Domain-Adversarial-based Temporal Graph Convolutional Network for Traffic Flow Prediction Problem

Traffic flow prediction is an essential task in the intelligent transportation system (ITS). However, extracting spatial and temporal features from complex traffic data is a critical challenge. Some deep learning-based methods have been applied to perform the traffic flow prediction problem, including convolutional neural network and recurrent network. Recently, the graph-based network has received a lot of attention from researchers, which could model the complex road network as a graph structure. With the rapid growth of graph neural network, it has become a state-of-the-art technique to perform the traffic prediction tasks. However, these deep learning-based traffic prediction approaches require a huge amount of traffic data, while many road segments suffer from the lack of historical traffic data. Meanwhile, some traffic data are missing due to unexpected issues such as malfunction of detectors. The severe data paucity problems will greatly degrade the performance of prediction. To address this challenge, this paper proposes a domain-adversarial-based neural network for traffic flow prediction. Contrast experiments have been conducted on several open-source traffic datasets, which have well demonstrated that the domain adaptation techniques could transfer the learned features from a source road network to a target network with data paucity problem. It also illustrates the effectiveness of our proposed method and shows the importance of domain adaptation method in traffic prediction problem.