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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>
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    <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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      <title>Analysis of injury severity of single-vehicle and two-vehicle crashes involving lightweight vehicles (K-car) in Japan: A random parameters approach with heterogeneity in means</title>
      <link>https://trid.trb.org/View/2772169</link>
      <description><![CDATA[This study investigates the factors influencing injury severity in crashes involving lightweight vehicles (K-cars) in Japan, addressing safety concerns arising from their structural vulnerability. It aims to overcome limitations in prior research by developing a disaggregated analytical framework that separately examines single-vehicle and multiple two-vehicle crash scenarios (K-car vs K-car, K-car vs regular car, K-car vs truck) and explicitly accounts for unobserved heterogeneity. A further objective is to test the transferability of findings across these distinct crash types. The study utilized nationwide crash data (2020–2022) from Japan’s National Police Agency involving at least 1 K-car. Injury severity was consolidated into a binary variable (casualty vs no-injury). Five separate random parameters probit models with heterogeneity in means were developed to analyze single-vehicle crashes, 3 types of two-vehicle crashes, and an aggregated dataset of all crashes. These models assessed the influence of temporal, environmental, roadway, vehicle, and driver characteristics on injury outcomes. To validate the analytical approach, an out-of-sample prediction method was employed to quantify the prediction bias and assess the transferability of models across crash types. The model accounting for heterogeneity demonstrated a superior goodness-of-fit. Key findings included the following: Older drivers exhibit a dichotomous risk profile, facing higher injury risk in single-vehicle crashes but lower risk in two-vehicle crashes. In addition, medians with only painted lines are associated with more severe outcomes than those with physical barriers. Out-of-sample prediction analysis confirmed that the models are not transferable across crash types, underscoring the necessity of type-specific modeling. Safety analysis for lightweight vehicles must be disaggregated by crash type while accounting for unobserved heterogeneity. The study reveals complex factors, such as the dichotomous risk profile of older drivers, which demand targeted policy interventions. These research findings provide an evidence-based framework for developing effective countermeasures, including enhanced driver assistance systems and improved roadway designs, to comprehensively improve the safety of lightweight vehicles.]]></description>
      <pubDate>Fri, 25 Sep 2026 10:51:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772169</guid>
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    <item>
      <title>Examining traffic violations in severe casualty truck crashes: A text mining and reliable network analysis of narrative reports</title>
      <link>https://trid.trb.org/View/2726601</link>
      <description><![CDATA[Trucks are more likely to be involved in severe casualty crashes compared with other vehicle types. The elimination of traffic violations is crucial to preventing severe casualty truck crashes. However, there is a lack of comprehensive analyses of truck violations and their conditions related to severe casualty crashes. This study aims to identify thematic communities of truck driver violations through a modeling framework integrating text mining and reliable network analysis. This study collected 432 textual reports of severe truck casualty crashes in China from 2013 to 2020, which were divided into crash narratives and metadata for separate preprocessing. For the narrative part, the ELECTRA model was used for Chinese word segmentation and part-of-speech tagging, and keywords were extracted by combining with TF-IDF. The metadata was processed through named entity recognition, geocoding, etc., and then merged with the narrative keywords. Association rules were mined by the Apriori algorithm to construct a network with keywords as nodes and lift values as edge weights, which was visualized by the ForceAtlas2 algorithm. The Leiden algorithm was adopted to detect thematic communities, whose significance was validated by QStest. Text mining results reveal 77 most relevant keywords extracted from 432 police narratives. Overloading and speeding emerge as predominant traffic violations, correlating with 43% and 30% of severe casualty truck crashes, respectively. A total of four overloading and five speeding statistically significant thematic communities are identified. Notably, the circumstances associated with truck overloading and speeding manifest distinct characteristics. For overloading, conditions contributing to severe casualty crashes encompass rural highways with curves or slopes, provincial or national highways in the afternoon, expressways during nighttime, and locations