Accident; analysis and prevention最新文献

{"title":"Ensuring safe operation of autonomous vehicle: A comprehensive survey of Operational Design Condition","authors":"Haowei Xu,&nbsp;Yang Li,&nbsp;Hongmao Qin,&nbsp;Yougang Bian,&nbsp;Shu Jiang,&nbsp;Wenwei Que,&nbsp;Jianzuo Man","doi":"10.1016/j.aap.2025.108366","DOIUrl":"10.1016/j.aap.2025.108366","url":null,"abstract":"<div><div>The rapid evolution of Connected Autonomous Vehicles (CAVs) introduces a growing array of tasks and scenarios, significantly complicating the safe operation of Autonomous Driving Systems (ADS). These systems must navigate rare and unpredictable challenges, such as extreme weather, road irregularities, and unconventional behaviors from other traffic participants. Traditional safety specifications fall short of defining the safe operating conditions required for such complexities. Hence, the paper introduces the Operational Design Condition (ODC) framework that encompasses the status of drivers and passengers, vehicle status, ODD, and other conditions. This survey reviews current approaches to defining and describing the Operational Design Domain (ODD) and provides an in-depth look at the ODC framework for ADS. Moreover, this survey formulates a closed-loop ODC development process by summarizing existing research and leveraging established hazard analysis techniques to set ODC boundaries. In addition, it also discusses ODC monitoring and extension methods, emphasizing the necessity for ongoing research and development. It calls for advancements in standardization, the creation of a more dynamic and adaptable ODC design, the granular ODC mapping and management, the deeper integration of Vehicle-to-Everything (V2X) technologies, and the evolution of ODC testing and validation methods. These efforts are crucial to ensure the safe and effective operation of Autonomous Vehicles (AVs) across various scenarios.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108366"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145832028","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 colored lane markings improve road safety? Causal evidence from Seoul","authors":"Seungyeon Lee ,&nbsp;Eun Hak Lee","doi":"10.1016/j.aap.2025.108336","DOIUrl":"10.1016/j.aap.2025.108336","url":null,"abstract":"<div><div>Colored lane markings are a recent traffic safety intervention in South Korea, designed to improve driver awareness and visual guidance. This study aims to evaluate their effectiveness in reducing traffic crashes. Specifically, 82 road segments in Seoul where the markings were installed were analyzed by comparing crash trends before and after the intervention using data from 2010 to 2024. To estimate the intervention’s effect, a counterfactual analysis was conducted by constructing a baseline scenario representing crash trends in the absence of the intervention. The causal impact of the colored markings was then identified by comparing this baseline with observed outcomes. The results show that the implementation of colored lane markings led to an average 26.7 % reduction in crash rates at statistically significant sites. To identify where the intervention was most effective, the relationship between surrounding land use and observed safety outcomes was examined. The analysis indicates that the markings were more effective on highways and arterial roads, which tend to have higher speeds and simpler traffic conditions. In contrast, roads in dense urban areas showed limited improvements. This outcome is attributable to complex traffic conditions and high levels of visual and environmental clutter. Taken together, these findings suggest that the intervention is highly effective and provides safety benefits on arterial networks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108336"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684091","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":"Modeling interactive crash avoidance behaviors: A multi-agent state-space transformer-enhanced reinforcement learning framework","authors":"Qingwen Pu ,&nbsp;Kun Xie ,&nbsp;Hongyu Guo ,&nbsp;Yuan Zhu","doi":"10.1016/j.aap.2025.108334","DOIUrl":"10.1016/j.aap.2025.108334","url":null,"abstract":"<div><div>Understanding vehicle–pedestrian interactions at urban intersections is critical for enhancing traffic safety. This study aims to model the interactive crash avoidance behavior of road users in near-miss scenarios, addressing the complexities of their decision-making process. Utilizing high-resolution trajectory data collected by unmanned aerial vehicles (UAV), this study proposed a multi-agent state-space Transformer enhanced deep deterministic policy gradient (MA-SST-DDPG) framework to model the vehicle–pedestrian interactions in near-miss scenarios. The framework integrates the state-space model for capturing long-term temporal dependencies and Transformers for dynamically prioritizing critical features, enhancing its ability to learn from rare safety–critical scenarios. The MA-SST-DDPG framework effectively learned sequential decision-making over continuous action spaces, effectively prioritizing critical states and capturing dynamic interactions in vehicle–pedestrian near-miss scenarios. Compared to alternative approaches, it demonstrated superior performance in simulating realistic evasive behaviors. Cross-dataset evaluation confirmed the generalizability of the proposed model on external datasets. Additionally, we employed the proposed model to generate vehicle–pedestrian interactions