Utilizing the eigenvectors of freeway loop data spatiotemporal schematic for real time crash prediction.
The concept of crash precursor identification is gaining more practicality due to the recent advancements in Advanced Transportation Management and Information Systems. Investigating the shortcomings of the existing models, this paper proposes a new method to model the real time crash likelihood based on loop data through schematic eigenvectors. Firstly, traffic volume, occupancy and density spatiotemporal schematics in certain duration before an accident occurrence were constructed to describe the traffic flow status. Secondly, eigenvectors and eigenvalues of the spatiotemporal schematics were extracted to represent traffic volume, occupancy and density situation before the crash occurrence. Thirdly, by setting the vectors in crash time as case and those at crash free time as control, a logistic model is constructed to identify the crash precursors. Results show that both the eigenvectors and eigenvalues can significantly impact the accident likelihood compared to the previous study, the proposed model has the advantage of avoiding multicollinearity, better reflection of the overall traffic flow status before the crash, and improving missing data problem of loop detectors.
School of Transportation Engineering, Tongji University, 4800, Cao'an Highway, Shanghai, China. Electronic address: fangsek@tongji.edu.cn.
Shou'en
S
Fang
School of Transportation Engineering, Tongji University, 4800, Cao'an Highway, Shanghai, China. Electronic address: 100931@tongji.edu.cn.
Wenjing
W
Xie
School of Transportation Engineering, Tongji University, 4800, Cao'an Highway, Shanghai, China. Electronic address: benwjh@163.com.
Junhua
J
Wang
School of Public Health, UC Berkeley, Safe Transportation Research and Education Center (SafeTREC), University of California, Berkeley, United States. Electronic address: davidr@berkeley.edu.
David R
DR
Ragland
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D016013:Likelihood Functions; D016015:Logistic Models; D008962:Models, Theoretical; D012449:Safety
Crash precursor; Real time accident model; Road safety; Spatiotemporal schematics