proximate to signalized intersections. In contrast, five circumstances are linked to speeding: curved or sloped road segments during the afternoon, rural highways in autumn, straight road sections during the night, work zone areas on four-lane roadways, and un-signalized intersections on weekdays. Moreover, the authors also extracted vehicle and driver features across diverse environments, facilitating the identification of key elements for preventing severe casualty truck crashes. For instance, light trucks exhibit a higher susceptibility to severe casualty crashes attributed to overloading on rural highways. This study demonstrates the advantages of textual data and reliable network analysis. Text data analysis proves to be more convenient, yielding a richer array of comprehensive information while demanding less subjective judgment. The findings of this paper inform consequent enforcement and engineering measures for mitigating severe casualty truck crashes.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726601</guid>
    </item>
    <item>
      <title>Large-scale traffic safety management using Temporal Fusion Transformers: Prediction, interpretation, and intervention analysis</title>
      <link>https://trid.trb.org/View/2742180</link>
      <description><![CDATA[As urban road networks become increasingly complex, managing safety is a critical challenge. Proactive safety management requires both accurate risk prediction and an understanding of contributing factors. This study introduces a Temporal Fusion Transformer (TFT)-based framework that integrates prediction, interpretation, and intervention analysis for large-scale safety improvement. The TFT predicts a risk index for each intersection and link, utilizing both static and dynamic variables. Its dynamic attention mechanism provides insights into how the effect of each variable shifts over time. Trained and validated on the high-resolution pNEUMA trajectory dataset from Athens, Greece, the model achieved strong predictive performance (MAE = 0.30, RMSE = 0.461). Temporal analysis of attention weights revealed that taxis and motorcycles consistently contribute the most to elevated safety risks, while medium vehicles were most influential during the early morning hours. These insights enable the development of targeted strategies, such as dynamic access restrictions or time-based charges for specific vehicle classes during high-risk periods. A sensitivity analysis further shows how modest changes in traffic composition can significantly improve safety, quantifying the benefits of proposed interventions. Overall, the framework offers transportation agencies a practical, data-driven tool that bridges the gap between advanced predictive modeling and real-world safety management strategies.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742180</guid>
    </item>
    <item>
      <title>Deriving and validating a new modified two-dimensional surrogate safety measure to investigate driving risk across different passenger vehicle types</title>
      <link>https://trid.trb.org/View/2762686</link>
      <description><![CDATA[Driving risk assessment is crucial for advancing transportation safety, particularly in the context of increasing vehicle automation and electrification. This study proposes a modified two-dimensional time-to-collision (2D-MTTC) measure that incorporates relative acceleration in two-dimensional space to better characterize driving risk across different types of passenger vehicles. The proposed 2D-MTTC is validated through a two-stage validation process using different datasets to show its effectiveness and performance compared with the benchmark two-dimensional time-to-collision (2D-TTC): i) macro-level validation by computing the correlation between conflict risk and historical crash records of the road network; and ii) micro-level validation by detecting critical risk events in real conflicts and crash trajectories. After validation, the driving risk captured by 2D-MTTC between different types of passenger vehicles is examined using the Third Generation Simulation dataset (TGSIM). Specifically, the identified driving risks are compared between automated vehicles (AVs) and human-driven vehicles (HDVs), as well as between electric-based automated vehicles (EAVs) and internal combustion engine-based automated vehicles (ICEAVs). The results demonstrate the effectiveness of 2D-MTTC and its improved performance relative to 2D-TTC in identifying driving risks and capturing their duration and intensity. The findings further reveal that the driving risk characteristics among AVs and HDVs, EAVs and ICEAVs are significantly different and urge the consideration of vehicle types in proactive safety analysis.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762686</guid>
    </item>
    <item>
      <title>Vehicle group risk modeling based on risk propagation mechanism</title>
      <link>https://trid.trb.org/View/2752728</link>
