摘要
针对出租车盲目寻客导致空载率高的问题,提出了一种出租车载客热点推荐策略,以最大程度优化匹配乘客过程,提高寻客效率。基于出租车历史轨迹数据,结合热点乘客信息的时间序列特性,提出基于循环神经网络的分段预测(SPBR)算法,以及基于分时马尔可夫决策过程(TMDP)的载客推荐模型。实验表明,SPBR算法预测结果的RMSE比SVR、CART和BPNN等算法分别降低了67.6%、71.1%和64.5%;TMDP模型出租车期望回报比历史期望提升了35.9%。
To solve the problems of unloading rate caused by blind passenger search of taxis,the hotspot recommendation strategy of taxi passengers was proposed.The proposed strategy could optimize the process of matching passengers to the greatest extent to increase the efficiency of passenger search.Based on the historical trajectory data of taxis and the time series characteristics of hotspot passenger information,a segment prediction method was proposed based on recurrent neural network(SPBR)and a passenger recommendation model was proposed based on time-varying Markov decision process(TMDP).Experimental results show that the RMSE predicted by SPBR algorithm is 67.6%,71.1%and 64.5%lower than the SVR,CART and BPNN algorithms.The expected return of taxis based on the TMDP algorithm is 35.9%higher than historical expectations.
作者
王桐
高山
龚慧雯
孙博
WANG Tong;GAO Shan;GONG Huiwen;SUN Bo(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Key Laboratory of Advanced Ship Communication and Information Technology,Harbin Engineering University,Harbin 150001,China)
出处
《通信学报》
EI
CSCD
北大核心
2021年第2期37-51,共15页
Journal on Communications
基金
国家自然科学基金资助项目(No.61102105,No.51779050)
国家重点研发计划基金资助项目(No.2016YFB0700100)
哈尔滨市青年后备人才基金资助项目(No.2017RAQXJ036)
中央高校基本科研业务费资金资助项目(No.HEUCFG201831,No.3072020CF0815)。
关键词
出租车空载率
分时马尔可夫决策过程
热点预测
分段预测方法
载客推荐模型
taxi empty loading rate
time-varying Markov decision process
hotspot prediction
segment prediction method
passenger recommendation model