under varying combinations of initial speeds. Results showed that the simulated interactions successfully replicated the dynamics of real-world near-miss events. Higher initial vehicle and pedestrian speeds were linked to increased conflict rates. Moreover, pedestrians were more likely to yield when vehicles traveled faster and pedestrians walked slower, whereas slower vehicles tended to yield to faster-moving pedestrians. The outcomes of this study can facilitate the development of safety-aware simulations that closely mimic interactive crash avoidance behaviors of road users, paving the way for exploring proactive measures to prevent crashes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108334"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735318","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":"Beyond distraction: unraveling touchscreen effects on driver takeover performance and visual attention dynamics in Level 3 automated driving","authors":"Jiateng Li,&nbsp;Jun Ma","doi":"10.1016/j.aap.2025.108348","DOIUrl":"10.1016/j.aap.2025.108348","url":null,"abstract":"<div><div>Rapid advances in automated driving technology and the widespread adoption of in-vehicle information systems (IVIS) have led to an increasing prevalence of drivers engaging in non-driving-related tasks (NDRTs) during autonomous operation, thereby introducing potential safety hazards. In this study, we conducted a driving simulator experiment with 30 participants to examine the effects of IVIS NDRTs (i.e., navigation, video, audio, and reading tasks) and takeover time budgets on takeover timing, takeover quality, and visual behavior. Results from linear mixed-effects models indicate that IVIS touchscreen interactions significantly prolonged takeover time and lane change time, increased maximum lateral acceleration, and reduced minimum time-to-collision (TTC), suggesting that drivers adopted aggressive control behaviors during takeovers, which in turn elevated collision risk. Moreover, visual behavior analysis revealed an increased proportion of long glances directed away from the forward roadway and a delayed reallocation of visual attention to key regions (such as mirrors, the road, and the malfunctioning vehicle) following the takeover request. These findings enhance our understanding of human factors in automated driving and provide empirical evidence for optimizing driver-vehicle interaction protocols and improving the safety of riding in conditionally automated driving systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108348"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735405","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":"Interpretable analysis of risk factors for intercity bus operation safety on plateau roads using the SHAP method","authors":"Meiping Yun,&nbsp;Hao Zheng,&nbsp;Peiyu Lu","doi":"10.1016/j.aap.2025.108384","DOIUrl":"10.1016/j.aap.2025.108384","url":null,"abstract":"<div><div>Identifying and analyzing factors that influence traffic risk is crucial for reducing crash frequency and severity. This is particularly true for intercity buses operating on high-altitude plateaus, where unique environmental and topographical conditions heighten operational risks. However, quantitative research on how these factors influence traffic operation risk in such contexts, especially across freeways, general highways, and urban roads, remains insufficient. This study uses two months of GPS trajectory data from 10 intercity bus routes in Qinghai province, China to address this gap. We assessed traffic operation risks at discrete road segments using three indicators: Coefficient of Speed Variation (CSV), Severity of Rapid Acceleration (SRA), and Severity of Rapid Deceleration (SRD). A Random Forest regression model, interpreted by the SHAP method, was employed to explore the complex effects of influencing factors. The results quantify the unique risks of the plateau environment, showing that driving instability surges at high altitudes, and the impact is significantly amplified by high speeds. The analysis further highlights the operational risks of intercity buses, identifying a clear fatigue threshold where continuous driving leads to more frequent aggressive maneuvers. Critically, the research differentiates these risks by road type, demonstrating a shift in dominant safety factors, as road geometry poses a greater threat on general highways than on freeways. These findings provide quantitative evidence for developing targeted safety interventions that address the unique operational risks of plateau roadways.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"227 ","pages":"Article 108384"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882095","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":"Interaction patterns exploration and risk assessment within lane changing in terms of initial scenarios and evolution process","authors":"Junhua Wang ,&nbsp;Bo Yao ,&nbsp;Qiangqiang Shangguan ,&nbsp;Ting Fu ,&nbsp;Chengmin Li","doi":"10.1016/j.aap.2025.108358","DOIUrl":"10.1016/j.aap.2025.108358","url":null,"abstract":"<div><div>The lane-changing (LC) process is a sequential interaction process influenced by multiple factors, including the driver’s decision-making, speed, traffic density, road conditions, and the behavior of surrounding vehicles. However, it remains unclear in which scenarios drivers tend to execute lane changes and how the lane changing vehicle and the surrounding vehicles jointly complete the entire lane-changing