      <description><![CDATA[Surrogate safety measures (SSMs) have been widely used for traffic risk assessment. However, most existing approaches remain focused on the risk state of individual vehicles, providing limited representation of the relational and propagative nature of risk in highly interactive traffic environments. In such environments, the risk observed at one vehicle may reflect the cascading propagation of disturbances through a vehicle group. To address this limitation, this study proposes a novel framework for vehicle group risk modeling based on the perspective of risk propagation. First, Modified Time to Collision is adopted to quantify single vehicle risk as the foundation for group-level safety characterization. Second, vehicle group categorization is conducted using a spectral clustering based method, through which vehicles exhibiting continuous temporal and spatial interactions are separated from the traffic flow as stable analytical units. Third, directed risk propagation relationships within vehicle groups are identified through granger causality analysis, and the corresponding propagation intensity is quantified using a generalized additive model. On the basis of these components, a vehicle group risk indicator is constructed by integrating the basic risk of individual vehicles with the propagation induced effects arising from the interaction structure within the group. Experiments are conducted on the highD dataset, and the results validate the effectiveness of the vehicle group risk assessment framework proposed in this study. The proposed approach offers an interpretable and propagation aware basis for vehicle group risk assessment, providing methodological support for extending traffic safety analysis from individual vehicles to vehicle groups.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752728</guid>
    </item>
    <item>
      <title>Automatic detection of emergency maneuvers, crashes, and strong jolts in naturalistic riding data from e-bicycles and e-scooters</title>
      <link>https://trid.trb.org/View/2743022</link>
      <description><![CDATA[This study develops and validates a smartphone-based framework for automatically detecting emergency maneuvers, strong jolts, and crashes involving electric scooters and electric bicycles. Detection criteria were established through controlled track experiments and subsequently evaluated using data collected during a naturalistic riding study involving 119 participants and more than 26,000 km and 1,600 h of riding, combining accelerometer, gyroscope, GPS, and video recordings. Threshold-based detection criteria were defined using variables selected for their physical relevance and ability to discriminate between target and non-target situations. Hard braking, sharp turns, strong jolts, and crash-related events were identified using combinations of acceleration, jerk, rotational dynamics, and post-event vehicle motion. Video review showed that 74% of hard-braking detections corresponded to harsh-braking maneuvers, 64% of sharp-turn detections reflected genuine avoidance maneuvers, and 91% of strong-jolt detections were associated with infrastructure features. Video verification of collision candidates confirmed several reported and previously unreported impacts, including collisions with other road users and single-vehicle falls. Application of the framework to the naturalistic dataset revealed marked differences between vehicle types. E-scooter users experienced higher rates of hard braking and strong jolts than e-bicycle users, reflecting behavioral differences and vehicle characteristics. Illustrative mapping examples showed that detected events and rider-reported hazardous situations could occur in close proximity, suggesting opportunities for future spatial analyses of micromobility safety. Although additional validation on larger crash datasets is required, the results demonstrate that threshold-based approaches can provide meaningful indicators of rider safety, support large-scale monitoring of micromobility risks, and contribute to infrastructure and transport-safety assessment.]]></description>
      <pubDate>Thu, 20 Aug 2026 09:54:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2743022</guid>
    </item>
    <item>
      <title>Learning Safety-Critical Scenarios from Real-World Pre-Crash Data for Autonomous Driving Safety Validation</title>
      <link>https://trid.trb.org/View/2711945</link>
      <description><![CDATA[A major challenge in Autonomous Vehicle (AV) safety validation is the scarcity of safety-critical interactions and the difficulty of modeling their underlying decision dynamics. The authors propose CrashGAIL, a conditional adversarial imitation learning framework that learns interaction-conditioned policies from real-world pre-crash trajectories and synthesizes high-risk two-vehicle scenarios under four interpretable motion-direction conditions. CrashGAIL combines (i) a dual-agent conditional policy architecture, (ii) a VAE warm-up for stabilizing latent representations, and (iii) an auxiliary condition-classification head to improve condition-behavior consistency, followed by PPO-based adversarial policy optimization. Experiments on 816 in-depth crash cases show improved kinematic fidelity while maintaining realistic geometric diversity compared with a strong time-series generative baseline. In counterfactual simulation with the Baidu Apollo stack, CrashGAIL-generated scenarios yield a higher overall crash involvement rate and expose failure modes that are less visible under replay-based testing.]]></description>
