Accident; analysis and prevention最新文献

{"title":"Analysis of powered two-wheeler accident scenarios and multi-body simulations to support trunk protective equipment impact test methods","authors":"Thibaut Deville ,&nbsp;Selim Cheaibi ,&nbsp;Maxime Llari ,&nbsp;Wei Wei ,&nbsp;Anaïs Garo ,&nbsp;Jean-Philippe Lepretre ,&nbsp;Pierre-Jean Arnoux ,&nbsp;Catherine Masson","doi":"10.1016/j.aap.2025.108283","DOIUrl":"10.1016/j.aap.2025.108283","url":null,"abstract":"<div><div>Powered two-wheeler (PTW) riders are most frequently injured seriously to the thorax. Some thorax protective devices have been developed in order to reduce thorax injuries. Nevertheless, actual tests and standard lack real-world accident representativeness. The aim of the study was to determine realistic trunk impact conditions for accidents likely to result in thorax injury. The study is divided into two phases: a literature review to classify accident scenarios based on rider kinematics, and numerical simulations to analyze the riders impact conditions. Three accident scenarios have been found through a literature review and their main configurations have been numerically investigated via multi-body modeling. The 912 performed simulations were used to explore body contacts with counterparts over time, relative velocities and relative body orientation at impact. The results have highlighted the necessity of a conservative activation time delay for triggering protective devices. They have also led to propose three realistic impact conditions that can be used in future test protocols.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108283"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145463899","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":"Early identification of high-risk older two-wheeler riders: A dual-sample approach for 30-day mortality prediction","authors":"Ming-Chuan Hsu ,&nbsp;Ya-Hui Chang ,&nbsp;Chung-Yi Li ,&nbsp;I-Lin Hsu ,&nbsp;Chung-Shun Wong ,&nbsp;Ping-Ling Chen ,&nbsp;Hon-Ping Ma","doi":"10.1016/j.aap.2025.108298","DOIUrl":"10.1016/j.aap.2025.108298","url":null,"abstract":"<div><div>Older motorized two-wheeler riders face high risks of severe injuries and fatalities following motor vehicle crashes (MVCs), yet existing studies often rely on hospital-based data, potentially underestimating mortality risk. This study aims to identify predictors of 30-day mortality among older riders by leveraging both population-based and hospital-based samples. We conducted a retrospective cohort study using Taiwan’s Police-Reported Traffic Accident Registry (2019–2021), Taiwan Death Registry, and Taiwan National Health Insurance database (2016–2021). Two cohorts were established: a population-based sample of older motorized two-wheeler riders involved in crashes and a hospital-based sample of those admitted on the same day or one day after MVCs. The primary outcome was 30-day all-cause mortality. Predictive variables included injury severity, demographics, comorbidities, behaviors, and environmental factors. Logistic regression models assessed predictors, with model performance evaluated using the area under the receiver operating characteristic curve (AUC). Significant predictors of 30-day mortality included severe injury, head and neck trauma, older age, alcohol consumption, unlicensed riding, and crash location. Diabetes mellitus was a significant predictor in the population-based sample but not in the hospital-based sample, highlighting differences in data capture. Both models showed acceptable predictive ability, with AUC values of 0.80 and 0.84, respectively. A prehospital screening tool based on population-based data demonstrated effective early risk estimation at the crash scene. A population-based screening tool based on prehospital information may facilitate timely intervention and enhance trauma care for older motorized two-wheeler riders, supporting improved outcomes in aging populations.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108298"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145407795","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":"Evaluating the impact of transfer passage on emergency evacuation safety in transit hub","authors":"Kongjin Zhu,&nbsp;Ruihuang Yang,&nbsp;Tao Wang,&nbsp;Wenting Hu,&nbsp;Ning Guo","doi":"10.1016/j.aap.2025.108281","DOIUrl":"10.1016/j.aap.2025.108281","url":null,"abstract":"<div><div>Subway networks serve as the arterial transit for millions of urban commuters every day in large metropolitan areas. The evacuation efficiency of passengers in subway stations during emergencies is directly linked to the safety of travelers. This study investigates the evacuation safety problem in a subway transfer station, which are particularly difficult to analyze due to their complex structural layouts. To this end, a simulation framework is developed based on the floor field cellular automata model. This model is extended by incorporating a load-balancing mechanism at the automatic fare gate to simulate staff guidance. It also incorporates a multi-exit selection mechanism and inter-floor transfer rules for passengers within multi-level subway transfer stations. Based on this framework, this study systematically investigates the impact of enabling and controlling passenger flow through transfer passages on overall evacuation efficiency and safety. The results indicate that when the transfer passage is available, it is necessary to control the proportion of people utilizing the transfer passage to effectively utilize spatial resources. Otherwise, certain levels may become overly congested, thereby increasing evacuation risks. Our research provides insights for evacuation management in crowded transportation hubs like subway stations, and our methodological framework can also be adapted to various transfer subway stations, thereby optimizing their safety management.