Accident; analysis and prevention最新文献

{"title":"Assessing driver reactions to emergency navigation prompts in head-up displays","authors":"Maximilian David ,&nbsp;Dirk Kemper ,&nbsp;Martin Baumann ,&nbsp;Alvaro Garcia-Hernandez","doi":"10.1016/j.aap.2025.108199","DOIUrl":"10.1016/j.aap.2025.108199","url":null,"abstract":"<div><div>This study examines the effectiveness of warning messages via Head-up displays (HUDs) that provided navigation-based evasion instructions in enhancing driver performance and safety during critical traffic events. A dual-method approach was employed, combining an online survey with a driving simulator study. The online survey evaluated drivers’ initial reactions to different visual HUD designs, such as directional arrows, lane markings, and textual cues, within a simulated emergency scenario. The insights gained from the survey were used to identify the most effective warning design, which was subsequently implemented in a driving simulator experiment. There, the real-time responses of drivers to the preferred HUD design were examined under controlled conditions. The results show that the instructions improve drivers’ understanding of the required actions and significantly reduce reaction time to approaching emergency vehicles, as reflected by behavioural and perceptual indicators associated with improved situational awareness. Participants who received visual instructions in the HUD reported reduced cognitive workload, greater confidence in their actions and a more accurate understanding of lane navigation requirements. Data from eye movement analyses and lane deviation analyses confirmed faster and more precise reactions in critical situations, such as forming an emergency lane, while physiological measurements, including heart rate, showed no significant increase in stress levels. These results highlight the potential of head-up displays as effective tools for improving traffic safety and optimising rescue chain operations during critical incidents.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108199"},"PeriodicalIF":6.2,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852623","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":"Integrating crash and fluids toxicology data to examine injury outcomes and associated driver behaviors","authors":"Marisa E. Auguste ,&nbsp;Jennifer Pawelzik ,&nbsp;Caroline Scholz","doi":"10.1016/j.aap.2025.108200","DOIUrl":"10.1016/j.aap.2025.108200","url":null,"abstract":"<div><h3>Objectives</h3><div>To examine linked data of drug- and alcohol-involved driving in the State of Connecticut and the resulting association between driver behavior and injury outcomes from motor vehicle crashes.</div></div><div><h3>Methodology</h3><div>Logistic regression and correlation analysis were conducted on linked toxicology (urine, blood, serum, vitreous) and crash records for the period of 2017 to 2023. Descriptive analysis and simple (Chi²) inferential tests of demographic and crash factors were also conducted. Association of injury outcomes with crash and driver behavior characteristics was measured with estimated odds ratios.</div></div><div><h3>Results</h3><div>Older age, speeding, lack of safety equipment, testing positive for alcohol alone or with cannabis, and other drugs were significant predictors of driver injury. Gender was not significant. Speeding, lack of safety equipment, and a driver testing positive for alcohol or cannabis alone, or in combination, or for drugs other than cannabis significantly increased the odds of injury for all crash victims; age was not a significant predictor of overall crash severity. Counterintuitively, driver errors served as protective factors for both outcome variables, suggesting other predictors may have masked true relationships.</div></div><div><h3>Conclusions</h3><div>Study aims have resulted in improved analysis of crash data with the addition of drug classifications. Findings indicate that research of impaired driving behaviors and crash risk can be strengthened through data linkage. While a significant relationship was identified with most predictors, lack of restraint use emerged as the strongest predictor, increasing odds of severe injury nearly 20 times. Driver errors and substance use behaviors require a more thorough examination of their relationship with injury outcomes.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108200"},"PeriodicalIF":6.2,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852656","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":"High-dimensional functional boundaries search for deviation-robust testing of autonomous driving system","authors":"Yunwei Li ,&nbsp;Siyu Wu ,&nbsp;Anran Wang ,&nbsp;Lan Yang ,&nbsp;Hong Wang ,&nbsp;Jun Li ,&nbsp;Chaosheng Huang","doi":"10.1016/j.aap.2025.108156","DOIUrl":"10.1016/j.aap.2025.108156","url":null,"abstract":"<div><div>Testing and evaluation are essential for verifying the safety of the intended function (SOTIF) of autonomous driving systems (ADS), which focuses on estimating the system’s functional boundaries through a limited set of tests to assess its safe operational range. To achieve this, a series of valuable safety-margin scenarios must be designed as test cases. However, scenario testing faces the dilemma of the curse of dimensionality and the requirements for test coverage. Consequently, the construction and selection of test cases become significant challenges. Moreover, due to the black-box nature of the system under test (SUT), surrogate models are often introduced during the scenario generation process, which can introduce model deviation relative to the actual system and potentially lead to ineffective test scenarios as well as incorrect estimation of system functional boundaries (SFB). To address these challenges, an efficient framework for generating high-dimensional safety-margin scenarios and tracking SFB of SUT is proposed, which utilizes a baseline surrogate model to generate a diverse and comprehensive library of safety-margin test scenarios through a multi-population genetic algorithm (MPGA). Additionally, a System Functional Boundary Tracking (SFBT) module is employed to compensate for the deviation between the baseline surrogate model and the actual SUT, thereby adaptively generalizing the library of critical scenarios to estimate its high-dimensional functional boundaries. This framework will potentially assist in the testing and validation of the Operational Design Domain (ODD) for ADS.