A HYBRID MODEL BASED ON SUPPORT VECTOR MACHINE FOR BUS TRAVEL-TIME PREDICTION

作者：Zhong, Shiquan; Hu, Juanjuan; Ke, Shuiping^*; Wang, Xuelian; Zhao, Jingxian; Yao, Baozhen

来源：Promet-Traffic & Transportation, 2015, 27(4): 291-300.

DOI：10.7307/ptt.v27i4.1577

• bus travel time prediction
• support vector machine regression
• Grubbs' test method
• adaptive algorithm

Effective bus travel time prediction is essential in transit operation system. An improved support vector machine (SVM) is applied in this paper to predict bus travel time and then the efficiency of the improved SVM is checked. The improved SVM is the combination of traditional SVM, Grubbs' test method and an adaptive algorithm for bus travel-time prediction. Since error data exists in the collected data, Grubbs' test method is used for removing outliers from input data before applying the traditional SVM model. Besides, to decrease the influence of the historical data in different stages on the forecast result of the traditional SVM, an adaptive algorithm is adopted to dynamically decrease the forecast error. Finally, the proposed approach is tested with the data of No. 232 bus route in Shenyang. The results show that the improved SVM has good prediction accuracy and practicality.

• 出版日期2015
• 单位天津大学; 河北工业大学; 天津科技大学; 北京工业大学