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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Is panic over e-bike safety justified? A review of the literature and comparative analysis with conventional bicycles and e-scooters</title>
      <link>https://trid.trb.org/View/2705239</link>
      <description><![CDATA[The increasing popularity of e-bikes has prompted global concerns about their safety. This literature review synthesizes English-language literature on e-bike safety, focusing on publication trends, data sources, major themes, and comparisons with human-powered bicycles and e-scooters. In total, 197 studies published between 2009 and June 2024 were reviewed. Research activity has expanded rapidly in recent years, with China and the United States emerging as the leading countries. Most studies examined rider demographics, crash involvement, rider behaviors, and comparisons with other traffic modes. The most common data sources were medical records, surveys, and naturalistic designs. Among studies comparing e-bike safety with bicycles or e-scooters, consistent patterns emerged. E-bike crashes occurred far less frequently than bicycle crashes (often by a magnitude of 10 or more), but when medical attention was required, e-bike riders more often sustained severe injuries. Comparisons between e-bikes and e-scooters were less clear-cut, with mixed findings on both crash incidence and injury severity. The findings of this review highlight implications for engineering, enforcement, and education. The distinct injury patterns and higher operating speeds associated with e-bikes point to opportunities for engineering improvements, including clearer lane design and safer shared infrastructure for e-bikes. Faster and higher-powered models also introduce enforcement challenges related to speed compliance, lane access, and rider eligibility, underscoring the need for clear and consistent regulations. Finally, targeted education may help address unsafe behavior and support safe integration of e-bikes into multimodal transportation systems.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705239</guid>
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    <item>
      <title>Estimating the causal impact of crashes on drivers’ behavior through a difference-in-differences quasi-experimental design</title>
      <link>https://trid.trb.org/View/2704827</link>
      <description><![CDATA[This study estimates the causal effects of traffic crashes on driving behavior and habits, focusing on total annual distance driven (km) and the proportion of kilometers driven above the posted speed limit, in urban areas, and at night. The authors use a unique three-year panel dataset provided by an insurtech company that collects annual driving data through in-vehicle sensors. Given the observational nature of the data, the authors apply a difference-in-differences (DiD) quasi-experimental approach to assess the causal impact of crashes on driving outcomes. The results indicate that the effects of crashes vary according to accident frequency, type of damage, and whether the driver is at fault. For the full sample, the authors find positive and statistically significant effects on annual distance driven and on the proportion of kilometers driven at night. However, when crashes result in bodily injury, the effect on the proportion of kilometers driven above the posted speed limit becomes negative. Furthermore, heterogeneity analyses show that distinct driver and vehicle profiles are associated with specific behavioral responses to crashes.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704827</guid>
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    <item>
      <title>Effective measures for evaluating safe driving ability with a driving simulator and eye movement tracking</title>
      <link>https://trid.trb.org/View/2704810</link>
      <description><![CDATA[Stroke survivors often wish to resume driving, but objective and reliable indicators for assessing fitness to drive are lacking. The authors aimed to establish effective measures for evaluating safe driving ability in brain-injured patients by integrating driving simulator (DS) performance and eye movement analysis. Participants included brain-injured patients, classified into mild and severe groups using Trail Making Test-B scores and the presence of visual field defects, neglect, or aphasia, alongside healthy controls. Neuropsychological assessments (Trail Making Test-A/B, Kohs Block Design Test, Mini-Mental State Examination) were conducted. Driving performance was evaluated using the Honda Safety Navi DS, focusing on the standard deviation (SD) of steering angle on straight roads and the SD of velocity on curved roads. Eye movements were recorded with Tobii Pro Glasses 2 during hazard detection and dangerous situation scenes, with particular attention to saccade amplitude. Group differences were analyzed using Kruskal–Wallis and Mann–Whitney U tests. The SD of steering angle on straight roads and the SD of velocity on curved roads were significantly higher in mild or severe brain-injured groups compared with healthy controls. However, saccade amplitude was significantly lower in both mild and severe brain-injured groups than in healthy controls during hazard detection scenes, indicating impaired visual exploration. To measure driving ability in both mild and severe brain-injured patients, saccade amplitude provided a promising objective indicator for evaluating driving ability instead of DS alone. These findings support the development of evidence-based fitness to drive assessments for clinical and rehabilitation applications.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704810</guid>
    </item>
