Accident; analysis and prevention最新文献

{"title":"Evaluating the impact of self-luminous road markings on driver behavior at unsignalized intersections: A simulator study","authors":"Hailin Shi,&nbsp;Feng Chen,&nbsp;Haotian Du,&nbsp;Ting Zhang,&nbsp;Chen Li","doi":"10.1016/j.aap.2025.107967","DOIUrl":"10.1016/j.aap.2025.107967","url":null,"abstract":"<div><div>Unsignalized intersections are accident-prone locations due to numerous conflict points and unclear right-of-way. This issue is exacerbated at night when road markings become less visible, leading to increased accident rates. Self-luminous road markings, a new type of proactive traffic safety control facility, have garnered increasing attention and are being gradually promoted due to their intelligent, stable brightness and variable characteristics. To explore the warning effect of self-luminous road markings at unsignalized intersections at night, this study designed three types of warning schemes: continuous-illuminating pedestrian crosswalk advance warning marking (CPWM), continuous-illuminating yield text advance warning marking (CYWM), and transition-illuminating pedestrian crosswalk advance warning marking (TPWM). Based on previous research, nine indicators were selected for comprehensive evaluation from the perspectives of driver’s operating, visual characteristics, and psychology. Finally, an entropy-based matter-element model was constructed to comprehensively evaluate the warning effect. The results show that the CYWM and CPWM schemes can help drivers effectively reduce the mean speed and potential lateral conflict risk at intersections, enhancing drivers’ awareness of road environment information. However, the TPWM scheme had adverse effects due to greater visual stimulation. The effectiveness of the three schemes ranks from high to low as CYWM, CPWM, and TPWM. This study confirms that self-luminous road markings are effective at intersections, providing new insights for infrastructure upgrades and offering a general framework for evaluating the effectiveness of traffic safety facilities.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107967"},"PeriodicalIF":5.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520707","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 impact of spiral tunnel characteristics on driver HRV and stress perception: A naturalistic driving experiment","authors":"Lei Han ,&nbsp;Zhigang Du ,&nbsp;Shoushuo Wang","doi":"10.1016/j.aap.2025.107983","DOIUrl":"10.1016/j.aap.2025.107983","url":null,"abstract":"<div><div>This study examined the impact of the combined configuration of spiral tunnel length and radius on drivers’ heart rate variability (HRV) and stress perception through a naturalistic driving experiment with 30 participants. Three spiral tunnels varying in both length and radius were evaluated, and the effects of uphill and downhill driving directions were also considered. The results revealed that the combined configuration of tunnel length and radius significantly influenced drivers’ physiological and psychological states. Specifically, longer tunnels with smaller radii were associated with increased average heart rate (HR), decreased standard deviation of normal-to-normal intervals (SDNN), elevated low frequency to high frequency ratio (LF/HF), and reduced sample entropy (SampEn), all indicating heightened stress responses. Uphill driving consistently led to higher average HR, lower SDNN, and higher LF/HF ratio compared to downhill driving, reflecting increased stress due to greater physical and mental demands. These findings offer invaluable insights for the design and management of spiral tunnels, with the ultimate goal of enhancing driver safety and comfort. By optimizing tunnel characteristics and implementing appropriate traffic management strategies, it is possible to create a more favorable driving environment that mitigates the negative impacts on drivers and promotes overall well-being.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107983"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488320","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":"Car-following safety modeling and risk assessment of autonomous vehicle in icy and snowy weather","authors":"Lihua Li ,&nbsp;Chuang Zhou ,&nbsp;Jiaping Huang ,&nbsp;Zhizhen Liu ,&nbsp;Jintao Xie ,&nbsp;Zhe Tan","doi":"10.1016/j.aap.2025.107982","DOIUrl":"10.1016/j.aap.2025.107982","url":null,"abstract":"<div><div>This paper is to study the effect of icy and snowy weather on the car-following (CF) safety of autonomous vehicle (AV). The influence of weather is abstracted as mathematical model parameters, and the CF model and risk decision equation of AV under icy and snowy weather are constructed. Comparing the influence of various climates on the CF of AV and the potential safety hazards, the CF parameters of AV in icy and snowy weather are designed based on Intelligent Driver Model (IDM). The road friction coefficient is matched by the maximum acceleration and comfortable deceleration of the vehicle, and the perception error coefficient is identified by space headway and speed of the vehicle. The Waymo dataset is used as the basic data source, and the safe value interval of icy and snowy parameters is calculated by combining the CF equation and the dataset characteristics. The rationality and stability of the parameters are verified by the