Accident; analysis and prevention最新文献

{"title":"A personalized driving risk assessment and rolling prediction method by integrating multiple indicators","authors":"Yuran Li ,&nbsp;Guizhen Chen ,&nbsp;Yikai Luo ,&nbsp;Bangju Chen ,&nbsp;Jin Shao ,&nbsp;Yan Li","doi":"10.1016/j.aap.2025.108201","DOIUrl":"10.1016/j.aap.2025.108201","url":null,"abstract":"<div><div>There are significant differences in driving styles among different drivers. A personalized perspective on driving risk assessment and prediction can help to proactively prevent traffic safety and provide a risk prediction basis for intelligent driver assistance systems. A personalized driving risk assessment and rolling prediction method is proposed to predict a driver’s potential accident risk in real-time by integrating vehicle dynamics and Heart Rate Variability (HRV) indicators. A natural driving experiment is designed to obtain Critical Incident Events (CIEs) and changes in each indicator. The frequency of CIEs is used as a driving risk characterization. To identify the significant personalized indicators affecting various CIEs, an improved Bayesian network model is developed to obtain the influence mechanism of each indicator on CIEs. The Dynamic Time Warping Barycenter Averaging (DBA) method is used to calculate the representative series of each significant indicator, which can obtain the characteristic time series under different risk levels. The weights of each CIE class are calculated by the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to get the risk score calculation rule. These scores are then clustered by the Fuzzy C-means (FCM) algorithm to determine the different risk levels. Finally, the Bayesian Optimization (BO) −based Bidirectional Gated Recurrent Unit (BiGRU) is integrated with a Convolutional Neural Network (CNN) and Extended Kalman Filter (EKF) to construct the BCBGE (BO-CNN-BiGRU-EKF) model, which enables continuous prediction of driving risk. Results from a natural driving experiment involving 60 drivers in Xi’an indicate that driving risk can be grouped into four levels. A personalized risk indicator analysis was conducted for each driver. The results indicate that each type of CIE is associated with three to four key indicators of vehicle dynamics or HRV. When the observation window length is 3.8 s and the prediction window length is 2.4 s, the proposed rolling prediction model achieves an accuracy of 92.03%, which is 3.37% to 15.88% higher than the accuracies obtained using the GRU, Bidirectional Long Short-Term Memory (Bi-LSTM) −EKF, and BO-CNN-BiLSTM-EKF models.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108201"},"PeriodicalIF":6.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866405","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":"Qualitative and quantitative hybrid analysis of heterogeneous crowds involving individuals with diverse types of disabilities passing through bottleneck","authors":"Xiangxia Ren ,&nbsp;Zhiming Fang ,&nbsp;Rui Ye ,&nbsp;Shaocong Xie ,&nbsp;Shuchao Cao ,&nbsp;Jun Zhang","doi":"10.1016/j.aap.2025.108204","DOIUrl":"10.1016/j.aap.2025.108204","url":null,"abstract":"<div><div>This paper presents a series of controlled experiments involving a heterogeneous disabled group composed of individuals with various types of disabilities and normal pedestrians passing through a bottleneck. A hybrid qualitative-quantitative analysis was applied to examine the movement characteristics. The disabilities include physical impairment, lower limb impairment, visual impairment, hearing impairment, mental impairment and intellectual impairments. Qualitatively, typical phenomena of the interaction between the visually and hearing-impaired individuals, and spatial features around the wheelchair users can be observed through the video recordings. Qualitatively, the path length ratio (PLR) and entropy of trajectories for different disabled are calculated and compared. With regard to the typical phenomenon, the Cross-Correlation Function (CCF) was quantified to measures the similarity between the interaction, including “shoulder guide” cooperative movement of the visual disabled and interacting with each other of the hearing disabled. The results show that the speeds of visually impaired individuals and their guides are strongly correlated both temporally and spatially.</div><div>Further, the fundamental diagram of the heterogeneous crowds with various types of disabilities was quantified. The heterogeneous group exhibits slightly higher speeds compared to the elderly group at the same density. Due to the high heterogeneity within the crowd, it is challenging to achieve a stable state at the exit. The impact of heterogeneity on traffic efficiency is assessed through cumulative passage times, flow, and flow rates. It is demonstrated that the traffic efficiency of the heterogeneous group is lower than that of the elderly group and the building code standards. Various parameters such as passage time, actual speed, effective speed, instantaneous speed, stagnation time, and acceleration are calculated to analyze the impact of disability types on pedestrian passage efficiency. Factors contributing to the reduced traffic efficiency of the heterogeneous group are analyzed from temporal and spatial perspectives. Wheelchair users experience more pronounced stoppages due to their larger space requirements. Additionally, the reaction time and the collision avoidance behaviors of pedestrians in the heterogeneous group lead to longer exit intervals. This results in a relatively dispersed concentration of crowd density within the experimental space. These findings underscore the importance of enhancing crowd management and optimizing traffic designs for heterogeneous groups with various types of disabilities for ensuring their mobility and safety. The results provide reliable data support for transportation modifications and guidelines for the development of evacuation models, as well as the formulation of management strategies for heterogeneous populations.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108204"},"PeriodicalIF":6.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866404","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 influence of cognitive distractions and driving experience on hazard perception performance during partially automated driving","authors":"Meng Sun ,&nbsp;Dengbo He","doi":"10.1016/j.aap.2025.108202","DOIUrl":"10.1016/j.aap.2025.108202","url":null,"abstract":"<div><div>Drivers of partially automated vehicles are relieved from operational driving tasks but are still expected to be prepared to assume control of the vehicle when the capabilities of driving automation are exceeded. Thus, drivers’ capability to perceive hazards and react proactively may still benefit driving safety in the context of driving automation. Previous research has found that experience and distractions can affect drivers’ hazard perception performance in vehicles without automation, while the influential factors of hazard perception performance in partially automated vehicles are still unclear. In this study, a driving simulator experiment was conducted to explore the effects of driving experience and cognitive distractions (i.e., auditory n-back task) on hazard perception in partially automated vehicles when drivers are faced with different predictable hazards, i.e., behavioral prediction (BP) hazard, environmental prediction (EP) hazard, and anticipatory prediction (AP) hazard, with the increase of the scenario complexity. In total, 18 experienced and 18 novice drivers drove with adaptive cruise control and lane-centering control systems. We found that experienced drivers exhibited more proactive behaviors than novice drivers when handling AP hazards and were less likely to get involved in crashes. At the same time, cognitive distractions failed to affect drivers’ visual attention behaviors but affected proactive behaviors in response to hazards. Additionally, drivers noticed BP hazards later than EP and AP hazards. This study extends the understanding of drivers’ hazard perception skills, highlights the role of driving experience, and provides insights into training programs in the context of driving automation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"221 ","pages":"Article 108202"},"PeriodicalIF":6.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861106","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":"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}