process in these scenarios. This research establishes a two-level integrated clustering framework tailored to the specifics of LC interactions. At the upper level, the normalized k-means algorithm and pairwise aggregation SSE (sum of the squared errors) are jointly used in this research to handle high-dimensional data and analyze the scenarios in which drivers tend to execute lane changes (i.e., LC initial-scenario). At the lower level, the interaction feature extraction method is utilized to explore the LC interaction patterns corresponding to different LC initial-scenarios. Based on the highD trajectory data and the aforementioned two-level integrated clustering framework, this research identifies 9 major LC initial scenarios and 20 major LC interaction patterns of human drivers, and establishes the relationships between different LC initial scenarios and LC interaction patterns. Particularly, we discover that the interaction patterns could be further categorized into global general pattern, local general pattern, and special pattern. Additionally, by introducing the driving risk quantification indicators, this research identifies risky and safe LC initial scenarios and interaction patterns. The findings offer an in-depth understanding of the entire lane-changing process, providing a theoretical foundation for autonomous driving tests, lane-changing collaborative design, and traffic warning systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"226 ","pages":"Article 108358"},"PeriodicalIF":6.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145779915","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":"Profiling crash-associated factors and injury risk patterns among lost-in-thought (daydreaming) drivers: a combined cluster-sequence analysis approach","authors":"Panick Kalambay ,&nbsp;Philip Balyagati ,&nbsp;Angela Kitali ,&nbsp;Emmanuel Kidando","doi":"10.1016/j.aap.2025.108315","DOIUrl":"10.1016/j.aap.2025.108315","url":null,"abstract":"<div><div>Cognitive distraction, particularly in the form of being lost in thought or daydreaming, is a significant yet underexamined contributor to traffic crashes. This study investigates crash patterns and risk profiles associated with drivers who experienced lost-in-thought distraction at the time of a crash in Washington State. Using cluster correspondence analysis (CCA), distinct associations between crash characteristics and driver attributes were uncovered. In addition, process mining was employed to identify typical sequences of crash events. Three meaningful clusters emerged. Cluster 1 involved crashes on road segments with speed limits exceeding 40 mph, lacking traffic control, and often involving male drivers in clear weather. Cluster 2 was marked by crashes at signalized intersections under partly cloudy or foggy conditions, with a higher likelihood of injury. Cluster 3 reflected rainy or low-light crashes involving young drivers on curved, divided, high-speed roads. Across all clusters, frequent crash sequences included collisions with vehicles in transport, parked cars, and fixed objects. Cluster 1 and Cluster 2 stood out for their distinct contextual characteristics. Cluster 1 crashes often involved crossing the center line, suggestive of deep cognitive distraction. In Cluster 2, crashes frequently occurred after drivers stopped at flashing red lights or stop signs, then proceeded, indicating momentary lapses in attention. The study also highlights the limitations of current crash data and emphasizes the need for standardized reporting of distraction-related incidents. Findings support context-specific countermeasures, such as signal enhancements, curve warnings, and distraction-focused training for novice drivers, to address the multifaceted risks associated with lost-in-thought crashes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108315"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562147","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":"Crash analysis for urban road networks: Semi-parametric spatial negative binomial models with monotonic constraints","authors":"Miaojie Xia ,&nbsp;Li Guan ,&nbsp;Jiang Du ,&nbsp;Yang Wang","doi":"10.1016/j.aap.2025.108329","DOIUrl":"10.1016/j.aap.2025.108329","url":null,"abstract":"<div><div>Spatial heterogeneity constitutes an essential element in advanced crash frequency modeling frameworks. However, current spatial methodologies exhibit two primary limitations: (1) inflexible parametric formulations of exposure variables, and (2) non-conjugate model structures that compromise Gibbs sampler convergence. To address these issues, we develop a semi-parametric spatial count model that introduces flexible exposure specifications, requiring only monotonic functional relationships while explicitly incorporating spatial dependence. This framework employs data-augmented Gibbs sampling to achieve computationally efficient Bayesian estimation through conjugate forms. Simulation results verified that the estimation works as intended. Empirical evaluations across Houston and Dallas urban road networks demonstrated significant performance advantages relative to conventional approaches. Integration of estimates with roadway characteristics and exposure metrics yielded substantive insights into crash patterns within adjacent roadway segments. Road crash analysis in both cities showed three key patterns. First, segment length consistently serves as an offset variable, while annual average daily traffic (AADT) displays complex, segment-varying