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108281"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145407750","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":"Characteristics analysis of autonomous vehicle pre-crash scenarios","authors":"Yixuan Li ,&nbsp;Xuesong Wang ,&nbsp;Tianyi Wang ,&nbsp;Lishengsa Yue ,&nbsp;Qian Liu","doi":"10.1016/j.aap.2025.108285","DOIUrl":"10.1016/j.aap.2025.108285","url":null,"abstract":"<div><div>To date, hundreds of crashes have occurred in open-road testing of autonomous vehicles (AVs), highlighting the need for improving AV reliability and safety. However, current studies predominantly analyze crash data based on oversimplified classification schemes that lack clear scenario definitions. Consequently, they impede an in-depth investigation of crash characteristics. Pre-crash scenario typology classifies crashes based on vehicle dynamics and kinematics features. Building on this, characteristics analysis can identify similar features under comparable crashes, offering a more effective reflection of general crash patterns and providing more targeted recommendations for enhancing AV performance. In this paper, we initially collected the latest 774 California AV crash reports, then selected 384 autonomous mode crashes, and used the newly revised pre-crash scenario typology to identify AV pre-crash scenarios. To improve the efficiency of scenario identification and adaptability to future updates in scenario typology, we proposed a set of mapping rules to extract pre-crash scenarios automatically. We successfully identified 27 types of AV pre-crash scenarios with an accuracy of 98.1%. Through detailed analysis, we obtained two key groups of AV pre-crash scenarios: rear-end scenarios and intersection scenarios. Based on the abundance of crash data, we adopted different analysis methods to analyze the features of key scenarios. Association analysis of rear-end scenarios showed that the significant environmental influencing factors were traffic control type, location type, light, etc. For intersection scenarios prone to severe crashes with detailed descriptions, we employed causal analysis to obtain the significant causal factors: habitual violations and temporary obstruction of view. The extracted scenarios in this paper and their features can assist in constructing the AV simulation test with precise environmental parameters and realistic interactions with other traffic parties. The resulting optimization recommendations can inform regulators and reveal control-algorithm weaknesses across diverse real-world conditions, thereby enhancing the AV safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108285"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145414254","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 hybrid statistical-machine learning methodology for addressing endogeneity and temporal instability in speeding-crash frequency relationships","authors":"Sajad Asadi Ghalehni ,&nbsp;Amir Pooyan Afghari","doi":"10.1016/j.aap.2025.108284","DOIUrl":"10.1016/j.aap.2025.108284","url":null,"abstract":"<div><div>Speeding is a key behavioural factor contributing to increased crash frequencies along road segments, especially horizontal curves. Estimating the effect of speeding on crashes is, however, very challenging due to several reasons. Traditional speeding data collection methods often introduce measurement error in the analysis. In addition, there is a complex inter-relationship between driver behaviour, roadway geometry, and crash risk leading to endogeneity between speeding and crash risk. While instrumental variable modelling has been previously used for addressing such endogeneity, the effectiveness of this technique depends on strong instruments that correlate well with speeding but not with crashes. Moreover, the effects of explanatory variables on crashes may vary across locations and time too.</div><div>This study aims to address these gaps by developing a new methodology combining improved data collection and a hybrid statistical-machine learning model for better identification of speeding and a more accurate estimation of its effect on crashes. The model, tested on 179 km of horizontal curves along rural roads in Iran, integrates negative binomial regression and gradient boosting with shapley values. The negative binomial model is specified with random parameters and mixed spline indicators accounting for unobserved heterogeneity and temporal instability in the data. Results indicate high predictive power of the machine learning model in predicting speeding from exogenous variables, complemented by intuitive shapley values and feature importance for those variables. A comparison of statistical fit between the proposed model and several state-of-the-art modelling candidates showed that our model is superior to the existing modelling techniques. The results of this model suggest that curve’s geometry and traffic characteristics are strong predictors of speeding, while driving more than 20 % over the speed limit substantially contributes to increased crash frequency. The effects of passenger and heavy vehicle traffic on crashes change over time.