Contrastive optimized graph convolution network for traffic forecasting

Abstract

Traffic forecasting is an increasingly important research topic in the field of Intelligent Transportation Systems (ITS). In this field, prediction models based on Graph Convolution Networks (GCN) have become very popular. Most GCN-based models focus on constructing various optimized or dynamic road network graphs to represent the spatio-temporal correlation hidden in traffic data. However, these methods currently only consider the construction of a single improved road network graph and ignore the relationship of these existing optimized road network graphs. Therefore, in this paper, we propose a Contrastive Optimized Graph Convolution Network (COGCN) to connect two kinds of optimized road network graphs and maintain their global-local feature consistency through contrastive learning. The proposed COGCN model is evaluated in detail using four real traffic datasets: two traffic speed datasets and two traffic flow datasets. Experimental results show that COGCN improves forecasting accuracy by at least 2% on the two speed datasets and 9% on the two flow datasets compared to the existing state-of-the-art GCN-based methods.