Prediction of Transportation Network Based on PageRank Algorithm

Abstract

As a new research field, network science is gaining more and more attention with the networking of human society. Urban traffic is a kind of typical complex network, the analysis of its property is becoming a hot topic. The research of the urban traffic network congestion is presented in this paper. Through in-depth analysis of the urban traffic network features and a variety of circumstances which lead to traffic congestion, we build a complex network model which the nodes represent specific roads. In this model, we get the PageRank algorithm improved which in the field of search engines, and we apply it to a transportation network. Then we predict the situations of urban traffic network congestion with this model. Finally, comprehensive tests are conducted with the data from the monitoring of real roads. The experimental results show that the model proposed in this paper can simulate the change of traffic flow, which has a good forecast effect on the urban traffic congestion and also has a referential value to the planning of urban traffic.