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108284"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361403","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":"Quit playing games with our lives: Layoffs predict road traffic fatalities","authors":"Dritjon Gruda ,&nbsp;Ricardo Gonçalves ,&nbsp;Milad Sharafi Zadegan","doi":"10.1016/j.aap.2025.108302","DOIUrl":"10.1016/j.aap.2025.108302","url":null,"abstract":"<div><div>Economic downturns are typically associated with fewer traffic accidents due to reduced driving. However, the psychological and social shocks of sudden job loss may counterintuitively increase risk on the road. In this paper, we examine whether mass layoffs announcements are associated with short-term increases in traffic fatalities in the United States using spatial autoregressive models. Merging monthly U.S. county-level data on mass layoffs with motor vehicle fatality counts, we find a significant uptick in monthly traffic fatalities following major layoff events. This pattern persists after accounting for seasonal trends and regional factors, including unemployment rates and weather conditions. These findings suggest that the stress and disruption caused by mass layoffs can have deadly consequences beyond the workplace. We discuss psychological mechanisms (e.g., distress-related driving impairment) and implications linked to short-term rises in traffic fatalities and public health implications for fatal crash risk.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108302"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450615","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":"Application of the dual-model interpretability framework of XGBoost-SHAP and GAT-GNNExplainer to investigate the impact of the built environment on traffic accidents at intersections","authors":"Ying Chen,&nbsp;Jiaxun Zhu,&nbsp;Jierong Long,&nbsp;Haoxiang Lin,&nbsp;Haoye Liu,&nbsp;Bolin Xiang","doi":"10.1016/j.aap.2025.108305","DOIUrl":"10.1016/j.aap.2025.108305","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Exploring the link of characteristics of the built environment and traffic accidents is crucial for enhancing road safety. Prior research has predominantly utilized the “5D” framework to examine how the built environment influences accident rates. However, intersections, which are high-risk areas for traffic accidents, are significantly influenced by their surrounding streetscape features. These features have not been systematically integrated into the existing “5D” framework of the built environment. Moreover, previous studies considered intersections as isolated units, employing analytical models that presuppose sample independence and neglect the spatial spillover effects of explanatory variables due to the interconnected nature of these units. To overcome these limitations, this study introduces a sixth dimension to the built environment, the Detailing of Intersection Interface, by incorporating surrounding streetscape features at intersections. This dimension is quantified using semantic segmentation techniques to calculate the pixel proportions of static streetscape elements from Street View Images (SVIs). Furthermore, to capture the spatial spillover effects of built environment characteristics at neighboring intersections, a spatial topology graph was constructed, defining intersections as nodes and road connections as edges. A Graph Attention Network (GAT) model was subsequently developed to predict accident occurrences at intersections, effectively accounting for the spatial dependencies of accident risks. A dual-model interpretability framework, combining eXtreme Gradient Boosting-Shapley Additive exPlanations (XGBoost-SHAP) and GAT-GNNExplainer, was established to explore the association between built environment characteristics and accident occurrence from global, neighborhood, and individual perspectives. The results demonstrate that the two-layer GAT model (GAT-2) achieved an accuracy of 80.05%, marking a significant improvement in predictive performance by accounting for the spatial spillover effects of built environment features at nearby intersections. From the global perspective, critical factors influencing intersection traffic accidents include POI density, building density, traffic facility density, and distance to bus. These factors exhibit significant nonlinear and threshold effects, as well as strong interaction effects among them. From the neighborhood perspective, intersection traffic accidents are influenced by local built environment features but also by those of adjacent intersections, and the GAT-GNNExplainer framework effectively identified the key neighboring intersections and their key features contributing to accident occurrence, thereby effectively quantifying the spatial spillover effects of built environment features. From the individual perspective, the XGBoost-SHAP interpretability framework quantified the contributions of local built environment features to the prediction results. Overall, the pr","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108305"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145463901","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 new framework for modelling traffic conflict interval time based on correlated random parameter duration model with heterogeneity in means and variances","authors":"Gen Li ,&nbsp;Shengxu Ji ,&nbsp;Wanrong Cheng ,&nbsp;Weiyun Wang ,&nbsp;Xuyichen Yan ,&nbsp;Zhen Yang ,&nbsp;Yang Yang ,&nbsp;Jie Zhang","doi":"10.1016/j.aap.2025.108282","DOIUrl":"10.1016/j.aap.2025.108282","url":null,"abstract":"<div><div>Traffic conflict interval time on highways not only impacts traffic flow stability, heightens the risk of secondary conflicts, and adds uncertainty. To explore the impact of various factors on conflict interval time, this study proposes the correlated random parameters accelerated failure time (AFT) model with heterogeneity in means and variances (CRPHMV), aiming to uncover the correlations and heterogeneity among influencing factors. Given the High D trajectory data, the CRPHMV AFT model demonstrates superior fitting performance compared to other advanced models. Specifically, four variables—mean speed, acceleration variance, traffic flow, and high-risk conflicts exhibit heterogeneity effects. Further analysis reveals that the mean heterogeneity of medium-risk conflicts<!--> <!