Citation

Elhenawy M, Jahangiri A, Rakha HA, El-Shawarby I. Accid. Anal. Prev. 2015; 83: 90-100.

Affiliation

Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States; Ain-Shams University, Cairo, Egypt. Electronic address: shawarby@vt.edu.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.aap.2015.06.016

PMID

26225822

Abstract

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only.

Language: en