EFFECTS OF AUTOMATED TRAFFIC ENFORCEMENT ON DRIVER BEHAVIOR AT A SIGNALIZED INTERSECTION IN SAUDI ARABIA

For automating road network Saudi Arabia recently introduced 'Saher Traffic Monitoring Enforcement System' (STMES) using red-light and speed-limit camera. This paper analyzes the impact of STMES on driver behavior and decision making process at signalized intersections. It proposes models for analyzing driver's probabilities of stopping and crossing decisions at signalized intersections during yellow-light interval on the basis of speed, distance from intersection and vehicle type. Models also compare STMES fitted intersections with conventional intersections without automated systems. Binary logistic regression and Artificial Neural Network (ANN) models are developed using video data at intersections in Makkah city. Analyses reveal that STMES increases stopping probability by 26 percent and distance to the stop line is the most significant factor affecting the drivers' decisions in the yellow interval. Dilemma zone shifts closer to the stop line and the number of early stops increases due to STMES. Artificial neural network model performs better than binary logistic regression model in predicting the stopping probability at signalized intersections. Research findings suggest that ANN models can be effectively used in traffic signal design and dilemma zone reduction to improve efficiency and safety at roadway intersections.