Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process

Tong WANG; Shan GAO; Huiwen GONG; Bo SUN

doi:10.11959/j.issn.1000-436x.2021002

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Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process

Papers | 更新时间：2024-06-05

- Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process
- Journal on Communications Vol. 42, Issue 2, Pages: 37-51(2021)
- 作者机构：
  
  1. 哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
  2. 哈尔滨工程大学先进船舶通信与信息技术重点实验室，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation(61102105);The National Natural Science Foundation of China(51779050);The National Key Research and Development Program of China(2016YFB0700100);The Harbin Science Fund for Young Reserve Talents(2017RAQXJ036);Fun-damental Research Funds for the Central Universities(HEUCFG201831);Fun-damental Research Funds for the Central Universities(3072020CF0815)
- DOI：10.11959/j.issn.1000-436x.2021002
  CLC： TN911.22
- Online First：2021-02，
  
  Published：25 February 2021
- 稿件说明：
移动端阅览
Tong WANG, Shan GAO, Huiwen GONG, et al. Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process[J]. Journal on Communications, 2021, 42(2): 37-51.
DOI：

Tong WANG, Shan GAO, Huiwen GONG, et al. Research on forecast and recommendation technology of taxi passengers based on time-varying Markov decision process[J]. Journal on Communications, 2021, 42(2): 37-51. DOI： 10.11959/j.issn.1000-436x.2021002.

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%。

Abstract

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.

关键词

Keywords

references

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