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

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.