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.
关键词
载客路线推荐/载客区域推荐/层次化推荐/极深因子分解机/深度Q网络
Key words
pick-up route recommendation/pick-up area recommendation/hierarchical recommendation/extreme Deep Factorization Machine(xDeepFM)/Deep Q Network(DQN)
维普
万方数据