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108156"},"PeriodicalIF":6.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830506","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":"Hazard analysis of an Automated Lane Keeping System using Systems-Theoretic Process Analysis","authors":"Mariat James Elizebeth,&nbsp;Siddartha Khastgir,&nbsp;Paul Jennings","doi":"10.1016/j.aap.2025.108171","DOIUrl":"10.1016/j.aap.2025.108171","url":null,"abstract":"<div><div>Systems-Theoretic Process Analysis (STPA) is an effective safety analysis technique that identifies how unsafe interactions among components within a complex system may result in accidents. This study aimed to evaluate the efficacy of STPA by applying it to an Automated Lane Keeping System (ALKS). The goal was to explore areas of potential risk in the system and make recommendations on how overall system safety could be improved. The STPA analysis of ALKS identified 87 Unsafe Control Actions (UCAs) based on interactions between the various components. An analysis of the UCAs revealed 537 causal factors (CFs), including software faults like flawed control algorithms and conflicting controls, sensor performance limitations, specification issues such as missing feedback signals, and errors in human–machine interaction, such as excessive dependence on the ALKS and drivers having incorrect expectations regarding ALKS operation. 1074 requirements were proposed to prevent or mitigate these causal factors, such as educating drivers about both the benefits and limitations of the ALKS to ensure safe use. The results highlighted the importance of communicating both the capabilities as well as the limitations of modern complex systems to the users to guarantee safety. This study, which is the first comprehensive application of STPA to ALKS, identified gaps with existing regulatory requirements for ALKS, and 87 recommendations were made to bridge these gaps. Our research has shown that this top-down, well-structured, and holistic method can especially be advantageous for regulators and policymakers to formulate requirements and policies to deploy and operate complex, innovative technologies, safely.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108171"},"PeriodicalIF":6.2,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831625","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 effect of data transformation on the severe event prediction in road traffic using extreme value theory","authors":"Zhankun Chen,&nbsp;Carl Johnsson,&nbsp;Carmelo D’Agostino","doi":"10.1016/j.aap.2025.108186","DOIUrl":"10.1016/j.aap.2025.108186","url":null,"abstract":"<div><div>Extreme Value Theory (EVT) is the state-of-the-art method for proactive prediction of accident frequency from traffic interactions on a microscopic scale. The main advantage of using EVT is to predict unobserved critical events based on one or more Surrogate Measures of Safety (SMoS) (single- or multivariate EVT) through a mathematical extrapolation of extreme interactions. Such interactions are quantitatively described by SMoS, which commonly measure the proximity of two road users, increasing the probability of a collision as the proximity decreases. Those events with a higher likelihood of turning into an accident are defined as severe interactions, and they are considered extremes and are used in the EVT model. Since EVT analysis focuses on the upper tail of the distribution, decreasing transformations are a prerequisite, without which it is impossible to model the extremes. However, prediction results depend on the shape of the indicators’ distributions. Some studies use simple transformations, such as negation, while others employ nonlinear methods that adjust the relationship between proximity and severity. In the present study, the theory of tail analysis has been used to rigorously formulate the effect of a set of conventional linear and nonlinear transformations of SMoS. The approach was tested on a Swedish dataset, and the effects of the transformations on the prediction of extreme events were evaluated based on an accident model built on local data and Empirical Byes correction. The novelty of this study is that one of the most fundamental concepts in traffic conflict theory, such as conflict-crash relationships, has been examined with mathematical interpretation. The results of this study can be further extended to become a standard procedure in modelling traffic conflicts using EVT.