Predicting taxi demands via an attention-based convolutional recurrent neural network.

As a flexible public transportation in urban areas, taxis play an important role in providing comfortable and convenient services for passengers. Due to the existence of the imbalance between supply of drivers and demand of passengers, an accurate fine-grained taxi demand prediction in real time can help guide drivers to plan their routes and reduce the waiting time of passengers. Recently, several methods based on deep neural networks have been provided to predict taxi demands. However, these works are limited in properly incorporating multi-view features of taxi demands together, with considering the influences of context information. In this paper, we propose a convolutional recurrent network model for fine-grained taxi demand prediction. Local convolutional layers and gated recurrent units are employed in our model to extract multi-view spatial–temporal features of taxi demands. Moreover, a novel context-aware attention module is designed to incorporate the predictions of each region with considering its contextual information, which is our first attempt. We also conduct comprehensive experiments based on multiple real-world datasets in New York City and Chengdu. The experimental results show that our model outperforms state-of-the-art methods, and validate the usefulness of each module in our model. [ABSTRACT FROM AUTHOR]