    <item>
      <title>Traffic demand resilience analysis in highway tunnels based on equilibrium allocation model</title>
      <link>https://trid.trb.org/View/2704806</link>
      <description><![CDATA[Due to the uncertainty of travelers’ choices and the complex factors affecting the traffic conditions in highway tunnels, it is difficult to accurately grasp the changing patterns of traffic demand, which in turn affects the accuracy of traffic demand resilience analysis. Therefore, this article proposes a tunnel traffic demand resilience analysis method based on the equilibrium allocation model to improve the operational stability of the tunnel traffic system in emergency situations. This method comprehensively considers key congestion influencing parameters such as traffic flow, vehicle speed, vehicle type composition, tunnel length and slope, and combines stochastic utility theory to construct a stochastic equilibrium allocation model to characterize the random selection behavior of travelers and achieve tunnel traffic allocation under elastic demand. On this basis, a tunnel traffic demand resilience analysis model was established by constructing a traffic efficiency function and an operational risk function, which can accurately solve the remaining capacity of congested road sections. To verify the effectiveness of the proposed method, actual toll data from the highway network in J province were selected for case analysis. The research results show that this method has an error of less than 10% in the analysis of remaining traffic capacity in congested tunnel sections, with an overall queue length accuracy of 83.05% and a maximum queue length accuracy of 93.62%. In addition, this method can maintain high accuracy in flow demand analysis under abnormal working conditions, providing a reliable basis for dynamically adjusting traffic flow distribution, optimizing traffic resource allocation, and ensuring stable operation of tunnel traffic systems. It has important theoretical and practical value.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704806</guid>
    </item>
    <item>
      <title>Discovering individual human mobility changes and mobility inequality due to the pandemic</title>
      <link>https://trid.trb.org/View/2704805</link>
      <description><![CDATA[The COVID-19 pandemic caused global disruptions, and understanding its impacts on human mobility is essential for traffic management, urban planning, and epidemic prevention. While previous studies validated aggregate mobility changes, the shifts in the underlying statistical distributions of human mobility remain underexplored. Using mobile phone signaling data from Beijing and Shenzhen, China, the authors quantify pandemic-induced changes in individual mobility and recovery patterns. The maximum likelihood method is applied to select the most fitting distribution for human mobility. The authors find that the number of trips, radius of gyration, and entropy dropped sharply during the pandemic and have yet to fully recover. Individuals increasingly adopted simpler trip chains, and their movement predictability rose due to the pandemic. Notably, travel distance shifted from an exponential to a lognormal distribution during the pandemic, while the radius of gyration exhibited the opposite pattern. After that, travel distance distributions returned to pre-pandemic forms, but the radius of gyration did not. Besides, females, older adults, non-commuters, and high-income groups experienced greater mobility reductions during the pandemic than their counterparts. These findings highlight distributional shifts in human mobility during crises and underscore the need to ensure basic travel access for disadvantaged groups.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704805</guid>
    </item>
    <item>
      <title>Improving train accident data structure for more effective data-driven risk analysis and accident prevention</title>
      <link>https://trid.trb.org/View/2704795</link>
      <description><![CDATA[Train accident data are essential for quantitative risk analyses, and advances in data analytics have considerably improved the efficacy of data-driven risk assessments. Despite the importance of train accident data quality and resolution, limited research has focused on how accident data structures can be improved to better assist more accurate and effective risk analyses. This research presents a novel data diagnosis framework that systematically examines accident data structure and identifies key weaknesses (i.e., missing or low-resolution data fields) from a risk analysis perspective. Recommendations are made to improve train accident data structures to enhance data availability, completeness, and resolution. A case study based on the United States railway accident database was conducted to demonstrate the diagnosis framework. Results showed that information such as curvature, grade, train consist, and railcar loading status should be added to train accident data. Furthermore, the key to obtaining comprehensive and high-resolution accident data is connecting train accident data with external databases that provide important information for risk analyses such as weather conditions and hazmat through connection identifiers. This research contributes to the development of risk-oriented train accident data to enable more effective and accurate risk analysis for critical current and future rail safety topics.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704795</guid>
    </item>
    <item>
      <title>Autonomous vehicles and public transit incentives: A Case study of Waymo’s pilot transit credit program in the San Francisco Bay Area</title>
      <link>https://trid.trb.org/View/2704536</link>
