Traffic Forecasting Using Graph Convolutional Network

Intelligent Transportation System (ITS) is being developed in many countries around the world and traffic forecasting lies in the heart of ITS. Traffic intensity is determined by the average speed of vehicles passing through observed road junctions in a traffic network at each time interval and the goal of traffic forecasting is to predict the traffic intensity in near future by observing the traffic data from the past and current time along with the physical traffic network. Research in deep learning models to forecast traffic intensities has gained great attention in recent years due to their capability to capture the complex spatio-temporal relationships within the traffic data. However, the task of traffic forecasting is challenging because there lies a complex spatio-temporal relationship in traffic data as the traffic within a busy city changes heavily in different locations throughout different periods in a day. As the traffic junctions and roads between them can be considered as a graph, we employ graph neural networks to model the traffic forecasting problem which have been published in IJCNN 2021 and PAKDD 2021. Also, to capture the repetitive traffic pattern we consider the traffic data of the past few days instead of considering the traffic data of the past day only. With rigorous experiments we have produced better results for accurate traffic forecasting.