Modeling vehicle delays at signalized junctions: Artificial neural networks approach

Abstract

Delay of vehicles at signalized junctions is one of the main criteria used for evaluation of control systems’ performances. The vehicle delay is uniform and non-uniform delay types. The uniform part consists of signal timings; the non-uniform part includes vehicle queuing, random arrivals and over-saturation cases of traffic flows. The uniform part of the vehicle delays is basically determined using conventional delay formulas. But for the non-uniform part, artificial neural network (ANN) approach is used and a vehicle delay estimation model [artificial neural network delay estimation of traffic flows (ANNDEsT)] is developed. ANNDEsT model compared with Webster, HCM and Akçelik delay calculation methods and field observations, shows encouraging results especially for the cases of over-saturation or non-uniform conditions.