Bus speed estimation by neural networks to improve the automatic fleet management

Abstract

In the urban areas, public transport service interacts with the private mobility. Moreover, on each link of the urban public transport network, the bus speed is affected by a high variability over time. It depends on the congestion level and the presence of bus way or no. The scheduling reliability of the public transport service is crucial to increase attractiveness against private car use. A comparison between a Radial Basis Function network (RBF) and Multi layer Perceptron (MLP) was carried out to estimate the average speed, analysing the dynamic bus location data achieved by an AVMS (Automatic Vehicle Monitoring System). Collected data concern bus location, geometrical parameters and traffic conditions. Public Transport Company of Palermo provided these data.