><![CDATA[This study compares different conflict classification methods based on three surrogate safety indicators (PET, TTC, DST). Single-indicator measures (PET-based, TTC-based, DST-based thresholds) and mixed-indicator measures (Accumulative-percentile, Clustering, Manual-review) are evaluated. Results show PET-based thresholds method detects more serious conflicts, while TTC-based thresholds method identifies more minor conflicts. Among mixed methods, the classification results of Accumulative-percentile method largely depend on the selection of dividing percentiles, while Clustering method and Manual-review method differ mainly in ‘normal passage’ and ‘minor conflict’ proportions. For single-indicator measures, PET-based thresholds method covers the widest range of influencing factors, whereas TTC-based and DST-based methods focus more on ‘evasive action’ factors. Mixed-indicator measures generally identify more variables than single-indicator measures. We further provide practical guidance to help with the selection of conflict classification methods.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633418</guid>
    </item>
    <item>
      <title>Analyzing severity of vehicle–bicycle crashes: an explainable boosting machine strategy</title>
      <link>https://trid.trb.org/View/2633417</link>
      <description><![CDATA[Vehicular crashes involving bicycles result in substantial annual fatalities, raising serious concerns for traffic safety authorities. Understanding the factors influencing crash severity is vital for designing effective countermeasures. However, the limited interpretability of many machine learning models complicates traffic safety assessments. This study introduces the Explainable Boosting Machine (EBM), a transparent glass-box model developed to predict the severity of vehicle–bicycle crashes and identify influential factors. A dataset of 5,341 crashes from the Ningbo Public Security Bureau (2020–2021) was analyzed. To address class imbalance, multiple data augmentation techniques were employed, and Bayesian optimization was used for hyperparameter tuning. EBM performance was benchmarked against black-box models, including LightGBM and XGBoost, using holdout evaluation. The EBM combined with borderline-SMOTE achieved a G-mean of 0.816 and an imbalanced accuracy of 0.651. Key predictors included weather and seasonal effects, with season–location interactions significantly influencing crash severity. This framework provides interpretable insights for data-driven traffic safety interventions and future research.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633417</guid>
    </item>
    <item>
      <title>Robustness assessment of a multilayer composite network with a two-stage fusion community detection algorithm</title>
      <link>https://trid.trb.org/View/2633416</link>
      <description><![CDATA[Multimodal transportation, including aviation, high-speed rail, and waterways, is crucial for urban freight in China. To evaluate the robustness of urban freight networks, this study constructs a multilayer composite network model for aviation, high-speed rail, and waterways based on complex network theory. A two-stage fusion community detection algorithm (TFCA) is proposed for community partitioning. The study simulates network attack risks under deliberate attack strategies, based on network characteristics and community division results. Results show that nodes with high equilibrium centrality, mainly in East and Central China, require early intervention to enhance network robustness; the network is resilient against random risks but more vulnerable to attacks targeting high equilibrium centrality nodes; Adjusting capacity coefficients φ, τ, and ε can strengthen network robustness, with τ and ε having a more significant impact. These findings provide a scientific basis for optimizing urban freight networks and improving their overall risk resistance.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633416</guid>
    </item>
    <item>
      <title>Integrated design and optimization of customized bus travel services for urban commuting</title>
      <link>https://trid.trb.org/View/2633415</link>
      <description><![CDATA[Customized bus (CB) travel services present a promising solution for urban transportation, blending the convenience of private cars with the cost efficiency of public transit. This study proposes a comprehensive CB service system for urban commuting, optimizing both station locations and scheduling strategies. First, K-means clustering algorithm is applied to determine bus station locations based on the spatical distribution of commuting demand. The bus routing problem is then formulated as a mixed-integer nonlinear programming (MINLP) model, considering both system costs and passenger waiting times. To efficiently solve the MINLP model in urban scenarios, an adaptive large-scale neighborhood search (ALNS) algorithm is employed. Finally, the proposed system is validated using real-world data. Results indicate that the CB service notably reduces average passenger waiting time and vehicle travel time compared to traditional taxi services, while also achieving over a 30% reduction in operating costs.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633415</guid>
