Robust and Responsive Learning of Spatiotemporal Urban Traffic Flow Relationships

Spatiotemporal models that incorporate information about the relationships between traffic flows over space and time have recently become the mainstream of urban traffic forecasting. Many algorithms have been developed to learn the structure of spatiotemporal relationships - the travel time-based, cross-correlation, graphical lasso, mutual information, and random forest methods, among several others. In this paper, we address two issues of spatiotemporal relationship learning. First, we suggest the application of ensemble techniques (ranking- and voting-based) that combine the advantages of individual algorithms and allow more robust structures to be obtained for learning spatiotemporal relationships. Second, we propose strategies that enable discovery of their dynamics and provide extensive information on traffic flows for the responsive learning of spatiotemporal relationships. In addition to the widely used forecasting performance (estimated for the spatially regularised vector autoregression model, the spatiotemporal k-nearest neighbour algorithm, and the high-order graph convolutional artificial neural network), we compare the estimated spatiotemporal relationship structures using their complexity and stability metrics and demonstrate the importance of these characteristics. A multitude of numerical experiments using a large citywide traffic data set are conducted, and the results demonstrate the preferences of ensemble learning and the strengths of the proposed learning strategies.