Evaluation Model for Pedestrian Vehicle Conflict Degree at
Signalized Intersections

doi:10.16097/j.cnki.1009-6744.2023.06.010

Abstract

Abstract: In order to investigate the key influencing factors affecting the severity of pedestrian-vehicle conflicts at urban signalized intersections and to improve intersection safety management, typical urban road signalized intersections are selected. In this paper, UAV aerial photography is used to obtain traffic flow videos. Conflict point information parameters and location distribution characteristics are obtained through manual observation and Tracker software analysis and processing. To quantify the degree of conflict, post intrusion time, collision area speed, and potential collision distance are used as evaluation indicators for the severity of pedestrian vehicle conflicts. The K-means clustering algorithm is used to iteratively classify street crossing conflicts according to their severity, and 21 explanatory variables were determined in terms of people, vehicles, and roads. The multivariate ordered Logistic model is screened by Pearson correlation analysis. The AUC (Area Under Curve) of the model's estimated classification probability for the severity level of conflicts was 0.971, which was verified through the ROC (Receiver Operating Characteristic) curve. The results showed that the distance between pedestrians and the conflict point (0.364), the tendency of vehicles in front of the conflict point (stopping to yield: -4.22; slowing down to yield: -0.937), whether pedestrians ran red lights (0.818), the number of motor vehicle lanes (0.29), the length of waiting time for red lights (0.012), the age group of pedestrians (- 0.869), and the color of pedestrians' clothing (0.673) are significant factors affecting the severity of pedestrian vehicle conflict. The research results of this article can provide certain reference value for the formulation of traffic strategies for pedestrian crossing safety.

Key words: urban traffic, pedestrian and vehicle conflict, multivariate ordered Logistic mode, conflict severity, cluster analysis

摘要： 为探究城市信号交叉口影响人车冲突严重程度的关键因素，提升交叉口安全管理水平，本文选取典型的城市道路信号交叉口，采用无人机航拍获取交通流视频，基于人工观测和Tracker软件解析处理得到冲突点信息参数与位置分布特征。为量化冲突程度，采用后侵入时间、冲突区域车速、潜在碰撞距离作为人车冲突严重程度评价指标，利用K-means聚类算法将过街冲突按严重程度迭代分类，确定人、车、路三方面下的21个解释变量。通过Pearson相关性分析筛选，建立多元有序Logistic模型，并通过ROC(Receiver Operating Characteristic)曲线验证得到模型对冲突严重级别的估计分类概率结果AUC(Area Under Curve)为0.971。结果表明：行人与冲突点的距离(0.364)、车辆在冲突点前的趋向(停车让行为-4.22，减速让行为-0.937)、行人是否闯红灯行为(0.818)、机动车道数量(0.29)、行人等待红灯时间长短(0.012)、行人年龄段(-0.869)、行人着装颜色(0.673)是影响人车冲突严重程度的显著因素。本文研究结果能够为行人过街安全的交通策略制定提供一定参考价值。

关键词: 城市交通, 人车冲突, 多元有序Logistic模型, 冲突严重程度, 聚类分析

CLC Number:

U491

ZHANG Wen-hui, XU Hai-bin, ZHOU Ge, WEN Wen. Evaluation Model for Pedestrian Vehicle Conflict Degree at Signalized Intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 89-99.

张文会, 徐海彬, 周舸, 温文. 信控交叉口行人过街冲突严重程度评价模型[J]. 交通运输系统工程与信息, 2023, 23(6): 89-99.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2023.06.010

http://www.tseit.org.cn/EN/Y2023/V23/I6/89

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Evaluation Model for Pedestrian Vehicle Conflict Degree at Signalized Intersections

信控交叉口行人过街冲突严重程度评价模型

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