    </item>
    <item>
      <title>E-scooters as transit feeders: insights from revealed preferences of shared e-scooter users</title>
      <link>https://trid.trb.org/View/2633414</link>
      <description><![CDATA[While e-scooters are increasingly used for short-distance trips, concerns persist regarding their effective integration with public transit. This study sheds light on the factors shaping revealed preferences for using e-scooters as transit access/egress mode by surveying 2,126 shared e-scooter users. We developed a Generalized Structural Equation Model (GSEM) to analyze the impacts of sociodemographic, transportation-related, and attitudinal factors on the frequency of e-scooter integration with transit. The results identified four attitudinal variables—transportation efficacy, variety seeking, environmental concern, and perceived safety—as the main drivers. Regarding demographic factors, experienced e-scooter users are more likely to integrate with transit, and women are less likely to use e-scooters in integration with transit. Furthermore, the findings underscored the role of transportation variables, households without personal vehicles, and access time to e-scooters under five minutes. The insights gained from the study help urban planners enhance e‑scooter–transit synergy and advance sustainable micromobility use.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633414</guid>
    </item>
    <item>
      <title>Modeling bounded rationality in discretionary lane-changing with the cumulative prospect theory</title>
      <link>https://trid.trb.org/View/2633413</link>
      <description><![CDATA[Discretionary lane-changing behavior, a prevalent expressway phenomenon, necessitates driver-centric analysis. Existing studies often assume perfect rationality, neglecting bounded rationality. This work divides decisions into perception and judgment phases. For perception, stated preference surveys and data-driven methods evaluate how drivers’ attention allocation and cognitive limits shape decisions. During judgment, cumulative prospect theory with logistic regression models heterogeneous reference points, achieving >90% accuracy—10% higher than expected utility models. Sensitivity analysis reveals: 1) 17% increased lane-changing propensity when risk aversion falls below loss sensitivity; 2) Loss aversion dominance in suboptimal lanes; 3) Systematic underestimation of high-probability events via probability distortion. These results quantitatively establish bounded rationality’s cognitive mechanisms in driving, offering theoretical foundations for human-machine collaborative systems and traffic management strategies addressing cognitive constraints. The dual-phase framework enhances behavioral realism while maintaining computational tractability in driving simulations.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633413</guid>
    </item>
    <item>
      <title>Investigation of hit-and-run crash severity through explainable machine learning</title>
      <link>https://trid.trb.org/View/2633412</link>
      <description><![CDATA[This study investigates factors influencing the severity of hit-and-run crashes using explainable machine learning techniques. A 5-year dataset from Victoria, Australia, was analyzed with CatBoost algorithms and SHAP values to highlight key severity factors. The presence of police at the crash scene emerges as the most critical determinant, underscoring the importance of law enforcement in mitigating severe crash outcomes. Crashes involving passenger vehicles and those on weekends were also linked to higher severity. The number of vehicles and total persons involved showed non-linear effects, with both low and high values associated with lower severity. Alcohol-related crashes and speed limit zones, while moderately important, revealed complex roles in severity prediction. These novel findings offer valuable insights for targeted interventions and policy-making to mitigate the impact of severe hit-and-run crashes and enhance road safety. In this way, policymakers can develop more effective strategies to reduce the impact of these phenomena.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633412</guid>
    </item>
    <item>
      <title>Traffic signal coordinated control model for long arterial based on traffic flow spatiotemporal characteristics</title>
      <link>https://trid.trb.org/View/2633411</link>
