The existing research neglects the income earned by taxi driver when picking up the next passenger and recommends route with a relatively low probability of picking up for taxi.In response to the above problem,the taxi route recommendation problem based on mobile sequential recommendation was modeled,and an improved model for profitable taxi route recommendation based on mobile sequential recommendation(PMSR)considering the next passenger's income was proposed.An improved model for profitable taxi route recommendation based on mobile sequential recommendation considering demand(PMSR-D)was proposed,considering the impact of the demand for pick-up point on the likelihood of taxi successfully picking up passenger at pick-up point.The density-based spatial clustering of applications with noise(DBSCAN)algorithm,simulated annealing algorithm and greedy algorithm were used to verify the PMSR and PMSR-D models,based on the taxi GPS trajectory data in Shanghai.Results showed that the minimum expected fare at the pick-up points recommended by PMSR model was relatively high.From 7:00 to 10:00,the PMSR model had an average increase of 148.2％and 253.0％in picking up probability compared to the potential cruising distance(PTD)model and the route recommendation model based on temporal-spatial metric(RTS),respectively.From 13:00 to 16:00,the PMSR model had an average increase of 88.1％and 48.0％in picking up probability compared to the PTD and RTS model,respectively.This indicated that the PMSR model can recommend route with high expected fare and high picking up probability for taxi,which was superior to the PTD and RTS models.Compared with the PMSR model,the PMSR-D model added 125 and 20 potential passenger demands from 7:00 to 10:00 and 13:00 to 16:00,respectively,verifying the effectiveness of PMSR-D model.
万方数据
维普
