A PSO-SVM model for short-term travel time prediction based on bluetooth technology

被引：0

作者：

Wang, Qun ^{[1
]}

Liu, Zhuyun ^{[2
]}

Peng, Zhongren ^{[1
,3
]}

机构：

[1] Center for ITS and UAV Applications Research, Shanghai Jiao Tong University, Shanghai

[2] School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin

[3] Department of Urban and Regional Planning, University of Florida, PO Box 115706, Gainesville, 32611-5706, FL

来源：

Journal of Harbin Institute of Technology (New Series) | 2015年 / 22卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Bluetooth detection; Particle swarm optimization (PSO); Support vector machine (SVM); Travel time prediction; Urban arterials;

D O I：

10.11916/j.issn.1005-9113.2015.03.002

中图分类号：

学科分类号：

摘要：

The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short-term travel time forecasting on urban arterials, a prediction model (PSO-SVM) combining support vector machine (SVM) and particle swarm optimization (PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO-SVM model's error indicators are lower than the single SVM model and the BP neural network (BPNN)model. Particularly, the mean-absolute percentage error (MAPE) of PSO-SVM is only 9.4534% which is less than that of the single SVM model (12.2302%) and the BPNN model (15.3147%). The results indicate that the proposed PSO-SVM model is feasible and more effective than other models for short-term travel time prediction on urban arterials. ©, 2015, Harbin Institute of Technology. All right reserved.

引用

页码：7 / 14

页数：7

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