Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning

Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city. Recent advances primarily rely on deep reinforcement learning (DRL) to directly learn the optimal dispatching policy. These works, however, are still not sufficiently efficient because they overlook several pieces of valuable context information. As a result, they may generate quite a few improper actions and introduce unnecessary coordination costs. To improve existing works, we present COX - a context-aware taxi dispatching approach that incorporates rich contexts into DRL modeling for more efficient taxi reallocations. Specifically, rather than simply dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network graph into zones for practical taxi dispatching. In addition, COX comprehensively analyzes zone-level taxi demands and supplies through accurate taxi demand prediction and timely updates of taxi statuses. COX improves the DRL modeling by integrating these derived contexts, e.g., state representation with complete demand/supply data and sequential action generation with full coordination among idle taxis. In particular, we implement an environment simulator to train and evaluate COX using a large real-world taxi dataset. Extensive experiments show that COX outperforms state-of-the-art approaches on various performance metrics, e.g., on average improving the total order values by 6.74%, while reducing the number of unserved taxi orders and passengers' waiting time by 4.92% and 44.84%, respectively.