Accident; analysis and prevention最新文献

{"title":"Revealing the lateral interference of lateral organization of automated truck platoon on surrounding manual vehicles","authors":"Qi Li,&nbsp;Feng Chen","doi":"10.1016/j.aap.2025.108244","DOIUrl":"10.1016/j.aap.2025.108244","url":null,"abstract":"<div><div>Automated truck platoon (ATP) represents a promising near-term automated mobility solution that can advance road sustainability by reducing pavement wear when configured with lateral offsets. As ATPs will initially share the roadway with human-driven vehicles (HDVs) before a full shift to autonomous transport, understanding how ATP lateral organization affects adjacent-lane HDVs is critical to preventing unintended safety risks. To address this gap, we first conducted driving simulation experiments with 38 nonprofessional commuters to quantify the effects of lateral-offset ATPs on driver behavior. We then compared the critical lateral distances between driving simulation data and naturalistic driving data using nonparametric probability model, and used Monte Carlo simulation on naturalistic driving data to model ATP‐induced interference under varying distribution scenarios. Our results show that lateral‐offset ATPs induce significantly greater lateral deviations and speed reduction for HDV, especially on curves. Specifically, ATP elevates driver fear perception score by 22.77 %, increases lateral deviation of HDV by 38.46 %, and raises interference probability by 25 %. Moreover, drivers’ responses to ATPs do not fully align with risk-homeostasis theory. Based on these insights, we recommend limiting ATP lateral dispersion, particularly avoiding right-biased formations, and prohibiting such configurations on curved segments. By clarifying how lateral organization of ATP shapes mixed-traffic dynamics, this study informs the safe integration of platooning technologies into existing road networks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108244"},"PeriodicalIF":6.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic risk assessment for autonomous vehicles from spatio-temporal probabilistic occupancy heatmaps","authors":"Han Wang ,&nbsp;Yuneil Yeo ,&nbsp;Antonio R. Paiva ,&nbsp;Jack P. Goodman ,&nbsp;Jean Utke ,&nbsp;Maria Laura Delle Monache","doi":"10.1016/j.aap.2025.108226","DOIUrl":"10.1016/j.aap.2025.108226","url":null,"abstract":"<div><div>Accurately assessing collision risk in dynamic traffic scenarios is a crucial requirement for trajectory planning in autonomous vehicles (AVs) and enables a comprehensive safety evaluation of automated driving systems. To that end, this paper presents a novel probabilistic occupancy risk assessment (PORA) metric. It uses spatiotemporal heatmaps as probabilistic occupancy predictions of surrounding traffic participants and estimates the risk of a collision along an AV’s planned trajectory based on potential vehicle interactions. The use of probabilistic occupancy allows PORA to account for the uncertainty in future trajectories and velocities of traffic participants in the risk estimates. The risk from potential vehicle interactions is then further adjusted through a Cox model, which considers the relative motion between the AV and surrounding traffic participants. We demonstrate that the proposed approach enhances the accuracy of collision risk assessment in dynamic traffic scenarios, resulting in safer vehicle controllers, and provides a robust framework for real-time decision-making in autonomous driving systems. From evaluation in Monte Carlo simulations, PORA is shown to be more effective at accurately characterizing collision risk compared to other safety surrogate measures.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108226"},"PeriodicalIF":6.2,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What does the research tell us about contributory factors related to inattention and driving in rural areas? A systematic review","authors":"Lisa Buckley,&nbsp;Verity Truelove,&nbsp;Steven Love","doi":"10.1016/j.aap.2025.108246","DOIUrl":"10.1016/j.aap.2025.108246","url":null,"abstract":"<div><div>Inattentive driving, such as visual-manual distraction, cognitive and affective based inattention, and impaired attention, poses a significant risk to traffic safety. Rural environments provide unique challenges for attention when driving, presenting an important area for research. As such, this study conducted a systematic review to synthesise research that explores the factors associated with inattentive driving and/or related motor vehicle crashes and injuries in rural areas. To be eligible for inclusion, studies were required to include analyses that identified a factor/s associated with driving and inattention in rural areas. Studies were excluded if they were conducted outside of high-income countries and were focused on commercial or occupational driving. Databases that were searched included PubMed, PsycNET, SCOPUS, and TRID, as well as reference lists of relevant systematic reviews that had a focus on inattention and driving. Of the 5142 original research articles that were identified, 23 papers met the eligibility criteria. The inattentive factors that were covered primarily included phone use, fatigue or drowsy driving, or distraction more broadly. Key contributory factors to inattentive driving and/or crashes across rural, regional and remote environments included road characteristics, driver characteristics, built environments and environment conditions. The findings also highlight the limited research in this area outside of crash-related data, with numerous future directions proposed. Given the heterogeneity and variety of factors that contribute to inattentive driving and crashes across distinct rural environments, more nuanced approaches for preventing inattentive driving in these areas is required. As such, stakeholders could consider performing comprehensive assessments of the unique circumstances associated with specific environments when considering interventional approaches to this issue.