Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data

Abstract

Traffic prediction in smart cities is an essential way for intelligent transportation system. The objective of this article is designing and implementing a traffic prediction scheme which can forecast the traffic flow with high efficiency and accuracy in Hong Kong. One problem in traffic prediction is how to balance the importance of historical traffic data and real-time traffic data. To make use of the real-time data as well as the history records, our ideas are combining data-driven approaches with model-driven approaches. First, the limitations of two baseline approaches auto-regressive integrated moving average and periodical moving average model are discussed. Second, artificial neural network is applied in the hybrid prediction model to balance between the two models. The training of neural network enables the artificial neural network to weight between real-time traffic data and traffic patterns revealed by historical traffic data. Furthermore, an emergency strategy using the Bayesian network is added to the prediction scheme to handle with the traffic accident or other emergent situation. The emergency prediction strategy on unexpected traffic situation considers the traffic condition of nearby links to predict the speed change on the link. Finally, experimental results of short-term and long-term predictions demonstrate the efficiency and accuracy of the proposed scheme.