      <pubDate>Fri, 26 Jun 2026 13:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711945</guid>
    </item>
    <item>
      <title>A hybrid latent class analysis and association rule mining framework to identify injury-related risk patterns in level 2 advanced driver assistance system crashes</title>
      <link>https://trid.trb.org/View/2714554</link>
      <description><![CDATA[Automated driving features could improve road safety by reducing the number of crashes caused by human error. However, as the market penetration of Level 2 advanced driver assistance systems (ADAS) increases, so does the number of crashes involving these technologies. This study proposes a data-driven framework combining latent class analysis, association rule mining, and logistic regression to analyze 617 Level 2 ADAS crashes reported to the National Highway Traffic Safety Administration between 2019 and 2026. Two latent classes are identified, which are lower-speed local road crashes and high-speed highway crashes. Association rule mining and logistic regression are conducted within each latent class, revealing substantial heterogeneity in injury-related crash patterns. In lower-speed local road crashes, injury occurrences are mainly associated with frontal impacts, intersection-related crashes, and moderate-speed configurations. In high-speed highway crashes, injury occurrences are mainly associated with high-speed configurations, fixed objects, light-to-medium duty vehicles, frontal impacts, and morning or late-night conditions. Practical applications include placing greater emphasis in scenario-based testing for combinations involving moderate and high pre-crash speeds, frontal contact, fixed-object crashes, light-to-medium duty vehicle crash partners, and late-night conditions. These findings may help manufacturers refine speed-aware system-use guidance, driver warnings, and operational design constraints for Level 2 ADAS-equipped vehicles. Overall, this study demonstrates the value of class-specific analysis for uncovering heterogeneous injury-associated patterns in Level 2 ADAS crashes.]]></description>
      <pubDate>Fri, 26 Jun 2026 13:59:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714554</guid>
    </item>
    <item>
      <title>Benchmarking Advanced Tabular AI Models for Classifying SAE Automation Levels in Crash Data</title>
      <link>https://trid.trb.org/View/2711897</link>
      <description><![CDATA[The growing deployment of vehicles equipped with driver assistance and automated driving systems presents new challenges for crash record classification, as existing police-reported databases vary considerably in the completeness and consistency of automation-level metadata. This study benchmarks five tabular machine learning and deep learning models: random forest, XGBoost, MambaAttention, prior-data fitted network (TabPFN), and TabTransformer for classifying reported SAE automation categories using structured crash records from the Texas CRIS database (2024), comprising 4649 records across assisted driving (SAE Level 1), partial automation (SAE Level 2), and advanced automation (SAE Levels 3–5). The SAE automation label in each record reflects the vehicle’s designed automation capability derived from make, model, and year specifications rather than confirmed system engagement at crash time; the classification task, therefore, addresses SAE-coded vehicle capability identification rather than active automation detection. Records were partitioned at the crash level using GroupShuffleSplit to prevent crash-level data leakage, yielding 3721 training and 928 test records with zero Crash_ID overlap. SMOTE-NC was applied exclusively within the training partition to address the 21:1 class imbalance, interpolating continuous features while assigning categorical values by mode selection to preserve feature integrity. Fivefold stratified cross-validation with SMOTE-NC applied independently within each fold provided robust performance estimates. XGBoost achieved the highest macro-F1 (0.573; 95% CI 0.514–0.633), while TabPFN attained the highest macro-AUC (0.786) and accuracy (82.9%) without requiring task-specific training. MambaAttention achieved a macro-F1 of 0.509 and the highest advanced automation recall among the well-performing models (27.6%; 95% CI 11.1–45.2%), reflecting a trade-off between aggregate accuracy and minority-class sensitivity; this estimate carries substantial uncertainty given only 29 advanced automation test cases. McNemar’s significance tests confirmed that TabPFN and XGBoost form a statistically equivalent performance tier, while MambaAttention’s error profile differs significantly from all other models. TabTransformer exhibited persistent cross-validation instability and near-chance discriminative performance (AUC: 0.550) despite multiple architectural stabilization interventions, constituting a reproducible negative finding for vanilla transformer architectures