Artificial Neural Network Models for Car Following: Experimental Analysis and Calibration Issues

Abstract

This article deals with the application of artificial neural networks to model car following drivers’ behavior. The study is based on experimental data collected by several global positioning system-equipped vehicles that follow each other on urban roads. A “swarm” stochastic evolutionary algorithm has been applied in the training phase to improve convergence of the usual error-back propagation algorithm. Validation tests show that artificial neural networks (ANNs) provide a good approximation of driving patterns. Therefore, ANN can be suitably implemented in microsimulation models. In this regard, a new experimental calibration method for microsimulation software might consist of training one specific ANN for each representative individual of the driver population through systematic observations in the field or in virtual environment trials.