Nuri Park , Juneyoung Park , Yang-Jun Joo , Mohamed Abdel-Aty
{"title":"基于交通冲突分析的交叉口微观热点识别","authors":"Nuri Park , Juneyoung Park , Yang-Jun Joo , Mohamed Abdel-Aty","doi":"10.1016/j.aap.2025.108167","DOIUrl":null,"url":null,"abstract":"<div><div>Traditional approaches to identifying traffic crash hotspots have mainly focused on determining dangerous intersections within road networks, overlooking variations in crash risk within intersections. The micro-level crash hotspot analysis addresses this issue by identifying specific high-risk areas with precision. This study aims to identify micro-level hotspots within three signalized intersections using traffic conflict measures derived from drone video. An algorithm calculates conflicts based on various vehicle sizes and conflict angles. The traffic conflict measures in this study include time-to-collision (TTC), the time to a potential collision assuming constant speed; modified time-to-collision (MTTC), which detects conflicts by assuming constant acceleration; and post-encroachment time (PET), the time gap between two vehicles passing the same point. To select the most appropriate conflict measures and determine optimal thresholds at each intersection, we develop crash frequency models using generalized linear modeling (GLM). These selected conflict measures and thresholds are subsequently used to detect micro-level hotspot sections through kernel density. The results demonstrate that the TTC and PET are strongly related to micro-level crash frequencies, with different patterns emerging depending on crash angle and intersection location. Specifically, TTC-based conflicts are highly correlated with rear-end crashes occurring before the stop line, while PET-based conflicts are closely associated with crashes within the intersection, particularly with left-turning movements. This study contributes to intersection safety by identifying traffic conflict measures for micro-level hotspots and offering detailed safety interventions. These interventions include pavement marking enhancements, stop-line location adjustment, extended left-turn bays, or separated bike lanes, which are based on the specific conflict patterns observed in the study.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108167"},"PeriodicalIF":5.7000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Micro-level hotspot identification at intersections using traffic conflict analysis\",\"authors\":\"Nuri Park , Juneyoung Park , Yang-Jun Joo , Mohamed Abdel-Aty\",\"doi\":\"10.1016/j.aap.2025.108167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Traditional approaches to identifying traffic crash hotspots have mainly focused on determining dangerous intersections within road networks, overlooking variations in crash risk within intersections. The micro-level crash hotspot analysis addresses this issue by identifying specific high-risk areas with precision. This study aims to identify micro-level hotspots within three signalized intersections using traffic conflict measures derived from drone video. An algorithm calculates conflicts based on various vehicle sizes and conflict angles. The traffic conflict measures in this study include time-to-collision (TTC), the time to a potential collision assuming constant speed; modified time-to-collision (MTTC), which detects conflicts by assuming constant acceleration; and post-encroachment time (PET), the time gap between two vehicles passing the same point. To select the most appropriate conflict measures and determine optimal thresholds at each intersection, we develop crash frequency models using generalized linear modeling (GLM). These selected conflict measures and thresholds are subsequently used to detect micro-level hotspot sections through kernel density. The results demonstrate that the TTC and PET are strongly related to micro-level crash frequencies, with different patterns emerging depending on crash angle and intersection location. Specifically, TTC-based conflicts are highly correlated with rear-end crashes occurring before the stop line, while PET-based conflicts are closely associated with crashes within the intersection, particularly with left-turning movements. This study contributes to intersection safety by identifying traffic conflict measures for micro-level hotspots and offering detailed safety interventions. These interventions include pavement marking enhancements, stop-line location adjustment, extended left-turn bays, or separated bike lanes, which are based on the specific conflict patterns observed in the study.</div></div>\",\"PeriodicalId\":6926,\"journal\":{\"name\":\"Accident; analysis and prevention\",\"volume\":\"220 \",\"pages\":\"Article 108167\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accident; analysis and prevention\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0001457525002532\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ERGONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accident; analysis and prevention","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001457525002532","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
Micro-level hotspot identification at intersections using traffic conflict analysis
Traditional approaches to identifying traffic crash hotspots have mainly focused on determining dangerous intersections within road networks, overlooking variations in crash risk within intersections. The micro-level crash hotspot analysis addresses this issue by identifying specific high-risk areas with precision. This study aims to identify micro-level hotspots within three signalized intersections using traffic conflict measures derived from drone video. An algorithm calculates conflicts based on various vehicle sizes and conflict angles. The traffic conflict measures in this study include time-to-collision (TTC), the time to a potential collision assuming constant speed; modified time-to-collision (MTTC), which detects conflicts by assuming constant acceleration; and post-encroachment time (PET), the time gap between two vehicles passing the same point. To select the most appropriate conflict measures and determine optimal thresholds at each intersection, we develop crash frequency models using generalized linear modeling (GLM). These selected conflict measures and thresholds are subsequently used to detect micro-level hotspot sections through kernel density. The results demonstrate that the TTC and PET are strongly related to micro-level crash frequencies, with different patterns emerging depending on crash angle and intersection location. Specifically, TTC-based conflicts are highly correlated with rear-end crashes occurring before the stop line, while PET-based conflicts are closely associated with crashes within the intersection, particularly with left-turning movements. This study contributes to intersection safety by identifying traffic conflict measures for micro-level hotspots and offering detailed safety interventions. These interventions include pavement marking enhancements, stop-line location adjustment, extended left-turn bays, or separated bike lanes, which are based on the specific conflict patterns observed in the study.
期刊介绍:
Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.