under extreme tabular class imbalance. Feature contribution analysis using exact TreeSHAP decompositions for XGBoost identified population group, vehicle body style, roadway class, and crash timing as the dominant predictors of reported automation category. Each SAE category exhibits a distinct associative feature profile consistent with the geographic distribution and fleet composition of differently automated vehicles in the dataset. These associations are model-specific and exploratory; demographic features reflecting data collection patterns and geographic fleet distribution should not be interpreted as causal crash mechanisms. The findings demonstrate that tabular machine learning models can classify SAE-coded vehicle capabilities in police-reported crash records with meaningful accuracy, supporting applications in crash database quality assurance, automation-label inference at scale, and the design of automation-stratified safety research.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711897</guid>
    </item>
    <item>
      <title>Contributing factors to the severity of crash injury and vehicle damage involving Japanese lightweight vehicles (K-car): Considering unobserved heterogeneity</title>
      <link>https://trid.trb.org/View/2712744</link>
      <description><![CDATA[This study investigates the safety performance of lightweight vehicles by analyzing the factors influencing injury severity and vehicle damage in Japan.  Likelihood ratio tests were initially employed to validate the temporal stability of the crash data. Subsequently, random parameter bivariate probit models with heterogeneity in means were estimated to account for unobserved heterogeneity and correlations between crash outcomes.  An out-of-sample prediction analysis reveals a quantifiable structural safety gap for lightweight vehicles, which is often masked by the risk-compensatory behaviors of older drivers. The model demonstrates a significant negative correlation between the injury severities of the two drivers involved, indicating a “zero-sum” energy transfer effect, while the weak injury-damage correlation suggests a “decoupling” effect attributable to modern passive safety designs. Key findings indicate that injury severity is predominantly exacerbated by environmental factors like adverse weather and slippery surfaces, whereas vehicle damage is driven by geometric incompatibilities with road features like curbs. As the first study to apply this advanced modeling framework to lightweight vehicle safety, the findings offer evidence-based insights for optimizing emergency medical protocols, structural reinforcement standards, and driver assistance systems.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712744</guid>
    </item>
    <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>
    </item>
    <item>
      <title>Blame attribution mechanisms in AV accidents: A multi-dimensional exploration integrating social media and survey data</title>
      <link>https://trid.trb.org/View/2687063</link>
      <description><![CDATA[Before the full autonomous vehicle (AV) era, the present is an extraordinary era of shared control between humans and AV systems. In the current era, AV accidents are increasingly inevitable, and the occurrence raises complex ethical and legal challenges, resulting in the difficulty in determine the responsibility. Previous research primarily employed qualitative or quantitative analyses based on hypothetical scenarios to identify public blame targets in AV accidents and reached conflicting conclusions. Importantly, the research merely confirmed the existence of attribution bias, it has not sufficiently explained the underlying mechanisms. By integrating multiple analytical methods, this study developed a comprehensive attribution theory framework to explain the reasons for attribution bias in AV accidents. The research collected 90,426 valid comments from the Chinese social media platforms about AV accidents from April 1, 2021, to January 20, 2025. The three dimensions of concern topic, comment volume, and sentimental polarity identified the AV system as the main blame target. Based on the qualitative analysis results, Heider's naive attribution theory was adopted as the foundational theoretical framework to construct a comprehensive model of responsibility attribution in AV accidents. The authors found that trust and affective tagging are core factors in blame attribution. There is a gender tradeoff between knowledge and sentiment in AV accidents. Additionally, media exposure revealed a selective activation mechanism of media effects in the death scenario among males.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687063</guid>
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    <item>
      <title>Methods for Situational Awareness Assessment in Maritime Accident Analysis – Comparison of OZT Quantification Methods Using Heading and Course Over Ground</title>
      <link>https://trid.trb.org/View/2669610</link>
