Artificial neural networks applied to the estimation of vehicle headways in freeway sections

Abstract

Vehicle headways play a role of paramount importance in many traffic engineering applications. They provide operators of transportation systems with information for selecting and designing traffic control strategies and safety measures. Their role will undoubtedly even increase particularly in the intelligent transportation systems. Modell ing vehicle headways, as a probability distribution function or time series, has been the focus of a large number of research projects, most of which are dealing with the statistical approach. This paper presents an Artificial Neural Networks (ANN) alternative to the classical techniques. Two networks were designed, one for the time series problem and the other for the general probability distribution function. Simulation of the two networks with data gathered from nine different freeways in Riyadh revealed that accurate models can be achieved. The network was trained with all the data mixed up. However, it was able to reproduce the behaviour of any single freeway.