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108186"},"PeriodicalIF":6.2,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826321","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 model based on cyclist fall experiments which predicts the maximum allowable handlebar disturbance from which a cyclist can recover balance","authors":"Marco M. Reijne ,&nbsp;Frank H. van der Meulen ,&nbsp;Frans C.T. van der Helm ,&nbsp;Arend L. Schwab","doi":"10.1016/j.aap.2025.108159","DOIUrl":"10.1016/j.aap.2025.108159","url":null,"abstract":"<div><div>Falls are a significant cause of injury among cyclists, highlighting the need for effective fall prevention interventions. However, ex-ante evaluation of such interventions remains challenging for engineers designing safer infrastructure and bicycles, as well as for safety professionals developing training programs. This study proposes the Maximum Allowable Handlebar Disturbance (MAHD) — the largest external handlebar disturbance a cyclist can recover from — as a performance indicator for evaluating fall prevention interventions. While bicycle dynamics and cyclist control models have the potential to determine this indicator and simulate interventions, their application is currently limited by a lack of validation in predicting the MAHD and the narrow range of interventions that can be incorporated into existing cyclist control models. To address these limitations, we conducted controlled experiments with 24 participants of varying ages and skill levels, exposing them to impulse-like handlebar disturbances that resulted in both recoveries and falls. This dataset, which includes recorded cyclist falls, supports future validation of bicycle dynamics and control models in predicting the MAHD. In addition, using Bayesian Model Averaging, we identified key cyclist factors influencing the MAHD, with forward speed and cyclist balancing skill being critical predictors. Incorporating these predictors into cyclist control models can substantially improve their practical application. These insights were then used to develop a Bayesian multilevel logistic regression model to predict the MAHD for different types of cyclists. Our findings improve the potential for bicycle dynamics and control models to proactively evaluate cyclist fall prevention methods, contributing to safer cycling environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108159"},"PeriodicalIF":6.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781878","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":"Experts’ perspectives on shared responsibility for speed management: A thematic analysis informed by systems thinking","authors":"Maria Eugenia Keller,&nbsp;Barry Watson,&nbsp;Sherrie-Anne Kaye,&nbsp;Mark King,&nbsp;Ioni Lewis","doi":"10.1016/j.aap.2025.108185","DOIUrl":"10.1016/j.aap.2025.108185","url":null,"abstract":"<div><div>Sharing responsibility for road safety is a key principle of the Safe System Approach, but little practical guidance has been provided on its implementation. This article utilises a systems thinking lens to explore how the concept of shared responsibility for speed management is understood and operationalised. The study was informed by thirty-three semi-structured interviews with road safety experts and practitioners from varied backgrounds, mostly from Sweden and Australia. A reflexive thematic analysis exploring perceptions around the concept of shared responsibility for speed management and associated emerging challenges was conducted, from which four themes were generated. The first of these themes suggested that responsibility in this context can be understood as being anchored in legal frameworks, in moral imperatives or as related to crash causality factors. The second theme gathered shared patterns of meaning around competing mindsets with very different explanations into how road safety results are delivered, with implications for effectively sharing responsibility for speed management. Theme three suggested that sharing responsibility for speed management can be enhanced by stakeholders’ goal alignment. Finally, the fourth theme suggested the need to modify the speed management’s governance framework, including reassessing the roles, responsibilities and accountability of stakeholders as well as the transparency of policy processes. This study suggests challenges may arise in some contexts in operationalising the concept of shared responsibility for speed management. Practical implications include developing practitioner guidelines providing conceptual clarity and tools to improve speed management governance and responsibility design, tying performance metrics to individual and collective responsibilities and enhancing transparency.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108185"},"PeriodicalIF":6.2,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771915","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 validity of self-assessment predicts on-road driving performance beyond the effects of age and sex in older drivers with and without MCI","authors":"Daniel A. Schlueter,&nbsp;Kim L. Austerschmidt,&nbsp;Jessica Koenig,&nbsp;Maximilian Flieger,&nbsp;Julia Bergerhausen,&nbsp;Thomas Beblo,&nbsp;Martin Driessen,&nbsp;Max Toepper","doi":"10.1016/j.aap.2025.108172","DOIUrl":"10.1016/j.aap.2025.108172","url":null,"abstract":"<div><h3>Objectives</h3><div>Higher age is often seen as a key factor in the decline of driving skills. Moreover, there is some evidence that overestimation is related to both higher age and poorer on-road performance in older drivers. However, it is unknown how the extent of overestimation or underestimation affects on-road driving performance beyond age.