City traffic prediction based on real-time traffic information for Intelligent Transport Systems

Abstract

Intelligent Transportation Systems (ITS) have been considered important technologies to mitigate urban traffic congestion. Accurate traffic prediction is one of the critical steps in the operation of an ITS. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on highway networks. Due to the fundamental difference between the traffic flow pattern on highways and that on city roads, much of the existing models cannot be effectively applied to city traffic prediction. In this paper, we propose two city traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road link. The proposed models are implemented to predict the traffic volume in Cologne in Germany, and the real data are collected through simulations in the traffic simulator SUMO. The results show that both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model. In addition, we found that Model-1 is suitable for short prediction interval that is in the same magnitude as the link travel time, while Model-2 demonstrates superiority when the prediction interval is larger than one minute.