基于加权时变泊松模型的出租车载客点推荐模型

doi:10.11772/j.issn.1001-9081.2017092339

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 923-927.DOI: 10.11772/j.issn.1001-9081.2017092339

• 人工智能 • 下一篇

基于加权时变泊松模型的出租车载客点推荐模型

商建东¹, 李盼乐¹, 刘润杰¹, 李润川^2,3

1. 郑州大学智慧城市研究院, 郑州 450000;
2. 郑州大学产业技术研究院, 郑州 450000;
3. 郑州大学互联网医疗与健康服务河南省协同创新中心, 郑州 450000

收稿日期:2017-09-28 修回日期:2017-12-04 发布日期:2018-04-09 出版日期:2018-04-10
通讯作者: 李盼乐
作者简介:商建东(1968-),男,河南郑州人,教授,博士,主要研究方向:数据挖掘、高性能计算;李盼乐(1992-),男,河南洛阳人,硕士研究生,主要研究方向:轨迹挖掘;刘润杰(1972-),男,河南安阳人,副教授,博士,主要研究方向:通信网络特性、混沌分形方法;李润川(1991-),男,河南商丘人,博士研究生,主要研究方向:智慧医疗、机器学习。

Recommendation model of taxi passenger-finding locations based on weighted non-homogeneous Poisson model

SHANG Jiandong¹, LI Panle¹, LIU Runjie¹, LI Runchuan^2,3

1. Smart City Research Institue, Zhengzhou University, Zhengzhou Henan 450000, China;
2. Research Institute of Industrial Technology, Zhengzhou University, Zhengzhou Henan 450000, China;
3. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou Henan 450000, China

Received:2017-09-28 Revised:2017-12-04 Online:2018-04-09 Published:2018-04-10

摘要/Abstract

摘要： 针对出租车空载率高、司机寻客难的问题，提出泊松-卡尔曼组合预测模型（PKCPM）。首先，采用加权非齐次泊松模型，针对出租车历史数据进行建模，得到目标时刻的估计值；其次，基于当天的实时数据，将临近时刻乘客需求的平均值作为目标时刻预测值；最后，将预测值和估计值作为卡尔曼滤波模型的输入参数，实现对目标时刻出租车乘客需求的预测，同时引入误差反向传播机制，减小下一次预测误差。基于郑州市出租车轨迹数据集，对组合模型与非齐次泊松模型（NHPM）、加权非齐次泊松模型（WNHPM）、支持向量机（SVM）等三种模型进行对比，实验结果显示PKCPM的误差比WNHPM、SVM分别降低了8.85个百分点、14.9个百分点。该模型能对不同时段内、不同空间网格的乘客需求进行预测，为出租车寻找乘客提供可靠的依据。

关键词: 空载率, 卡尔曼滤波预测模型, 加权时变泊松模型, 临近时刻乘客需求

Abstract: To slove the problem of high taxi empty-loading ratio of taxi and difficulty in finding passengers, a new model called Possion-Kalman combined prediction Model (PKCPM) was proposed. Firstly, weighted Non-Homogeneous Poisson Model (NHPM) was used to get the estimated value of the target time based on taxi historical data. Secondly, the mean value of the passenger demand in the near time, was taken as the predicted value, based on the real-time data. Finally, the predicted value and the estimated value were used as the inputs of Kalman filtering model to predict the target variance, meanwhile, the error backpropagation mechanism was introduced to reduce the next prediction error. The experimental results on the taxi trajectory dataset in Zhengzhou show that compared with NHPM, Weighted NHPM (WNHPM) and Support Vector Machine (SVM), PKCPM achieves a better optimization effect, and the error of PKCPM is reduced by about 8.85 percentage points and 14.9 percentage points respectively compared with WNHPM and SVM. PKCPM can predict passenger demand within different time and spacial grid, and provides a reliable solution to taxi driver for finding passengers.

Key words: empty-loading ratio, Kalman filtering prediction model, weighted Non-Homogeneous Poisson Model (NHPM), passenger demand in the near time

中图分类号:

TP181

商建东, 李盼乐, 刘润杰, 李润川. 基于加权时变泊松模型的出租车载客点推荐模型[J]. 计算机应用, 2018, 38(4): 923-927.

SHANG Jiandong, LI Panle, LIU Runjie, LI Runchuan. Recommendation model of taxi passenger-finding locations based on weighted non-homogeneous Poisson model[J]. Journal of Computer Applications, 2018, 38(4): 923-927.

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基于加权时变泊松模型的出租车载客点推荐模型

Recommendation model of taxi passenger-finding locations based on weighted non-homogeneous Poisson model

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