{"title":"In-depth investigation into the hierarchical causal chain of fatal crashes between vulnerable road users and single motor vehicle.","authors":"Lan Huang, Xianghai Meng, Zhibin Ren","doi":"10.1080/15389588.2025.2471557","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Crash pattern recognition and characterization are essential for reducing the damage vulnerable road users (VRUs) suffer in motor vehicle crashes. However, traditional methods provide an incomprehensive understanding of crash causality and the impacts of VRU-vehicle interactions. Therefore, this study aims to provide a reasonable causality for various types of crashes.</p><p><strong>Methods: </strong>To achieve this goal, a three-layer causal analysis framework was developed. The layers consist of physical states (mainly environmental and human factors), interactions (pre-crash behaviors of drivers and VRUs), and crashes. First, latent class cluster analysis and sequence analysis were used to identify the interactive behavior patterns and crash patterns of VRU-vehicle pairs, respectively. Besides, an oversampling algorithm was proposed to assist the Granger causality test in uncovering the latent causal relationships between pre-crash behavior patterns and crash patterns. Finally, Sankey diagrams were utilized to compare and analyze the crash path.</p><p><strong>Results: </strong>The results show that single and consecutive crashes have nine and eleven typical scenarios, respectively, excluding considering the potential causal chains. These potential causal chains provide nine new scenarios. It was found that personal subjective factors primarily influence pre-crash behavior of drivers, while for VRUs, the traffic environment plays a crucial role. Noteworthily, the highest crash risk was only associated with the causal chain where vehicles are unable to brake in time.</p><p><strong>Conclusions: </strong>Clarifying the causal relationships between VRU-vehicle interaction and crash is essential, which can help finding the critical causes of fatal crashes. The analysis identified VRU violations and the inability of vehicles to brake in time as critical determinants of crash severity in both single and consecutive crash scenarios. Accordingly, targeted safety interventions were proposed, including enhancements to pedestrian crossing infrastructure and improvements in vehicle braking systems to mitigate crash risk.</p>","PeriodicalId":54422,"journal":{"name":"Traffic Injury Prevention","volume":" ","pages":"1-9"},"PeriodicalIF":1.6000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Traffic Injury Prevention","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/15389588.2025.2471557","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Crash pattern recognition and characterization are essential for reducing the damage vulnerable road users (VRUs) suffer in motor vehicle crashes. However, traditional methods provide an incomprehensive understanding of crash causality and the impacts of VRU-vehicle interactions. Therefore, this study aims to provide a reasonable causality for various types of crashes.
Methods: To achieve this goal, a three-layer causal analysis framework was developed. The layers consist of physical states (mainly environmental and human factors), interactions (pre-crash behaviors of drivers and VRUs), and crashes. First, latent class cluster analysis and sequence analysis were used to identify the interactive behavior patterns and crash patterns of VRU-vehicle pairs, respectively. Besides, an oversampling algorithm was proposed to assist the Granger causality test in uncovering the latent causal relationships between pre-crash behavior patterns and crash patterns. Finally, Sankey diagrams were utilized to compare and analyze the crash path.
Results: The results show that single and consecutive crashes have nine and eleven typical scenarios, respectively, excluding considering the potential causal chains. These potential causal chains provide nine new scenarios. It was found that personal subjective factors primarily influence pre-crash behavior of drivers, while for VRUs, the traffic environment plays a crucial role. Noteworthily, the highest crash risk was only associated with the causal chain where vehicles are unable to brake in time.
Conclusions: Clarifying the causal relationships between VRU-vehicle interaction and crash is essential, which can help finding the critical causes of fatal crashes. The analysis identified VRU violations and the inability of vehicles to brake in time as critical determinants of crash severity in both single and consecutive crash scenarios. Accordingly, targeted safety interventions were proposed, including enhancements to pedestrian crossing infrastructure and improvements in vehicle braking systems to mitigate crash risk.
期刊介绍:
The purpose of Traffic Injury Prevention is to bridge the disciplines of medicine, engineering, public health and traffic safety in order to foster the science of traffic injury prevention. The archival journal focuses on research, interventions and evaluations within the areas of traffic safety, crash causation, injury prevention and treatment.
General topics within the journal''s scope are driver behavior, road infrastructure, emerging crash avoidance technologies, crash and injury epidemiology, alcohol and drugs, impact injury biomechanics, vehicle crashworthiness, occupant restraints, pedestrian safety, evaluation of interventions, economic consequences and emergency and clinical care with specific application to traffic injury prevention. The journal includes full length papers, review articles, case studies, brief technical notes and commentaries.