短时交通流预测中的特征选择算法研究

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (2): 216-222.

短时交通流预测中的特征选择算法研究

万芳¹，黎光宇^{* 2, 3}，贾宁²，朱宁²

1. 安徽交通职业技术学院城市轨道交通与信息工程系，合肥 230051； 2. 天津大学管理与经济学部，天津 300072；3. 天津市肿瘤医院，天津 300060

收稿日期:2018-10-31 修回日期:2019-01-06 出版日期:2019-04-25 发布日期:2019-04-25
作者简介:万芳(1978-)，女，安徽马鞍山人，副教授.
基金资助:
安徽省教育厅大规模在线开放课程(MOOC)示范项目《PHP》/ Anhui Provincial Department of Education Massive Online Open Courses (MOOC) Pilot Project PHP(2016mooc124).

Feature Selection Algorithm in Short-time Traffic Flow Prediction

WAN Fang¹, LI Guang-yu^{2, 3,} JIA Ning², ZHU Ning²

1. Department of Urban Rail Transit and Information Engineering, Anhui Communications Vocational and Technical College, Hefei 230051, China; 2. School of Management, Tianjin University, Tianjin 300072, China; 3. Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China

Received:2018-10-31 Revised:2019-01-06 Online:2019-04-25 Published:2019-04-25

摘要/Abstract

摘要：

短时交通流预测是智能交通系统的重要基础，其精度直接影响到交通控制和诱导的效果.对于交通流预测中的非参数回归方法，其中一个重要的问题是状态向量的选取.本文提出基于 ReliefF和 Delta Test的特征选择算法来对特征向量进行选择.首先使用 ReliefF算法根据特征和类别的相关性对状态向量进行快速初步筛选，加快算法的执行效率.接下来以 Delta Test为性能指标，使用遗传算法对状态分量的权重进行进一步优选.最后通过基于实际数据的算例，对本文方法优选的状态向量与时间序列状态向量，简单时空关联向量进行了对比.结果表明，本文的方法在一般交通状态条件下和突变交通状态下都具有较好的性能.

关键词: 智能交通, 短时交通流预测, 状态向量选择, 道路交通系统, ReliefF方法

Abstract:

The short-term traffic flow prediction is an important foundation of intelligent transportation system, and its performance significantly influences the effectiveness of traffic control and guidance. For the nonparametric regression method in traffic flow prediction, one of the most important problems is state vectors selection. This paper presents a feature selection algorithm based on ReliefF and Delta Test to select feature vectors. Firstly, ReliefF algorithm is used to filter the state vectors according to the correlation between features and classes to speed up the efficiency of the algorithm. Then using Delta Test as performance index, genetic algorithm is used to optimize the weight of state component. Finally, the state vector selected by this method is compared with the state vector of time series and the simple Spatial-temporal correlation vector. Numerical results show that the proposed method outperforms the other two usually used methods under both general and abrupt traffic conditions.

Key words: intelligent transportation, short- term traffic flow prediction, state vectors selection, transportation systems, ReliefF method

中图分类号:

U491

万芳，黎光宇，贾宁，朱宁. 短时交通流预测中的特征选择算法研究[J]. 交通运输系统工程与信息, 2019, 19(2): 216-222.

WAN Fang, LI Guang-yu, JIA Ning, ZHU Ning. Feature Selection Algorithm in Short-time Traffic Flow Prediction[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 216-222.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19822.shtml

http://www.tseit.org.cn/CN/Y2019/V19/I2/216

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