Traffic Message Channel Prediction Based on Graph Convolutional Network

Abstract

With the development of big data, large-scale traffic flow forecasting which is a part of smart transportation has become an increasingly important research direction. Accurate and real-time traffic flow prediction is the key and difficult part of the traffic. The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle constitute a complex time correlation. For the current mainstream model, there are two main limitations: 1. Most of the existing models only focus on time correlation and ignore spatial correlation. 2. Even if the spatial correlation is concerned, the topological relationship between spaces is not fully considered. This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM). Specifically, PCA is used to reduce the dimension of data, GCN is used to learn the network topology of urban roads, LSTM is used to capture the time correlation of traffic flow. By comparing the results of different models, the proposed model is better than the current mainstream models.