      <description><![CDATA[This study evaluates the effectiveness of a pilot transit credit program launched by Waymo in the San Francisco Bay Area, which offered riders a $3 incentive to use an AV rideshare service in conjunction with nearby public transit stations. Using structured survey data, the authors explore how the incentive influenced travel behavior, mode substitution, and public transit connectivity. Results indicate that 69% of participants connected to transit, while 50% of these riders would have otherwise used privately paid rideshare and some would have used their private vehicle or walked or cycled. The remaining 38% of participants would have used transit to reach the public transit station. The study offers critical insights for transit agencies and mobility providers aiming to design integrated AV-transit systems that serve the public good. While there is a risk of misallocating funds to individuals already willing and able to pay for rideshare services, these findings suggest that targeted subsidies may influence mode choice and support multimodal travel in a pilot context. Despite the exploratory nature of the study, the results highlight a potential pathway for transit agencies to experiment with incentive-based integration strategies in partnership with emerging mobility providers.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704536</guid>
    </item>
    <item>
      <title>Modeling the market penetration of automated vehicles with an integrated choice-based diffusion approach</title>
      <link>https://trid.trb.org/View/2704530</link>
      <description><![CDATA[In the past decade, transportation researchers have predicted Automated Vehicle (AV) preferences using discrete choice models, including logit and probit models. These studies rely on stated preferences on AV adoption which only provides an adoption scenario at a single point in time. This limitation can be overcome by following a diffusion-based approach known as the Bass Model using sales data on the product. In this study, the authors use existing sales data on Level 2 AVs also known as advanced driver assistance systems (ADAS) for prediction of mass adoption of Level 3 AVs. This study is the first to model AV mode choice combining discrete choice theory with a diffusion model. Stated and revealed preference data from Puget Sound Travel Survey, and a Nested Logit Model are used to obtain the market penetration of AV travel modes. The market shares are then fed into the diffusion model for the prediction of the number of adopters for automated transit, automated taxi and privately owned autonomous cars. The findings reveal that it will take 20 years to reach full market potential for Level 3 automated cars and buses, when there is a 0.53 percent annual decrease in the price of the ADAS technology. By quantifying the timing and mode-specific dynamics of AV adoption under different cost scenarios, this study may help shape policies in the future related to the penetration of AVs, specifically pricing policies, and regulatory strategies for varying AV modes.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704530</guid>
    </item>
    <item>
      <title>Understanding traveller behaviour under congestion pricing: A survey-based decision tracking approach</title>
      <link>https://trid.trb.org/View/2704528</link>
      <description><![CDATA[This paper examines commuter responses to three congestion pricing (CP) schemes including cordon-based, distance-based, and travel time–based in Calgary, Canada. The study uses an efficiently structured, personalized stated preference survey that integrates sociodemographic information, revealed commuting patterns, and responses to hypothetical CP scenarios, with a focus on joint mode and departure-time decisions. A key contribution of this study lies in the survey design, which records respondents’ interactions with the choice interface and identifies the alternatives actively examined during each choice task. This information is used to estimate a tracked multinomial logit model in which the choice set is restricted to alternatives genuinely considered by each respondent. Comparison with a conventional non-tracked specification shows that assuming full consideration of all displayed alternatives inflates sensitivity to travel time, cost, and toll attributes. By contrast, the tracked model yields more moderate elasticities and parameter estimates that better reflect observed decision-making behavior. Applied to the congestion pricing context, the results suggest shifts towards transit and ridesharing under certain pricing structures, highlighting how pricing design can influence peak-period travel decisions. Overall, the findings demonstrate that incorporating interaction-tracking data into stated preference surveys enhances the behavioral credibility and policy relevance of model-based evaluations of congestion pricing strategies.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704528</guid>
    </item>
    <item>
      <title>Modeling loops and backhaul in maritime freight: A MILP extension for large-scale freight transport models</title>
      <link>https://trid.trb.org/View/2704515</link>
