Paper Key : IRJ************697
Author: Christy
Date Published: 03 Aug 2022
Abstract
Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. In this project, we utilize a large-scale GPS data set, a framework for evaluating and discovering data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This work proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS dataset. Experimental results demonstrate the feasibility and effectiveness.Keywords: Traffic prediction, TMS-GCN, Region-level traffic prediction, Traffic information prediction, Road traffic prediction.