Detecting the occurrence times and locations of multiple traffic crashes simultaneously with probe vehicle data. (May 2021)

• Record Type: Journal Article
• Authors: Wang, Zhengli; Liu, Kanglin; Zhu, Liyun; Jiang, Hai
• Is Part Of: Transportation research. Volume 126(2021)
• Publication Date: 2021-05
• Subjects: Multiple crashes -- Occurrence time -- Occurrence location -- Probe vehicle data; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103014
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We detect the occurrence times and locations of multiple crashes simultaneously. We develop an integer programming model to accomplish the detection. Numerical experiments show that our approach can reduce time and location bias. Abstract: When we detect the occurrence times and locations of traffic crashes in real life, we will face the situation where multiple crashes occur along a long road during a specific time period. Although there has been a proliferation of approaches that attempt to detect the occurrence times and locations of traffic crashes, most, if not all, of them typically manipulate the crashes singly when there are multiple crashes. In this research, we propose a new approach that can detect the occurrence times and locations of multiple crashes simultaneously. We first construct the speed contour plot using probe vehicle data on candidate links. We then formulate the detection process as an integer programming model and develop a set of novel constraints to estimate the spatiotemporal impact regions associated with multiple crashes by leveraging the spatiotemporal propagation of congestion. We subsequently take the time and location when the travel speed begins to drop in each impact region as the occurrence time and location of each corresponding crash. Finally, we validate our model using real data in Beijing and find that our model can handle the situation where multiple crashes are incorporated in the speed contour plot. Besides, our model can reduce the average time and location bias by 79.52% and 81.29%, respectively.