Citation

Wang L, Abdel-Aty M, Ma W, Hu J, Zhong H. Transp. Res. C Emerg. Technol. 2019; 103: 30-38.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.trc.2019.04.003

PMID

unavailable

Abstract

There have been plenty of segment-based real-time safety analyses for expressways or freeways. In these analyses, it was considered that the occurrence of a crash is because of segment conditions, including traffic and geometric. However, the movement of a vehicle is continuous from upstream to downstream. Thus, the crash occurrence of a vehicle might also be because of the traffic conditions along its trajectory. Under this idea, the study on the quasi-vehicle-trajectory-based real-time safety for expressways is proposed. It is hard to directly obtain the trajectory data of the vehicle involved in a crash. Thus, the crash-related vehicle's trajectory is derived from other traffic data sources, and it is called quasi-trajectory. To find vehicles' quasi-trajectories, the exact crash time was first identified by observing significant speed drops, and then, the vehicle locations for every one minute was distinguished by using the data from HERE, which provides space-mean speed information. The traffic information along the quasi-trajectories is from the Microwave Vehicle Detection System. A Bayesian matched-case-control logistic regression model was built to explore the impact of traffic parameters along the vehicle quasi-trajectory on real-time crash risk. The results showed that the segment speed difference and segment volume difference along the vehicle quasi-trajectory in the 0-4 min interval before crash occurrence all had a significant positive impact on crash risk. Meanwhile, the truck volume difference and average segment speed in the 0-1 min interval before crashes also have a positive impact on real-time crash likelihood. This study is new in real-time safety analysis and the findings have the potential to be applied to improve the safety of Connected and Autonomous Vehicles (CAV). A CAV continuously collects the traffic information along with its trajectory and could calculate the crash risk. When the hazardous traffic condition happens, actions can be taken by CAV to prevent the crash occurrence.

Language: en

Keywords

Connected and autonomous vehicles; Expressways; Quasi-vehicle-trajectory-based; Real-time safety study; Segment-based