Attention-based bidirectional long short-term memory for urban traffic prediction using multivariate data

Abstract

With the continuous expansion of urban and population size, the external cost of traffic congestion is increasing daily. Urban traffic prediction using multivariate data is significant in solving these problems. However, traditional prediction methods are mostly based on merely statistical theory, which only includes a single variable, resulting in the precision accuracy not being guaranteed. Therefore, based on machine learning theory, this paper collects multivariate traffic data from the main sections of Shenzhen, China. With the help of the advantages of deep learning model in data analysis, urban traffic prediction using multivariate data is proposed based on considering the characteristics of multi-parameters, which solves the problem that the traffic prediction method is mainly based on a single variable and the auxiliary information is not considered enough. To fully consider the spatial-temporal characteristics of the traffic dataset, an ABi-LSTM prediction method concerning the influence of upstream and downstream road sections is proposed, solving the problem of low prediction accuracy caused by imperfect consideration of spatial-temporal characteristics in previous traffic predictions. The results provide an important reference for traffic management departments to alleviate traffic congestion effectively, moreover, it's convenient for residents to understand road information more clearly and make reasonable travel choices.