relationships due to unobserved heterogeneity. In Houston, the effect of AADT on crashes weakens under low traffic volumes but becomes nearly proportional (offset-like) at medium-to-high volumes. Conversely, Dallas exhibits more linear patterns, suggesting city-specific heterogeneity. Second, compared to one-way highway design, other designs generally pose higher risks, with lane number, width, and roadbed width increasing risk to different degrees, while better median designs reduce risk. Finally, both cities exhibit strong spatial heterogeneity in crash count. This framework enables transportation agencies to prioritize road safety interventions through precise quantification of crash risk factors and spatial heterogeneity, while avoiding restrictive parametric assumptions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108329"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615802","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 hazards of skipping meals and losing sleep for ride-hailing drivers—evidence based on quasi-experimental and experience sampling methodology","authors":"Jingming Feng,&nbsp;Jian Chen,&nbsp;Tao Su,&nbsp;Huijian Fu,&nbsp;Ling Tan","doi":"10.1016/j.aap.2025.108304","DOIUrl":"10.1016/j.aap.2025.108304","url":null,"abstract":"<div><div>The rapid growth of ride-hailing services, driven by big data and platform algorithms, has intensified physiological stressors among drivers, particularly during peak mealtime periods, where hunger and sleep deprivation (“skipping meals and losing sleep”) have become prevalent occupational hazards. While prior research has separately documented the adverse effects of hunger and sleep loss on self-control, their synergistic impact on driving safety remains underexplored, especially in ecologically valid settings. Guided by the self-control strength model, this study investigated how hunger and sleep deprivation interact to influence dangerous driving behaviours among ride-hailing drivers, examining the mediating role of self-control resource depletion and the moderating effects of sleep deprivation and work identity perception (contractor vs. worker).</div><div>Using a mixed-methods approach, Study 1 (N = 70 students) employed a 2 × 2 mixed quasi-experiment to establish baseline effects, revealing that hunger directly depleted self-control resources (measured via Stroop task), with sleep deprivation exacerbating this relationship. Study 2 extended these findings to 153 active ride-hailing drivers using a 2 × 2 between-subjects quasi-experiment, confirming that hungry drivers exhibited greater self-control resource depletion than fed drivers, particularly under sleep deprivation. Study 3 utilized experience sampling with 76 drivers (651 data points) to test cross-level mechanisms, demonstrating that hunger indirectly increased dangerous driving behaviors through self-control depletion, with sleep deprivation and worker identity perception amplifying these effects.</div><div>These findings highlight the compounding risks of physiological stressors in ride-hailing work, emphasizing the need for interventions targeting both hunger management and sleep hygiene. The study contributes to accident prevention research by integrating ecological validity with theoretical rigor, offering actionable insights for drivers and platform operators to mitigate fatigue-related safety risks.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108304"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555914","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":"Evaluation of association between observed driving speeds and the occurrence of crashes using naturalistic driving study data","authors":"John N. Ivan ,&nbsp;Yaohua Zhang ,&nbsp;Nalini Ravishanker","doi":"10.1016/j.aap.2025.108335","DOIUrl":"10.1016/j.aap.2025.108335","url":null,"abstract":"<div><div>The SHRP2 Naturalistic Driving Study (NDS) data were used to investigate association between actual driving speeds before known crashes and at other times. Associations were evaluated for the same driver at a location where a crash occurred and similar locations where crashes did not occur, relative to the speeds of other drivers at those locations. It was found that an increase in the speed differential relative to other drivers at the same location between 6 and 10 s before a crash occurred was significantly associated with a crash occurring. The quantile of the average speed over that five-second period served as a better predictor than the quantile of the maximum speed. Crashes were also more associated with road locations classified as limited access highways, minor arterials, and major collectors. These findings are consistent across different drivers and types of road locations. The best-performing model classified all of the crashes in the dataset perfectly, and less than half of the cases classified as crashes were not crashes. This suggests an ability to identify conditions that are at least 50 percent likely to result in a crash. The results could be used by road agencies to identify observed vehicle speed variations that are likely to result in crashes, as well as by vehicle manufacturers to develop algorithms for identifying high-risk conditions for crashes considering speeds of other vehicles in the vicinity.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"225 ","pages":"Article 108335"},"PeriodicalIF":6.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145676087","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}