同济大学学报（自然科学版）2025，Vol.53Issue(6)：906-913,8.DOI:10.11908/j.issn.0253-374x.23363
基于计算机视觉的无信号交叉口机非冲突风险分析
Conflict Risk Analysis on Motor and Non-motor Vehicles at Non-signalized Intersection Based on Computer Vision
摘要
Abstract
Non-signalized intersections serve as critical nodes in urban mobility systems.In the absence of traffic signal control,these intersections often experience mixed flows of motorized and non-motorized vehicles,increasing the likelihood of vehicle-bicycle conflicts.Such conflicts frequently lead to abrupt braking and evasive maneuvers by drivers,ultimately reducing the efficiency of urban mobility.Therefore,it is essential to analyze the conflict risks at non-signalized intersections to inform intersection design improvements and enhance overall urban mobility performance.This study proposes a trajectory extraction framework based on computer vision techniques to analyze traffic interactions at non-signalized intersections.Utilizing over 2,000 minutes of video footage,we examine various traffic behaviors and conflict processes among different types of road users.A random-parameter ordered probit model is employed to capture the coupling relationships between driver behavior,environmental factors,and the severity of traffic conflicts.The results reveal that the sequence of intrusion during conflict events—combined with the type of road user involved—is highly correlated with the severity of the conflict.Notably,the involvement of electric bicycles significantly increases the risk level.The proposed trajectory extraction framework offers a valuable tool for researchers studying traffic conflict dynamics,while the findings provide theoretical guidance for enhancing urban traffic efficiency and informing practical traffic management strategies.关键词
城市交通/无信号交叉口/计算机视觉/交通冲突/安全风险Key words
urban traffic/non-signalized intersections/computer vision/traffic conflicts/safety risks分类
交通工程引用本文复制引用
柴浩,程平,张志鹏,王奕曾,胡昊..基于计算机视觉的无信号交叉口机非冲突风险分析[J].同济大学学报（自然科学版）,2025,53(6):906-913,8.基金项目
上海市科委科技创新行动计划(21YF1420000) （21YF1420000）
国家自然科学基金重点项目(52038008) （52038008）
