Multi-view spatiotemporal learning for traffic forecasting

Abstract

The spatiotemporal learning has been one of the most burning research topics in traffic data analytics for traffic forecasting, which is instrumental in developing the intelligent transportation systems. Capturing accurately and efficiently complex spatiotemporal dependencies of the road network is a critical prerequisite of the traffic spatiotemporal forecasting. This study proposes a multi-view spatiotemporal learning (MVSTL) framework, which combines the fast parallel learning (FPL) and the serial learning (SL) to juggle the receptive field with the fitting capability of the model. The FPL is proposed to capture the spatiotemporal correlations synchronously and efficiently by reducing the number of parameters significantly. As a supplement to the FPL, the SL, which extracts the spatial and temporal features serially, can expand the spatial/temporal receptive fields and avoid information overwriting. The evaluation of the MVSTL is carried out by employing four sets of traffic data from the LA freeway system, and the experiment results reveal that the MVSTL is able to perform better than the advanced methods in prediction accuracy.