Artificial neural network models for car following: experimental analysis and calibration issues

The paper deals with the application of Artificial Neural Networks to model the car following driver’s behavior. The study is based on experimental data collected by several GPS equipped vehicles that follow each other on urban roads. A ‘swarm’ stochastic evolutionary algorithm has been applied in training phase to improve convergence of the usual error-back propagation algorithm. Validation tests highlight that ANNs provide a quite good approximation of driving patterns and can be suitably implemented in micro-simulation models. On this regard, a new experimental calibration method for micro-simulation software might consist of training one specific ANN for each representative individual of driver population through systematic observations on the field or in virtual environment trials.