      <description><![CDATA[The paper presents LIFREM (Loops Including FREight Model), a model framework for improving the representation of maritime freight transport in large-scale freight transport models by integrating loops, cargo consolidation, and backhaul operations. Many large-scale freight models, such as the Swedish national freight transport model Samgods, calculate annual goods flows across different transport modes by minimizing firm-to-firm costs. These models generally restrict vehicles and vessels to direct routes, excluding the multi-stop routing patterns typical of maritime operations. This limitation reduces their ability to capture complex maritime dynamics such as port loops, consolidation, and backhaul. Using concepts from operational research and liner shipping network design, LIFREM uses a mixed-integer linear programming (MILP) framework to optimize freight assignments and incorporate loops and backhaul, thereby extending the representation of maritime transport in large-scale freight transport models and improving the representation of economies of scale in maritime transport. A case study of forest product exports from Northern Sweden illustrates how the inclusion of loops and backhaul in the modeled system can reduce transport costs and shift freight towards larger vessels, producing routing patterns qualitatively consistent with observed maritime transport structures in the Baltic Sea. The results also indicate that modal shifts may be non-linear, with transport modes remaining stable until threshold effects are reached. Computational scaling experiments assess the feasibility of applying the MILP formulation as a complementary module in large-scale freight transport models. The paper concludes by discussing limitations, computational tradeoffs, and possibilities for modular integration of the framework into large-scale freight transport models.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704515</guid>
    </item>
    <item>
      <title>The role of reverse logistics factors in shaping repurchase intention on e-commerce platforms among Generation Z in Vietnam</title>
      <link>https://trid.trb.org/View/2704336</link>
      <description><![CDATA[Reverse logistics has become a critical component in shaping post-purchase experiences on e-commerce platforms, particularly among Generation Z consumers in emerging markets. This research examines the influence of seven key reverse logistics service quality factors: information quality, communication quality, timeliness, compensation, empathy, convenience, and return policy, on customer satisfaction and trust, and how these factors subsequently affect repurchase intention in the context of e-commerce in Vietnam. Adopting a quantitative research design, data were collected from 375 valid respondents and analyzed using PLS-SEM. The findings reveal that timeliness, compensation, empathy, and convenience significantly enhance customer satisfaction, while compensation, empathy, convenience, and return policy positively influence trust. In contrast, information quality and communication quality were not found to have significant effects. Both satisfaction and trust emerged as strong predictors of repurchase intention, underscoring their mediating roles in the relationship between reverse logistics service quality and customer repurchase intention. The results offer practical implications for e-commerce platforms seeking to improve post-purchase service design and retain young consumers. By optimizing key service aspects such as timeliness, compensation, empathy, and convenience, e-commerce platforms can deliver a smoother and more satisfying post-purchase experience. Strengthening return policies also plays a crucial role in building trust – an essential factor in encouraging repeat purchases. These improvements not only drive repeat purchases but also build brand trust and reputation. Focusing on these areas helps businesses build stronger relationships with Generation Z, accelerating progress and securing a competitive position in the face of ongoing market evolution.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704336</guid>
    </item>
    <item>
      <title>Clustering drivers by demographic and aberrant driving behavior: Implications for traffic injury risk and safety interventions</title>
      <link>https://trid.trb.org/View/2704314</link>
      <description><![CDATA[Road traffic injuries remain a leading cause of death and disability globally. This study aimed to identify distinct clusters of drivers based on demographic characteristics and multidimensional aberrant driving behaviors, and to examine their association with the severity of traffic-related injuries. A secondary analysis was conducted on a cross-sectional dataset of 800 licensed drivers from Iran. Data included demographics, crash history, traffic fines, and responses to the Driver Behavior Questionnaire. Principal Component Analysis and Hierarchical Clustering on Principal Components were used to derive latent driver clusters. Cluster profiles were compared using statistical tests. An Extreme Gradient Boosting classifier was developed to assess the discrimination of the identified clusters. Three driver clusters were identified. Cluster 3, comprising mostly young, less-educated males with limited driving experience, showed significantly higher mean scores in all aberrant behavior dimensions and the highest proportion of severe injuries (40.7%; p < 0.001). Cluster 2, characterized by younger, more educated females with lower driving exposure and fewer behavioral violations, had the lowest severe injury rate (15.6%). Cluster 1 demonstrated intermediate characteristics and injury risk, positioned between Clusters 2 and 3 in terms of demographic, behavioral, and outcome variables. Classification performance was high (accuracy = 92.6%, AUC = 0.987), supporting the clustering scheme. Multidimensional clustering of demographic and behavioral data can effectively identify high-risk driver subgroups. These findings highlighted the value of behaviorally informed risk stratification in traffic safety frameworks, providing a foundation for precision-targeted interventions and injury prevention efforts.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704314</guid>
