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:

城市中的无信号交叉口是城市流动的关键节点，无信号交叉口在没有信号灯控制的情况下，机动车与非机动车容易出现混合流情况，导致机非冲突概率增加，并致使驾驶者出现频繁的刹车制动与避让行为，造成城市流动效率降低。研究无信号交叉口内机非冲突风险并优化城市无信号交叉口设计对提升城市流动效率极为必要。研究构建了一种基于计算机视觉技术的轨迹提取框架，结合超过2 000min的无信号交叉口视频数据，对交通参与者不同类型的交通行为和冲突过程进行了分析，利用随机参数次序概率对冲突过程中的驾驶行为及环境因素与冲突严重程度之间的耦合关系进行建模。结果表明冲突过程中的侵入次序与交通参与者的类型以及交通冲突的严重程度高度相关。其中，电动自行车的交通参与会增加交通风险。研究开发的交通参与者轨迹提取框架将为更多的交通冲突研究者提供方案，冲突风险的分析结果将为城市交通流动效率优化和相关交通管理者提供理论指导。

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.