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108246"},"PeriodicalIF":6.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying uncertainties in data and model: a prediction model for extreme rainfall events with application to Beijing subway","authors":"Liang Mu ,&nbsp;Yurui Kang ,&nbsp;Zixu Yan ,&nbsp;Xiaobao Yang ,&nbsp;Guangyu Zhu","doi":"10.1016/j.aap.2025.108238","DOIUrl":"10.1016/j.aap.2025.108238","url":null,"abstract":"<div><div>Extreme rainfall is the primary cause of flooding at subway stations, and accurate prediction of rainfall volumes is essential for early flood warning systems. While previous research mostly focuses on point-by-point predictions based on rainfall spatiotemporal characteristics, it frequently ignores the uncertainties associated with rainfall data and predictive models, leading to unreliable rainfall forecasts. To address these limitations, we introduce a new model for predicting probability density (PD-STGCN) that systematically integrates data and model uncertainty quantification. This model provides both point predictions (PP) and probability density predictions (PDP) for extreme rainfall events. We specifically combine Monte Carlo Dropout (MC Dropout) and prediction variance into a Spatiotemporal Graph Convolutional Network (STGCN) architecture to quantify uncertainties in both the model and the data, and then build a new loss function to train the model based on the quantification results. Additionally, based on the sample set obtained by the trained model, and Gaussian Kernel Density Estimation (KDE) is used to calculate the rainfall probability density function (PDF) at the predicted moments. Validation using two distinct extreme rainfall events in Beijing shows that our proposed model outperforms various benchmark models in both tasks for point prediction and probability density prediction. These findings provide urban flood management with a novel predictive tool that combines high accuracy with strong reliability.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108238"},"PeriodicalIF":6.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Counterfactual evaluation of heavy vehicle safety policies on fatal crash rates using recursive discrete polynomial grey models","authors":"Yanqi Lian ,&nbsp;Shamsunnahar Yasmin ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Shimul Md Mazharul Haque","doi":"10.1016/j.aap.2025.108245","DOIUrl":"10.1016/j.aap.2025.108245","url":null,"abstract":"<div><div>Heavy vehicles play a crucial role in freight transportation. Yet, their crash risks and economic burdens necessitate a thorough investigation of long-term crash trends and an evaluation of safety policies targeting heavy vehicles. The intervention time series method, widely used in policy evaluation without the control group, is limited by its lack of causal inference and reliance on predefined effect assumptions. Thus, this study proposes a counterfactual causal framework using a recursive discrete polynomial time grey model to estimate the causal effects of multiple persistent road safety policies within a single time series. Specifically, the framework defines causal effects as contrasts between potential outcomes. The recursive discrete polynomial time grey model, capable of handling small sample sizes and capturing both linear and nonlinear trends, is introduced for counterfactual outcome prediction in traffic safety policy evaluation. The residual-based nested bootstrap resampling method is adopted to compute the confidence intervals of the estimated causal effects. The proposed framework is demonstrated using the annual fatal crash rates involving heavy vehicles per billion vehicle kilometers traveled from 1989 through 2023 in Queensland, Australia. Three major safety policies targeting heavy vehicles over those years are evaluated: Heavy Vehicle Fatigue Management Laws, Heavy Vehicle Speed Compliance Legislation, and Heavy Vehicle National Law. The findings indicate that these policies have significantly reduced the fatal crash rates involving heavy vehicles, although their effects exhibit temporal fluctuations. Nevertheless, without implementing new and innovative safety policies, the fatal crash rate involving heavy vehicles is likely to increase, underscoring the urgent need for continued policy advancements to enhance the safety of freight transportation systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108245"},"PeriodicalIF":6.