-->versus acceleration variance indicators, and vehicle type versus acceleration variance indicators, tends to shorten the conflict interval time, while the mean heterogeneity of speed difference between lanes versus acceleration variance indicators is associated with an increase in the conflict interval time. Interaction effects between the unobserved heterogeneity of mean speed and high-risk conflicts indicators, as well as, mean speed and acceleration variance indicators, were found to significantly impact conflict interval time. These research findings can provide valuable insights for traffic management authorities to further enhance road safety and make informed decisions regarding traffic control measures and regulations.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108282"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145407807","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":"The Manchester driving behavior questionnaire (DBQ) integrating health and technology factors: The DBQ 2025 update with translations in 11 languages","authors":"Sepideh Harzand-Jadidi ,&nbsp;David C. Schwebel ,&nbsp;Ali Jafari-Khounigh ,&nbsp;Derya Azık ,&nbsp;Praveen Maghelal ,&nbsp;Giuseppina Spano ,&nbsp;Beata Maria De Ocampo ,&nbsp;Marie-Axelle Granié ,&nbsp;Davoud Khorasani-Zavareh ,&nbsp;Shahrzad Bazargan-Hejazi ,&nbsp;Andrea G. Eckhardt ,&nbsp;Martina Smorti ,&nbsp;Jiabin Shen ,&nbsp;Veronica Diaz Mendoza ,&nbsp;Mark J.M. Sullman ,&nbsp;Reza Mohammadi ,&nbsp;Homayoun Sadeghi-bazargani","doi":"10.1016/j.aap.2025.108278","DOIUrl":"10.1016/j.aap.2025.108278","url":null,"abstract":"<div><div>The Manchester Driving Behavior Questionnaire is a widely-used instrument to assess driving behavior but has become outdated, omitting items addressing modern communication technologies and health-related issues that impact today’s drivers. This study updated the original instrument with relevant health and technology items. The instrument updating process involved 5 steps. First, a literature review identified new and relevant items from existing instruments. Second, an international team with expertise in driving behavior, including the original instrument developer, reviewed and suggested revisions, assessed face validity, and recommend changes iteratively. Third, content validity was evaluated via computation of a content validity index (CVI) and content validity ratios (CVR). Fourth, the updated English version was translated into 11 languages by a global team. Finally, reliability of the Persian version was assessed via Cronbach’s alpha and intra-class correlation coefficient. The literature review led to new items addressing topics such as smartphone use and health conditions that may impact safe driving. Experts refined these items iteratively, creating an updated MDBQ with the 27 original items and 12 newly-developed ones. Content validity was assessed, yielding average CVIs of 0.95 and CVRs of 0.87. The questionnaire was then translated into 11 languages (Arabic, Azerbaijani Turkish, Chinese, French, German, Italian, Persian, Spanish, Swedish, Tagalog, and Turkish) according to standardized methods. The updated instrument retained all components of the original MDBQ, preserving comparability with existing data while also assessing contemporary topics. It provides a comprehensive instrument to assess driving behaviors and is recommended for use in research, policy, public health, and intervention development.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108278"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450555","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":"Towards Adaptive Autonomous Vehicle Systems: Considering Trust and Risk Perception during Failures","authors":"Cherin Lim,&nbsp;Prashanth Rajivan","doi":"10.1016/j.aap.2025.108267","DOIUrl":"10.1016/j.aap.2025.108267","url":null,"abstract":"<div><div>As autonomous vehicles (AVs) increasingly operate in unpredictable environments, their function is shifting from simply being modes of transportation to becoming active collaborators alongside human drivers. In turn, drivers must assume a cooperative role, working effectively with autonomous systems to achieve shared goals, most importantly, ensuring human safety. Despite considerable progress, real-world usage and testing of AVs continue to highlight vulnerabilities, particularly in safety-critical situations. This study examines how vehicle failure, the type of failure (security vs. mechanical), and the scenario context influence drivers’ trust and risk perception toward AVs. Initially, six categories of failure scenarios were identified using data from the California Department of Motor Vehicles’ disengagement reports. Subsequently, an online experiment was conducted, where participants experienced both baseline (normal operation) and failure drives. In the failure drive, participants encountered one specific failure type and scenario. The results revealed a substantial decrease in driver trust following the failure drive across all conditions. Higher perceived risk was associated with reduced trust and lower risk-taking behaviors. Scenarios were classified into high- and low-risk categories, with control-related issues having the most pronounced effect on increasing perceived risk. The experiment found no difference in trust or risk perception whether the failure was caused by a malicious intervention or a mechanical issue. These findings suggest that AV design should incorporate mechanisms for assessing and appropriately responding to varying risk levels in critical situations. Enhancing this capability will strengthen the collaborative relationship between humans and AVs, ultimately improving safety and reliability in human-machine teaming contexts.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"224 ","pages":"Article 108267"},"PeriodicalIF":6.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361347","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}