root mean square error (RMSE) method and the Wilson model. Using the SUMO platform, single and multiple factors scenes are designed for simulation experiments, and a safety field strength model is constructed to carry out CF risk assessment. It is found that the severity of icy and snowy weather significantly affects the road friction and perception error coefficient, and has strong safety disturbance to the driving speed and real-time headway of AV. The accelerated and decelerated CF will cause oscillation and change of autonomous driving traffic flow, and the fluctuation range and risk degree of the queue caused by decelerated CF is more pronounced than that caused by accelerated CF. The safety effects of CF vary with different icy and snowy coefficients, and the vehicle speed perception error is more likely to induce safety risks. This study further enriches CF methods in special scenes, providing the theoretical basis for AV winter travel.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107982"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488319","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":"Seat belt compliance behavior of drivers and passengers: A review of data collection, analysis, contributing factors and safety countermeasures","authors":"Ahmed Sajid Hasan ,&nbsp;Md.Arifuzzaman Nayeem ,&nbsp;Deep Patel,&nbsp;Omar Al-Sheikh,&nbsp;Mohammad Jalayer","doi":"10.1016/j.aap.2025.107968","DOIUrl":"10.1016/j.aap.2025.107968","url":null,"abstract":"<div><div>Seat belt non-compliance remains a critical global issue, significantly increasing the risk of severe injuries and fatalities in traffic collisions. Despite widespread awareness campaigns and safety advancements, a substantial number of drivers and passengers continue to travel unrestrained. Addressing this issue requires a comprehensive understanding of the seat belt compliance trend across the globe, the factors influencing seat belt use, the ongoing practices to collect and analyze seat belt compliance data, and effective strategies for improving compliance. This study seeks to synthesize existing research on global seat belt compliance behavior by examining contributing factors, advanced data collection methods, analytical techniques, and safety countermeasures. The goal is to identify research gaps and propose strategies to improve compliance and enhance road safety. 75 studies and relevant technical reports published between 2001 and 2023, sourced from databases such as Google Scholar, TRID, ScienceDirect, PubMed, Scopus, and Web of Science, were reviewed using a robust search and selection process. The review highlights that seat belt use is influenced by driver demographics, roadway design, trip features, and temporal factors. It highlights the methods used to collect and analyze seat belt use data, including observational surveys, roadside and in-vehicle cameras, and advanced machine learning techniques such as Convolutional Neural Networks. The analysis also emphasizes the effectiveness of the “3 E’s” approach—engineering, education, and enforcement. The findings demonstrated that the compliance rate across geographic regions varies because of the robustness of the 3E policy. This study identifies gaps in current research and offers actionable strategies to improve seat belt compliance through innovative data collection, analysis, and targeted interventions aimed at enhancing global traffic safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107968"},"PeriodicalIF":5.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale approaches to cope with scale effect issues in macroscopic crash analysis","authors":"Xiaoqi Zhai ,&nbsp;N.N. Sze ,&nbsp;Jaeyoung Jay Lee ,&nbsp;Pengpeng Xu ,&nbsp;Helai Huang","doi":"10.1016/j.aap.2025.107971","DOIUrl":"10.1016/j.aap.2025.107971","url":null,"abstract":"<div><div>Traffic safety has increasingly become an important concern in developing long-term transportation planning strategies. Since transportation planning steps always involve some kinds of geographic entity, predicting crashes for those entities is not only a mere avenue of analytic methods in safety research, but also influential to practical application in road infrastructure design and management. However, the analyses using different spatial units are subjected to the modifiable areal unit problem (MAUP), which refers to the issue of inconsistent statistical results when dealing with geographic data of different aggregation configurations. Especially, a high-level of spatial aggregation of data could bring about the loss of detailed spatial information, also known as the scale effect. In this study, we propose Bayesian multi-scale models that are capable of accounting for the scale effect due to the high-level spatial aggregation of traffic and crash data. The performances of proposed models were assessed, as compared to the conventional (independent) model, using the crash data of two geographical scales, i.e. block groups (lower level) and census tracts (higher level) in Hillsborough County of Florida. The results indicate that the proposed multi-scale models could address the scale effects and enhance the model performance at the highly aggregated spatial units such as census tracts. This study sheds light on exploring the nature of scale effect in the macroscopic crash analysis.