时空因素影响下在线短时交通量预测

交通运输系统工程与信息 ›› 2016, Vol. 16 ›› Issue (5): 165-171.

时空因素影响下在线短时交通量预测

李林超^a,b,c,d，何赏璐^a,b,c,d，张健*^a,b,c,d

东南大学a. 江苏省城市智能交通重点实验室；b. 江苏省现代城市交通技术协同创新中心；c. 江苏省物联网技术与应用协同创新中心；d. 物联网交通应用研究中心，南京210096

收稿日期:2016-03-04 修回日期:2016-05-20 出版日期:2016-10-25 发布日期:2016-10-25
作者简介:李林超（1991-），男，河北邢台人，博士生.
基金资助:
国家重点基础研究发展计划(973计划)/The National Basic Research Program of China (973 Program)(2012CB725405)；交通运输部科技示范工程/The Science and Technology Demonstration Project of Ministry of Transport of China (2015364X16030,2014364223150).

Online Short-term Traffic Flow Prediction Considering the Impact of Temporal-spatial Features

LI Lin-chao^a,b,c,d, HE Shang-lu^a,b,c,d, ZHANG Jian^a,b,c,d

a. Jiangsu Key Laboratory of Urban ITS, School of Transportation; b. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies; c. Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things; d. Research Center for Internet of Mobility, Southeast University, Nanjing 210096, China

Received:2016-03-04 Revised:2016-05-20 Online:2016-10-25 Published:2016-10-25

摘要/Abstract

摘要：

考虑交通流的时空因素进行短时交通流预测，能够提高预测的精度.为此，引入径向基核函数，将复杂的预测问题转化为高维空间的回归问题；然后，基于支持向量回归机并考虑时空因素影响作用建立在线的短时交通量预测模型，通过网格搜索的方法对模型参数进行优化；最后，构造时间—空间状态向量，通过不同的状态向量对时间和空间维度的影响进行了分析.利用高速公路检测器数据，对比不同模型的精度，对在线短时交通量预测模型的有效性和可行性进行了验证.结果表明：在线模型精度优于传统的支持向量回归模型，考虑时空因素影响后交通量预测模型具有更高的精度和稳定性.

关键词: 智能交通, 短时交通量预测, 支持向量回归, 时空因素, 状态向量

Abstract:

Considering the impact of temporal- spatial features of traffic flow can improve the prediction accuracy. Therefore, this paper introduces a radial kernel function to convert the complex predictive problem into a regression algorithm in high-dimensional space. Then, based on support vector regression, an online short-term traffic flow prediction model considering temporal-spatial features is built. Grid search method is used to optimize the parameters. Finally, state vector is built to analyze the influence of temporal- spatial features. Based on the dataset of detectors in highway, different models are compared and the validity and feasibility of the prediction model are verified. The results indicate that online model is superior to traditional support vector. If considering the influence of temporal- spatial features the prediction model is more accuracy and steady.

Key words: intelligent transportation, short- term traffic flow forecasting, support vector regression, temporal-spatial features, state vector

中图分类号:

U491.2

李林超，何赏璐，张健. 时空因素影响下在线短时交通量预测[J]. 交通运输系统工程与信息, 2016, 16(5): 165-171.

LI Lin-chao, HE Shang-lu, ZHANG Jian. Online Short-term Traffic Flow Prediction Considering the Impact of Temporal-spatial Features[J]. Journal of Transportation Systems Engineering and , 2016, 16(5): 165-171.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19282.shtml

http://www.tseit.org.cn/CN/Y2016/V16/I5/165

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