LAUSR

Taxi passenger demand prediction is of great significance to perceive citywide human mobility and make a lot of urban sensing applications more convenient. There are two major challenges to develop accurate predictive models, i.e., the complexity of the spatial-temporal dependencies as well as the dynamicity caused by some unpredictable dependencies. Although existing work uses various methods such as time series analysis, machine learning, and deep learning, most of them ignore two facts: the uncertainty of taxi demands and the impact of the parallel car-hailing markets (e.g., Uber demands) on taxi demands. In this paper, in order to deal with these two facts systematically, we design a unified framework that can use multi-source data to improve prediction accuracy. Specifically, we analyze the correlations between taxi and Uber demands and design two deep models, each of which containing a specific feature fusion method. The first model adaptively aggregates features of each grid according to the correlations. To realize the feature fusion among adjacent grids, the other method contains an additional local convolution. Besides, we also study the impact of Uber demand trends on taxi demands and aggregate the impact into the second model to improve prediction accuracy. We evaluate our models based on both taxi and Uber datasets collected from New York City, USA. Results show that our models achieve superior performance compared to the state of the art.