An effective joint prediction model for travel demands and traffic flows

In this paper, we study how to jointly predict travel demands and traffic flows for all regions of a city at a future time interval. From an empirical analysis of traffic data, we outline three desired properties, namely region-level correlations, temporal periodicity and inter-traffic correlations. Then, we propose a comprehensive neural network based traffic prediction model, where various effective embeddings or encodings are designed to capture the aforementioned properties. First, we design effective region embeddings to capture two forms of region-level correlations: spatially close regions have similar embeddings, and regions with similar properties (e.g., the number of POIs and the number of roads in a region) other than locations have similar embeddings. Second, we extract the "day-in-week" and "time-in-day" and utilize the temporal periodicity in designing the embeddings for time intervals. Third, we propose an effective encoding for past traffic data which captures two forms of inter-traffic correlations - the correlation between past and future traffic, and the correlation between travel demands and traffic flows within past traffic data. Extensive experiments on two real datasets verify the high effectiveness of our model.