面向GPS数据的出租车载客路线层次化推荐模型

Taxi Pick-Up Route Hierarchical Recommendation Model Facing GPS Data

出租车载客推荐能够有效提高司机利润,对于提升交通效率、改善城市出行体验以及推动智能交通的发展都具有重要意义。现有方法一般直接向司机进行载客区域或载客路线推荐,没有考虑将这两者进行结合,不仅面临数据稀疏性问题,而且难以兼顾推荐准确性与实时性能。为此,提出一种面向GPS数据的出租车载客路线层次化推荐模型,其中采用了抗稀疏性的极深因子分解机(xDeepFM)、深度Q网络(DQN)强化学习算法以及层次化推荐策略。首先,离线推荐高载客概率的大网格,以减少在线计算量;然后,当出租车司机提出实时载客推荐需求时,在离线推荐的大网格内进一步推荐高载客概率的小网格;最后,给司机规划一条到小网格的载客路线。在滴滴公司数据集上进行实验,结果表明,与现有的一些先进方法相比,该方法可以使空载出租车司机的巡航时间至少减少36%,巡航距离至少减少26%,并且推荐时间仅需85 ms。

Taxi pick-up recommendations can increase driver profits,improve traffic efficiency,enhance urban travel experiences,and advance intelligent transportation systems.Existing methods typically recommend either pick-up areas or pick-up routes to drivers,without combining both,resulting in data sparsity and challenges in balancing recommendation accuracy with real-time performance.This study proposes a hierarchical recommendation model for taxi pick-up routes using GPS data incorporating a sparsity-resistant extreme Deep Factorization Machine(xDeepFM),Deep Q Network(DQN)reinforcement learning algorithm,and a hierarchical recommendation strategy.The proposed method first recommends a high-probability pick-up area(large grid)offline to reduce online computational load.When a taxi driver requests a real-time pick-up recommendation,a smaller high-probability pick-up within the offline-recommended large grid is suggested.Finally,a pick-up route is planned for the driver.Experiments on the DiDi dataset demonstrate that,compared to existing state-of-the-art methods,the proposed approach can reduce idle taxi drivers'cruising time by at least 36%and cruising distance by at least 26%,and the recommendation time is only 85 ms.