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Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:Promet - Traffic - Traffico

Included Journals:Scopus

Volume:27

Issue:4

Page Number:291-300

ISSN No.:03535320

Abstract: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. ? 2015, Faculty of Transport and Traffic Engineering. All rights reserved.