    </item>
    <item>
      <title>Sufficient mobility and access within limits: Research agenda for bringing together corridor frameworks and transportation research</title>
      <link>https://trid.trb.org/View/2698843</link>
      <description><![CDATA[Recently developed frameworks that explicitly define boundaries of sustainability, such as “a safe and just space” or “consumption corridors,” are key for achieving good lives for all within ecological limits and have been explored in multiple influential studies. However, these “corridor frameworks” have rarely been explicitly applied to mobility and transport, and there is a need for more work in this direction. In this article, the authors provide an overview of the corridor frameworks and their links to four main strains of mobility and transport literature: sustainable transport, transport poverty, accessibility, and tourism and long-distance travel. The literature traditions have meaningful links to the corridor frameworks, but their approaches to social and ecological justice are largely disparate and disconnected. Existing studies rarely consider explicit ecological ceilings, and when ecological impacts are considered, the focus is usually on efficiency or relative improvements. Transport poverty and accessibility literature provide a meaningful contribution to defining social floors, but they largely neglect ecological ceilings and consumption maxima. Considerations of floors and ceilings are rarely explicit or are based on unquestioned assumptions of necessity and excess. Explicit ecological ceilings and social floors in mobility have been defined at national or global scales, but there is a need for more work on locally-specific thresholds that are distributively and procedurally just. The authors highlight the need to more comprehensively apply the corridor framework to transportation research and suggest a research agenda with seven main directions.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698843</guid>
    </item>
    <item>
      <title>Safe System maturity and Safe System readiness in three European and three African countries: A comparison of an emerging versus a mature context</title>
      <link>https://trid.trb.org/View/2698823</link>
      <description><![CDATA[The study provides a comparison of Safe System maturity and Safe System readiness in three European countries (Norway, Sweden, the Netherlands) and three African countries (Ghana, Tanzania, Zambia), based on document studies and focus group discussions (n = 73 interviewees and n = 44 interviewees). Safe System maturity refers to the level of Safe System implementation related to national road safety management, while the readiness assessment focuses on the factors influencing maturity. The study develops a model to assess Safe System readiness. Interviewees in the focus groups discussions in the African countries discussed insufficient implementation from the position of an emerging Safe System context, where factors like insufficient economic resources, corruption and insufficient institutional robustness limit Safe System implementation. Interviewees in the European countries discussed insufficient implementation from a mature Safe System context. These countries have had considerable reductions in fatal accidents since they implemented Safe System policies, but there is still room for improvement. Interviewees in the European countries generally indicated that they know what is needed to reach the Safe System, but that societal factors are constraining this implementation (e.g. cultural focus on freedom to take risk, lacking political sense of urgency related to road safety). There are several very effective measures that are not being used in the European countries, because factors like explicit political choices, goal conflicts and values limit Safe System implementation. The study concludes that there are considerable implementation barriers in both the emerging and the mature Safe System context, although they differ in nature.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698823</guid>
    </item>
    <item>
      <title>Territorial equity, energy performance, and policy orientation in Tunisian public transport: A multi-objective DOE-based analysis</title>
      <link>https://trid.trb.org/View/2698822</link>
      <description><![CDATA[Public transport systems operating across heterogeneous territories must balance operating costs, operational energy performance, and territorial fairness under constrained planning conditions. This study analyses these interactions in the Tunisian context using an integrated vehicle-crew scheduling framework combined with a full factorial design varying demand intensity, spatial imbalance, and policy orientation. Twenty-seven scenarios are solved on a single-day tactical horizon, and four responses - -cost, energy use, territorial equity, and a composite sustainability score — are examined using ANOVA and response-surface modelling. The results show that spatial imbalance and policy orientation are the main drivers of energy and equity outcomes, whereas short-term demand variations exert a more limited influence once the timetable is fixed. Energy use tends to increase when territorial or policy choices expand service coverage, highlighting the operational tension between fairness and resource intensity. Overall, the framework provides a policy-relevant and analytically transparent basis for identifying trade-offs and compromise regions in sustainability-oriented public transport planning.]]></description>
      <pubDate>Thu, 28 May 2026 09:03:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698822</guid>
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