A deep-learning framework considering multiple motifs for traffic travel time prediction

Abstract

How to accurately predict Short-term traffic travel time is an important problem in Intelligent Transportation Systems. However, the traffic data usually exhibit high nonlinearities and complex patterns. Predicting traffic travel time is a challenge. Most previous studies use the topological adjacency of road networks to explore the spatial correlations. However, as a real network, the road network contains higher-order connectivity patterns, which have different statistical significance. The topology adjacency cannot reflect these higher-order connectivity patterns. To obtain topological adjacency and higher-order connection pattern information, a novel deep learning framework was proposed: Multiple Motifs Graph Convolutional Recurrent Neural Networks, for traffic travel time prediction in this paper. The accuracy of travel time prediction can be improved by the proposed model. To be more specific, there are two meaning blocks in each unit of the model: (1) The spatial blocks captured spatial patterns information by the Multi-Motif graph convolution network and Motif Graph embedding; (2) The temporal blocks captured temporal patterns information by the combination of LSTM and the FC layer. To prove the effectiveness and accuracy of the prediction model, experiments were conducted on real world traffic travel time datasets.