Traffic flow forecast with urban transport network

Abstract

Traffic flow prediction has become a hot spot in the intelligent transportation system study. In this paper, novel methods are proposed to predict traffic flow. We divide 24 hours into 4 stages according to the bimodal distribution of traffic flow, and integrate topology features of urban traffic network into 4 typical machine learning methods. Experiments on the traffic flow of Qinhuangdao city demonstrate the effectiveness and potential of the proposed methods.