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

• 作 者: Qun Wang Zhuyun Liu Zhongren Peng
• 机 构: Center for ITS and UAV Applications Research,Shanghai Jiao Tong University,School of Transportation
• 出 处: Journal of Harbin Institute of Technology
• 发表日期: 2015年03期
• 关键词: urban arterials travel time prediction Bluetooth detection support vector machin

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 (PSOSVM) combining support vector machine (SVM) and particle swarm optimization (PSO) Field developed 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 results indicate that the proposed PSO-SVM model is feasible and more effective than other models for shortterm travel time prediction on urban arterials.