交通冲突
行人
运输工程
架构人行横道
人行横道
计算机科学
交叉口(航空)
聚类分析
模拟
工程类
人工智能
交通拥挤
浮动车数据
作者
Feifei Xin,Xiaobo Wang,Chongjing Sun
标识
DOI:10.1177/03611981211031885
摘要
In recent years, conflicts between crossing pedestrians and right-turning vehicles have become more severe at intersections in China, where right-turning vehicles are usually not controlled by traffic signals. This study proposes a quantitative method for evaluating the conflict risk between pedestrians and right-turning vehicles at intersections based on micro-level behavioral data obtained from video detection. A typical intersection in Shanghai was selected as the study site. In total, 670 min of video were recorded during the peak hours from 7:30 a.m. to 9:30 p.m on one day. After processing the video information, vehicle and pedestrian tracking data were obtained, including the velocity, acceleration, deceleration, time, and location coordinates. Based on these data, several conflict indicators were proposed and these indicators were extracted automatically using MATLAB to identify pedestrian–right-turning vehicle conflicts and to determine the severity of the conflicts identified. This process identified 93 examples of such conflicts. The conflict risks were quantitatively classified using the K-means fuzzy clustering method and all of the conflicts were assigned to five grades. The characteristics of the conflict distribution and the severity of different types of conflict were also analyzed, which showed that conflicts on different areas on the crosswalk differed in their severity. Based on the conclusions, practical traffic management and control measures are proposed to reduce the risk on pedestrian crossings.
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