Short-term urban road congestion prediction considering temporal-spatial correlation

Abstract

In order to address the gaps in the study of short-term urban road congestion prediction based on Baidu map real-time road condition data, a short-term prediction model for urban road congestion based on Pearson Correlation Coefficient (PCC) and Weighted Markov Chains (WMC) is constructed by combining historical temporal correlation of urban road congestion data with spatial correlation between road sections. The model use the PCC method to filter out the spatially significantly related road sections from the upstream and downstream sections of the target road section and add them to the target road section data set as the data input of the WMC prediction model to achieve the short-term prediction of urban road congestion. The performances of the proposed models are validated by using manually collecting real-time road condition data from Baidu map. The research results show that the model integrate the spatial and temporal correlations in the urban road congestion data. Compared with other three prediction models, the prediction accuracy of the proposed model is improved by 3.096% on average, and the prediction error is reduced by 0.135 on average.