A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology  

作　　者：Qun Wang Zhuyun Liu Zhongren Peng 

机构地区：[1]Center for ITS and UAV Applications Research,Shanghai Jiao Tong University [2]School of Transportation Science and Engineering,Harbin Institute of Technology [3]Department of Urban and Regional Planning,University of Florida

出　　处：《Journal of Harbin Institute of Technology(New Series)》2015年第3期7-14,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.71101109)

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( PSOSVM) 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. 453 4 %which is less than that of the single SVM model( 12. 230 2 %) and the BPNN model( 15. 314 7 %). The results indicate that the proposed PSO-SVM model is feasible and more effective than other models for shortterm travel time prediction on urban arterials.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. 453 4 % which is less than that of the single SVM model （ 12. 230 2 %） and the BPNN model （ 15. 314 7 %）. 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.

关 键 词：urban arterials travel time prediction Bluetooth detection support vector machine(SVM) particle swarm optimization(PSO) 

分 类 号：U491[交通运输工程—交通运输规划与管理]