PRACE: A taxi recommender for finding passengers with deep learning approaches

Abstract

In this paper, we propose a real-time recommender system (PRACE) for taxi drivers to find a next passenger and start a new trip efficiently, based on historical GPS trajectories of taxis. To provide high-quality passenger-seeking advice, PRACE takes passenger prediction, road condition estimation, and earnings into ranking simultaneously. Different from many previous researchers, we not only pay more attention to the driving context of taxis (i.e., driving directions, positions, etc.) but also extract meaningful representations of these attributes, using deep neural networks. To enhance the effect of learning, the result of statistics is added to the input of models. Relying on the map meshing method, we treat the prediction task as a multi-classification problem rather than a regression problem and make comparisons with several state-of-the-art methods. Finally, we evaluate our method through extensive experiments, using GPS trajectories generated by more than 10,000 taxis from the same company over a period of two months. The results verify the effectiveness, efficiency, and availability of our recommender system.