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107971"},"PeriodicalIF":5.7,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463592","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":"Does visual-audio feedback impair response performance and increase workload? using detection response task and NASA-TLX to examine the effect of the HMI information on driver performance at unsignalized intersections","authors":"Yunjie Ju,&nbsp;Feng Chen,&nbsp;Xiaonan Li,&nbsp;Hailin Shi","doi":"10.1016/j.aap.2025.107972","DOIUrl":"10.1016/j.aap.2025.107972","url":null,"abstract":"<div><div>The in-vehicle HMI systems regulate driving behavior by providing advisory or warning information to the driver, contributing to improved safety, reduced fuel consumption, and lower emissions. Although the issue of driver performance changes caused by HMI systems has received substantial recent attention, the implications on drivers’ workload has not received enough attention. Additionally, most previous studies provided classic visual, auditory or concurrent visual-audio feedback information but failed to determine whether the additional information resulted in workload overload, and lacked the quantitative analysis of response performance in various conflict environments. Toward to this end, this paper conducted a driving simulator experiment to examine the response performance and workload differences in the unsignalized intersection-approach process of drivers with various HMI system and conflict conditions. More precisely, an effect analysis on the drivers’ workload (response time of the DRT, DRT accuracy, and the NASA-TLX) was conducted, the Weibull AFT model with gamma heterogeneity and rANOVA method were applied. The Weibull AFT model estimation revealed the mixed effects of HMI system conditions in the drivers’ response time. In conflict situations, drivers with comprehensive visual-audio information responded earlier to DRT and lower workload. In addition, the variables for personal characteristics, safe driving history, and experience and willingness to use HMI system significantly influenced the response time of driver, among which female driver group performed longer response time. The results of DRT accuracy and NASA-TLX, drivers with comprehensive visual-audio information have excellent situation awareness when approaching and passing the unsignalized intersections; drivers believed they can accomplish performance level with less effort than others; they felt less time pressure and the driving pace was pace slow and relatively leisurely; there were fewer negative emotions such as insecure, discouraged, irritated, stressed, and annoyed. Thus, the key is whether a comprehensive situation awareness can be established for the driver, rather than purely reducing or adding additional information. The findings of this paper provide a theoretical basis for the human–machine interaction interface design and development of in-vehicle decision-making assistance systems for unsignalized intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107972"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453469","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 semi-parameter copula model for vehicle damage severity in lane-changing related crashes","authors":"Ruifeng Gu ,&nbsp;Penglin Song ,&nbsp;N.N. Sze ,&nbsp;Zijin Wang ,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.aap.2025.107979","DOIUrl":"10.1016/j.aap.2025.107979","url":null,"abstract":"<div><div>Lane changing behaviour occurs frequently on the highways. However, it also poses a major impact on traffic operation and safety since complex interactions between two or more vehicles on different traffic lanes are involved. In the lane-changing related crashes, correlation in damage level among the vehicles involved is prevalent. To this end, a copula approach is proposed to model the vehicle damage level of lane-changing related crash, with which the dependency between lane-changing and lane-keeping vehicles is accounted for. Additionally, a semi-parameter estimation approach is adopted to address the problem of heterogeneous data structure. In this study, crash data from Orlando City of Florida during the period between 2016 and 2019 are used. Then, the semi-parameter copula-based ordered logit models are estimated to measure the association between road environment, vehicle attributes, driver characteristics, crash circumstances, and vehicle damage level of two-vehicle lane-changing related crashes. Results indicate that there are major discrepancies in the influences of possible factors on vehicle damage level between lane-changing and lane-keeping vehicles. Furthermore, non-linear relationships between vehicle damage level, driver age, and time of crash are also revealed.