Citation

Elhenawy M, Rakha H, El-Shawarby I. Transp. Res. Rec. 2014; 2423: 24-34.

Copyright

(Copyright © 2014, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2423-04

PMID

unavailable

Abstract

The ability to model driver stop-or-run behavior at signalized intersections is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop-or-run behavior. The research presented in this paper used data collected from a controlled field experiment on the smart road at the Virginia Tech Transportation Institute to model driver stop-or-run behavior at the onset of a yellow indication. The paper offers three contributions. First, it evaluates the importance of various model predictors in the modeling of driver stop-or-run behavior in the vicinity of signalized intersections. Second, this paper introduces a new variable related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop-or-run behavior. Third, the paper applies well-known machine learning techniques, including k nearest neighbor (k nn), random forests, and adaptive boosting (AdaBoost) techniques on the data and compares their performance to standard logistic models in an attempt to identify the optimum modeling framework. The experimental work shows that adding the driver aggressiveness predictor to the model increases the model accuracy by approximately 10% for the logistic, random forest, and k nn models and by 7% for the AdaBoost model. The paper also demonstrates that all modeling frameworks produce similar prediction accuracies.