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-step deep reinforcement learning for traffic signal control to improve pedestrian safety using connected vehicle data","authors":"A. Dian Ren ,&nbsp;B. Gongquan Zhang ,&nbsp;C. Fangrong Chang ,&nbsp;D. Helai Huang","doi":"10.1016/j.aap.2025.108161","DOIUrl":"10.1016/j.aap.2025.108161","url":null,"abstract":"<div><div>The primary goal of traffic signals control (TSC) is to enhance safety and protect all traffic participants. However, there exists enhancement such as increasing safety for vulnerable road users (VRUs), especially pedestrians. This study proposes a novel two-step traffic signal control framework based on deep reinforcement learning (TSDRL-TSC) to improve pedestrian safety and overall traffic efficiency at intersections. Based on advanced communication technologies of connected vehicles (CV), the TSDRL-TSC acquires the data from real-time traffic conditions and dynamically adjusts traffic signals, aiming to minimize traffic conflicts and delays of pedestrians and vehicles. In the first step, TSDRL-TSC decides whether to use traditional four-signal phases or a modified version considering the protected/prohibited right turn (PPRT) strategy based on pedestrian conditions. In the second step, TSDRL-TSC optimizes the specific control scheme through deep reinforcement learning techniques, selecting the optimal signal phases/actions for the current intersection state to obtain long-term reward returns. The reward function considers the safety and efficiency of all traffic participant, designed to balance the requirement for pedestrian safety, pedestrian efficiency, and vehicle throughput. Simulation experiments at a representative six-lane bidirectional intersection in Changsha City validate the effectiveness of the proposed method. Results demonstrate that (1) TSDRL-TSC significantly reduces pedestrian-vehicle conflicts, jaywalking incidents, and total delays compared to adaptive traffic signal control and PPRT control; (2) TSDRL-TSC presents the potential as a robust solution to enhance pedestrian safety and traffic efficiency for complex urban traffic management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108161"},"PeriodicalIF":6.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do older drivers (65+) exhibit significant impairments in hazard prediction and attentional processes?","authors":"Daniel Salazar-Frías ,&nbsp;Sonia Ortiz-Peregrina ,&nbsp;Francesco Martino ,&nbsp;José-J. Castro-Torres ,&nbsp;Jorge Clavijo-Ruiz ,&nbsp;Cándida Castro","doi":"10.1016/j.aap.2025.108182","DOIUrl":"10.1016/j.aap.2025.108182","url":null,"abstract":"<div><div>This study pioneers the use of the Hazard Prediction-Orienting Test to examine attentional capture in older drivers (aged 65+). Participants watched short, naturalistic driving videos and were asked to predict what would happen next after the video cut to black just as a developing traffic hazard that would require a behavioral response (e.g., slowing down or changing lanes to avoid a collision) began to emerge. Each trial included three multiple-choice options, with the correct answer corresponding to the developing hazard. Attentional orienting was manipulated through three conditions: simple trials (one developing hazard); valid trials (two hazards: one potential, which does not require driver action, and another developing located nearby); and invalid trials (two hazards: one potential and another developing located at a distance). A total of 141 experienced drivers, grouped by age (middle-aged, young-senior, and elderly) completed the test. A 3 × 3 mixed-effects ANOVA revealed significant main effects by age group and trial type, as well as a significant interaction. Elderly drivers showed the greatest performance decline, specifically under complex hazard conditions (both valid and invalid trials). These results were supported by significant correlations with neuropsychological assessments, including the Trail Making Test, the Useful Field of View (UFOV), and visual function measures such as visual acuity. Furthermore, mediation analysis revealed that the effect of age on hazard prediction in invalid trials was significantly mediated by selective attention, as measured by UFOV subtest 3. These findings suggest that for drivers over 65, both hazard prediction and attentional performance decline to levels comparable to those of inexperienced drivers in our previous study. The test shows promise as a functional assessment tool for identifying age-related declines relevant to traffic safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108182"},"PeriodicalIF":6.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145084727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ethical decision-making framework for autonomous vehicles based on public moral preferences","authors":"Yi Yang ,&nbsp;Qingfan Wang ,&nbsp;Hong Wang ,&nbsp;Jiajie Shen ,&nbsp;Chenghao Ma ,&nbsp;Bingbing Nie","doi":"10.1016/j.aap.2025.108241","DOIUrl":"10.1016/j.aap.2025.108241","url":null,"abstract":"<div><div>The rationality of decision-making by autonomous vehicles in unavoidable ethical dilemmas significantly affects public acceptance, and this, in turn, impacts the promotion and application of the technology. Given the absence of public opinion in the current autonomous vehicle decision-making, this study proposed an ethical decision-making algorithm framework based on public moral preferences. Five moral preferences reflecting widespread social consensus were identified: reducing overall injury, reducing individual injury, ensuring equal care, protecting the compliant, and avoiding active harm. Further drawing on a large-scale online survey featuring quantified injury severity information of road users, we modeled these moral preferences into machine language that autonomous vehicles could understand and execute. On this basis, we constructed the ethical decision-making algorithm framework. Referring to literature-based AV ethical dilemmas and real-world traffic conditions, we curated 4,860 diverse traffic scenarios for algorithm validation. Owing to the integration of public moral views as a significant basis in ethical decision-making, the proposed algorithm’s decisions in these scenarios closely aligned with the moral preferences we introduced. Specifically, compared with the baseline algorithm, our algorithm reduced the average individual injury severity by 52 %, the standard deviation of injury severity by 15 %, and the injury severity proportion of compliers and non-occupiers by 20 % and 24 %, respectively. The proposed framework is highly scalable and is expected to serve as a reference for future research on autonomous vehicle ethics in other regions with different cultural backgrounds. This work contributes to the public acceptance of autonomous vehicle technology and further facilitates its promotion and application.