Online ride-hailing services have become a prevalent transportation system
across the world. In this paper, we study a challenging problem of how to
direct vacant taxis around a city such that supplies and demands can be
balanced in online ride-hailing services. We design a new reward scheme that
considers multiple performance metrics of online ride-hailing services. We also
propose a novel deep reinforcement learning method named Deep-Q-Network with
Action Mask (AM-DQN) masking off unnecessary actions in various locations such
that agents can learn much faster and more efficiently. We conduct extensive
experiments using a city-scale dataset from Chicago. Several popular heuristic
and learning methods are also implemented as baselines for comparison. The
results of the experiments show that the AM-DQN attains the best performances
of all methods with respect to average failure rate, average waiting time for
customers, and average idle search time for vacant taxis.

本论文研究了如何通过新的 reward scheme 和 deep reinforcement learning 方法 AM-DQN 来控制城市中的空车，以实现在线打车服务的供需平衡，并使用芝加哥的数据集进行了实验，结果表明 AM-DQN 相对于其他方法具有更好的性能。