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107979"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445021","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 design and system approaches for analyzing road traffic collisions in low-income settings","authors":"Khondhaker Al Momin ,&nbsp;Omar Faruqe Hamim ,&nbsp;Md. Shamsul Hoque ,&nbsp;Rich C. McIlroy","doi":"10.1016/j.aap.2025.107965","DOIUrl":"10.1016/j.aap.2025.107965","url":null,"abstract":"<div><div>Road crashes have become a critical issue in low- and middle-income countries (LMICs), where complex social and systemic factors exacerbate road safety challenges. This study combines Systems-Theoretic Accident Model and Processes-Systems Theoretic Process Analysis (STAMP-STPA) and Design with Intent (DwI), aiming to address both systemic and design-based factors, providing a comprehensive framework for enhancing road safety interventions in LMICs. A case study of a head-on collision in Bangladesh is analyzed using STAMP-STPA and DwI independently where the former identifies control and feedback failures, and the latter generates creative design solutions for road safety improvements. The design ideas generated in DwI workshops are analyzed using natural language processing (NLP) techniques, including topic modeling and bigram analysis, to extract key themes and reveal frequent word pairs for deeper insights. The STAMP-STPA analysis revealed critical systemic vulnerabilities related to road safety policies, vehicle standards, intersection design, and enforcement practices, which aided in proposing ten countermeasures across system levels. The DwI workshop yielded additional design ideas emphasizing infrastructure improvements, driver education, and technological solutions. NLP analysis of workshop data identified four key themes and prominent bigrams, highlighting critical areas for targeted interventions. By combining STAMP-STPA’s systematic analysis with DwI’s participatory, design-focused ideation, stakeholders can identify and implement countermeasures addressing both infrastructural and human factors to enhance road safety. Although this framework, tailored to LMIC contexts, demonstrates its applicability to a real-world case in Bangladesh, future research could expand it by incorporating multiple cases from various LMICs to enhance the generalizability of the findings.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107965"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445022","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 personalized human-machine shared driving system: A case study of obstacle avoidance","authors":"Zhiyao Pan ,&nbsp;Hongyu Zheng","doi":"10.1016/j.aap.2025.107961","DOIUrl":"10.1016/j.aap.2025.107961","url":null,"abstract":"<div><div>This paper presents a personalized human–machine shared driving (HMSD) system aimed at aiding drivers in obstacle avoidance path planning and tracking based on driving styles. Initially, a driver-in-the-loop experimental bench is utilized to collect the driver characteristic data. Then, the collected data undergoes normalization and clustering to quantify driving styles. Furthermore, a personalized path planning approach is developed to enhance driving safety, vehicle stability, and traffic efficiency. Finally, the proposed system is validated using the driver-in-the-loop experimental setup and a questionnaire survey. Results show the system’s capability to provide personalized assistance, reduce driving load, and make driver-vehicle interaction more reliable and smoother, garnering high satisfaction. Notably, the study reveals significant individual differences in the perception of the system’s effectiveness and trustworthiness. Aggressive drivers, confident in their driving abilities, display the lowest level of trust in the system. Conversely, cautious drivers perceive the system as reliable assistance, leading to the highest level of trust. Moderates maintain a modest level of trust, seeking a balance. These findings provide valuable insights for developing intelligent vehicle human–machine interaction systems, suggesting that customization to individual driving styles can significantly improve system acceptance and effectiveness.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107961"},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453468","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":"Simulation of human–vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method","authors":"Wenli Li ,&nbsp;Xianglong Li ,&nbsp;Lingxi Li ,&nbsp;Yuanhang Tang ,&nbsp;Yuanzhi Hu","doi":"10.1016/j.aap.2025.107960","DOIUrl":"10.1016/j.aap.2025.107960","url":null,"abstract":"<div><div>The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Protecting this group and safeguarding their rights and interests in urban transportation has garnered attention from academia and industry. The objective of this study is to develop a reliable simulation model that represents pedestrian crossing behavior at unsignalized crosswalks. A data-driven human–vehicle interaction behavior modeling framework is proposed, describing the human–vehicle interaction process at right-turning unsignalized intersections as a standard Markov decision-making process. In this framework, pedestrians are treated as the primary agents, and human–vehicle interactions are described using game theory. The Deep Maximum Entropy Inverse Reinforcement Learning (DMIRL) approach, combined with game theory, is employed to identify a reward function that encapsulates these interactions. The Deep Q-network (DQN) algorithm is then designed to simulate pedestrian crossing behavior based on the derived reward function. Finally, a comparison with a baseline algorithm that does not account for the game dynamics validates the proposed framework’s effectiveness and feasibility.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"214 ","pages":"Article 107960"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437488","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}