</div></div><div><h3>Methods</h3><div>112 older drivers with and without mild cognitive impairment participated in this prospective on-road study. All participants underwent a standardized on-road driving assessment, neuropsychological testing, collection of driving-related data and different self-assessments. Statistical analyses included a hierarchical regression analysis to predict on-road driving performance by adding age and sex in the first step and the validity of self-assessment (VSA) in the second step. Correlation analyses focused on the association between VSA and cognitive and driving-related behavioral factors.</div></div><div><h3>Results</h3><div>Results revealed that the combination of age and sex significantly predicted on-road driving skills (<em>R²_adjusted</em> = 0.320). The inclusion of VSA led to a significant increase of explained variance in the criterion (<em>R²_adjusted</em> = 0.639). Moreover, the degree of overestimation correlated with higher age, lower cognitive performance and more risky driving behavior.</div></div><div><h3>Discussion</h3><div>Our results highlight the importance of the VSA for on-road driving performance beyond the effects of age and sex. Moreover, the VSA appears to decrease towards overestimation with increasing age and decreasing cognition and should thus be a focus of safety research.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108172"},"PeriodicalIF":6.2,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739513","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":"Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach","authors":"Lei Han,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.aap.2025.108180","DOIUrl":"10.1016/j.aap.2025.108180","url":null,"abstract":"<div><div>Existing intersection safety analysis studies have primarily focused on macro-level static infrastructure and highly aggregated traffic features. The emergence of Connected Vehicle (CV) has enabled researchers to extract micro-level driving behavior attributes from CVs. Although longitudinal driving behaviors (e.g., hard braking) have been studied recently, critical lateral left and right turn behaviors, which are common and pose potential conflict risk at intersections, have been largely overlooked. Meanwhile, dealing with both spatial heterogeneity and nonlinear effects between crash frequency and multitudinous driving features is another critical challenge for intersection safety analysis. To address such gaps, this study extracted driving behavior features for both longitudinal movements and lateral left and right turns to comprehensively capture driving dynamics at intersections. A novel spatial ML framework was proposed to integrate nonlinear ML models (e.g., LightGBM) with geographically weighted regression: Besides a global ML model training on all samples to fit average estimations, distinct local ML models are trained for each spatial sample with its neighbors to capture localized spatial heterogeneity. Empirical experiments using CV data at a Florida county show that the inclusion of lateral turning behavior (e.g., hard left/right turns) leads to improved accuracy of intersection crash frequency prediction. Compared to traditional Rrandom Forest, XGBoost, LightGBM, and Multilayer Perceptron models, the spatial ML integrating LightGBM demonstrates significant improvements of 5.8%, 6.3%, and 5.6% in RMSE, MAE, and R², respectively. The results reveal the nonlinear impact of driving features and their spatial heterogeneity: In downtown, hard braking events primarily influence the risk of rear-end (RE) crashes. Drivers’ acceleration also is more likely to lead to RE crashes in urban areas. While hard left turns show greater influence of sideswipe and left turn crashes at suburban intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108180"},"PeriodicalIF":6.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739512","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":"Safe streets for cyclists? Quantifying the causal impact of cycling infrastructure interventions on safety","authors":"Anupriya ,&nbsp;Xiaowei Zhu ,&nbsp;Emma McCoy ,&nbsp;Daniel J. Graham","doi":"10.1016/j.aap.2025.108168","DOIUrl":"10.1016/j.aap.2025.108168","url":null,"abstract":"<div><div>London’s Cycle Superhighways (CS) form a network of cycle routes connecting central London to outer boroughs, introduced in 2010 to promote cycling and improve safety. This paper examines their causal impact on cycling volume and safety using detailed road traffic and road safety data from the UK’s Department for Transport. To estimate these effects, we employ propensity score-matched difference-in-differences and panel outcome regression models, comparing two distinct infrastructure types: segregated and non-segregated CS. A key contribution of this study is the development of a novel safety indicator — the normalised collision rate — that accounts for changes in cyclist volume (exposure) while incorporating expected non-linearities in the relationship between collisions and exposure. Our findings indicate that non-segregated CS did not increase cycling volume but led to a substantially higher collision rate. This increase appears to be driven by a post-intervention surge in the proportion of new, inexperienced cyclists along these routes. In contrast, segregated CS effectively increased cycling volume without increasing collision rates. Further, an evaluation of a major segregation upgrade along an existing non-segregated CS route revealed a notable reduction in collision rates. These results highlight the crucial role of segregated infrastructure in not only encouraging cycling but also ensuring it remains a safe and viable urban transport option.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108168"},"PeriodicalIF":6.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725094","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}