      <description><![CDATA[Traditional signal coordination methods face challenges in ensuring efficient traffic flow on long arterials due to urban expansion and complex spatiotemporal variations. However, existing methods struggle to achieve effective signal coordination under complex spatiotemporal variations, and lack methodological framework for universally applicable green wave coordination. To address this, a spatiotemporal partitioning-based green wave trajectory feature coordination optimization model is proposed. First, temporal partitioning is performed using an improved Fisher optimal segmentation method, while spatial subarea division is achieved via an enhanced K-Medoids algorithm. For each subarea, an arterial traffic signal control model is established based on green wave trajectory characteristics. Phase difference coordination equations are then applied to synchronize adjacent subareas. The model is validated on Foshan’s Lvjing Road, with evening peak performance compared against a classical green wave trajectory approach. Results indicate that the proposed model reduces vehicle average delay by 13.18% and the number of stops by 18.05%.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:56:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633411</guid>
    </item>
    <item>
      <title>Research on car-following models and platoon speed guidance based on real datasets in connected and automated environments</title>
      <link>https://trid.trb.org/View/2618035</link>
      <description><![CDATA[Connected and Automated Vehicles (CAV) utilize advanced sensors and communication technology to enhance traffic efficiency. This study applies platoon speed guidance in urban mixed traffic flow to evaluate the impact of CAV integration. The NGSIM (Peachtree) dataset is used to calibrate the standard headway, while the NGSIM (US101) and Gunter datasets optimize car-following model parameters. Model Predictive Control (MPC) enables multi-objective optimization of speed guidance strategies for four scenarios. A co-simulation with Python and SUMO evaluates performance. Results indicate that the calibrated car-following model accurately represents vehicle behavior, and platoon speed guidance effectively reduces stops and waiting times. Its effectiveness increases with traffic flow but diminishes at high congestion levels.]]></description>
      <pubDate>Mon, 09 Feb 2026 13:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618035</guid>
    </item>
    <item>
      <title>Online crowdsourced delivery optimization problem for takeaway orders with balanced rider resources and uncertain travel time</title>
      <link>https://trid.trb.org/View/2618034</link>
      <description><![CDATA[The takeaway food is mostly ordered online, and delivered. Some riders may receive orders beyond their capacity, causing resource imbalance. Additionally, uncertain travel time makes it difficult for riders to complete deliveries effectively. Thus, an integer programming is formulated for the online crowdsourced delivery problem with balanced rider resources and uncertain travel time (OCD-BRUT) to optimize rider delivery routes. An improved genetic algorithm (IGA) with order sequence optimization operator is developed. Numerical experiments on both simulated and real-world datasets demonstrate that the OCD-BRUT effectively balances rider resources, especially in medium and large instances. For small to medium instances, the average gap between the IGA and the optimal baseline is −2.58%, while the average gap reaches −7.48% in large-scale instances, indicating IGA’s efficiency in handling numerous orders in rush hours. Besides, a sensitivity analysis of several key parameters is also performed to derive managerial insights.]]></description>
      <pubDate>Mon, 09 Feb 2026 13:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618034</guid>
    </item>
    <item>
      <title>Understanding the role of travel context and latent traits on metro route choice using a hybrid choice model</title>
      <link>https://trid.trb.org/View/2618033</link>
      <description><![CDATA[Route choice decision-making is an intricate cognitive process. This study incorporates previously unstudied travel context and latent traits, in addition to traditional attributes, to examine their impact on metro route choices using a hybrid choice model framework. A stated choice experiment is designed to collect respondents’ preferred routes. A set of attitudinal indicators is utilized to measure passengers’ latent traits. Results indicate that metro passengers consider a variety of factors when choosing routes, placing particular emphasis on travel time especially when under time pressure. Cost, number of transfers, and higher crowded levels negatively affect choice behavior, while lower crowded levels, seat availability, and route familiarity exert a positive influence. Moreover, individuals with risk-taking or adaptability traits are likely to choose unconventional routes, those having a variety-seeking trait tend to favor unfamiliar routes and show less concern about crowding level, while flexible respondents persist in seeking less crowded riding experiences.]]></description>
      <pubDate>Mon, 09 Feb 2026 13:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618033</guid>
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