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108241"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of the prevalence of cannabis and alcohol use among drivers and passengers in British Columbia and Ontario, Canada","authors":"Lulu X Pei ,&nbsp;Herbert Chan ,&nbsp;Floyd Besserer ,&nbsp;Jeffrey Eppler ,&nbsp;Jacques Lee ,&nbsp;Andrew MacPherson ,&nbsp;Michael McGrath ,&nbsp;Robert Ohle ,&nbsp;John Taylor ,&nbsp;Christian Vaillancourt ,&nbsp;Jeffrey R Brubacher","doi":"10.1016/j.aap.2025.108242","DOIUrl":"10.1016/j.aap.2025.108242","url":null,"abstract":"<div><h3>Background</h3><div>Similar to drink driving, the prevalence of driving under the influence of cannabis (DUIC) is expected to depend on the availability and cost of cannabis which would impact cannabis use in both drivers and passengers, and factors that specifically target cannabis use in drivers such as the deterrent effect of traffic laws and driver’s opinion about the risks and acceptability of DUIC. To disentangle these effects, we aimed to compare the prevalence of alcohol and tetrahydrocannabinol (THC) detection 1) in drivers vs. passengers involved in motor vehicle accidents and 2) in drivers and passengers from BC vs. Ontario.</div></div><div><h3>Methods</h3><div>Chart review and toxicology data from an ongoing prospective study of moderately injured motor vehicle occupants were analyzed. Log-binomial regression models were used to obtain prevalence ratios (PRs).</div></div><div><h3>Results</h3><div>This manuscript reports on data from 3004 drivers and 941 passengers. Approximately half (55.1%) were male, and the mean (SD) age was 43.8 (19.1) years. Alcohol and THC detection prevalence was 14.2% and 12.4%, respectively. Passengers had higher prevalence of alcohol than drivers (aPR [95% CI]: 1.22 [1.06, 1.40]). No difference in THC prevalence was observed between drivers and passengers. Ontario drivers had higher prevalence of alcohol detection than BC drivers (aPR [95% CI]: 1.33 [1.13, 1.58]) but lower prevalence of THC detection (aPR [95% CI]: 0.80 [0.64, 0.99]). Among passengers, no significant interprovincial differences were observed for alcohol or THC detection.</div></div><div><h3>Conclusion</h3><div>These findings may be partially explained by differences in provincial traffic laws, public opinion, and overall consumption rates.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108242"},"PeriodicalIF":6.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven approach to child pedestrian crash analysis using dimension reduction, clustering, and explainable AI","authors":"Swastika Barua,&nbsp;Rohit Chakraborty,&nbsp;Md. Monzurul Islam,&nbsp;Subasish Das","doi":"10.1016/j.aap.2025.108229","DOIUrl":"10.1016/j.aap.2025.108229","url":null,"abstract":"<div><div>Child pedestrian (less than 15 years old) crashes are a critical road safety concern, often resulting in severe or fatal injuries due to the inherent vulnerability of young pedestrians. Despite the pressing nature of this issue, limited studies have comprehensively analyzed the key contributing factors influencing crash severity outcomes. Addressing this gap, the current study utilizes hybrid approach employing machine learning models, such as XGBoost and Random Forest, and Cluster Correspondence Analysis (CCA), a joint dimension reduction and clustering method to identify and explain the major risk factors associated with child pedestrian crash severity, and to generate evidence-based safety recommendations through advanced analytical techniques. SHAP analysis, an explainable AI, was conducted on each cluster to further investigate the impact of factors on crash severity. The study uses crash data from the Texas Department of Transportation’s Crash Records Information System (CRIS) spanning 2017 to 2022, including 4,762 crashes involving pedestrians under 15 years of age. Key variables include roadway factors (intersection type, traffic control, posted speed limits, road class), temporal aspects (crash hour, season), demographic attributes (age, ethnicity), and environmental conditions (weather, lighting). The results reveal that child pedestrian-involved crashes are associated with frequent interactions on city streets, intersections, and non-trafficway areas like parking lots and driveways. Poor lighting conditions, particularly in rural and high-speed road environments, and absent traffic controls in low-speed zones significantly contribute to crash severity. Temporal patterns indicate higher risks during afternoon and evening hours in urban areas and nighttime in rural settings, with turning movements further increasing risks due to difficulties in detecting pedestrians. This study provides critical insights into the multifaceted factors influencing child pedestrian crash severity, emphasizing the need for improved infrastructure, enhanced lighting, speed management, and targeted safety interventions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108229"},"PeriodicalIF":6.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}