Citation

Fang S, Xie W, Wang J, Ragland DR. Accid. Anal. Prev. 2016; 94: 59-64.

Affiliation

School of Public Health, UC Berkeley, Safe Transportation Research and Education Center (SafeTREC), University of California, Berkeley, United States. Electronic address: davidr@berkeley.edu.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.aap.2016.05.013

PMID

27258946

Abstract

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.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Language: en