      <description><![CDATA[Many ship collision accidents are attributed to human factors such as a lack of lookout and improper maneuvering. In accident investigations, information related to human factors is qualitative; therefore, analysis is required to be conducted about quantitative indicators. In response, methods have been developed to evaluate ship operators’ awareness by linking it with a collision risk index based on the vessel’s physical state. In a previous study, the authors quantified the relative position from their own ship heading to the expected collision point, defined it as an index indicating the possibility of collision, and compared the index values with the operators’ perceptions. There were cases where the expected collision point may not have been appropriately assessed, and the range of positions to be included in the evaluation remained ambiguous. In this study, indices were calculated based on each of heading and course over ground (COG) and then compared to clarify the range of positions that should be included in the evaluation. The relationship between these indices and the operators’ situational awareness was subsequently examined. In this study, it was observed that based on COG is often more appropriate for evaluation. Additionally, when the heading-based approach is high, it may be more effective to collect further information on human factors.]]></description>
      <pubDate>Mon, 20 Apr 2026 11:14:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669610</guid>
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    <item>
      <title>An Overdispersed Count Regression Model for Analyzing Road Accident Fatalities and Injuries in Bangladesh</title>
      <link>https://trid.trb.org/View/2672670</link>
      <description><![CDATA[Road traffic accidents (RTAs) continue to be a major global public health issue, particularly in countries with developing road safety infrastructure like Bangladesh, where the road traffic fatality rate remains alarmingly high. This study aims to examine the relationship between road traffic accident outcomes-fatalities and injuries-and multiple contributing factors, including driver behavior, vehicle characteristics, environmental conditions, and road infrastructure. Using a comprehensive dataset of 64,050 police-reported road traffic accidents in Bangladesh (2006-2015), we apply a Negative Binomial Regression (NBR) model to account for overdispersed count data. Our results highlight that driver-related factors such as seatbelt use and age, vehicle factors such as fitness certification, and environmental conditions such as weather and road geometry significantly influence both fatalities and injuries. Notably, roads without dividers and in rural areas were found to be particularly hazardous. The study underscores the need for targeted road safety interventions, such as improved enforcement of seatbelt use, infrastructure upgrades (e.g., dividers, lighting), and more transparent vehicle fitness monitoring. By integrating driver, vehicle, environmental, and infrastructural variables, this study provides a comprehensive understanding of road traffic accident severity in Bangladesh, offering data-driven insights to inform evidence-based policymaking and infrastructure planning aimed at reducing road traffic injuries and fatalities. Copyright: © 2026 Newaz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:25:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672670</guid>
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    <item>
      <title>Identifying critical urban traffic risks for autonomous vehicle crash severity</title>
      <link>https://trid.trb.org/View/2675066</link>
      <description><![CDATA[As autonomous vehicle technology continues to advance, it promises enhanced mobility and the potential to reduce human error in transportation. However, as the technology becomes more widespread, concerns regarding its safety and reliability have also grown. Recent high-profile incidents involving self-driving systems underscore the need to understand the underlying factors that influence the severity of traffic collisions. This study investigates key urban traffic-related risks that contribute to increased crash severity in such collisions. Official traffic collision reports from the California Department of Motor Vehicles were used as the primary data source. These reports were spatially and temporally aligned with detailed road network characteristics and traffic conditions, resulting in a comprehensive dataset. A Poisson regression model was constructed following dimensionality reduction and stepwise variable selection, and its validity was confirmed through an overdispersion test. The results indicate that low illumination, the presence of buses, and high traffic congestion significantly increase crash severity. These findings provide valuable insights into how situational traffic factors affect the performance and safety of autonomous driving systems. The outcomes of this study may inform the development of safer operating conditions, enhance risk assessment frameworks, and support effective deployment strategies for autonomous vehicles in complex urban environments.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675066</guid>
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