A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

  • Shiquan Zhong
  • Juanjuan Hu College of Architecture and Civil Engineering, Beijing University of Technology Beijing, 100022, China Transport Management Institute, Ministry of Transport of the People's Republic of China Beijing, 101601, China
  • Shuiping Ke College of Management and Economics, Tianjin University Tianjin, 300072, China
  • Xuelian Wang School of Management, Hebei University of Technology Tianjin 300130, China
  • Jingxian Zhao School of Economics and Management, Tianjin University of Science & Technology Tianjin, 300222, China
  • Baozhen Yao School of Automotive Engineering, Dalian University of Technology Dalian 116024, China
Keywords: bus travel time prediction, support vector machine regression, Grubbs’ test method, adaptive algorithm,

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.

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Published
2015-08-31
How to Cite
1.
Zhong S, Hu J, Ke S, Wang X, Zhao J, Yao B. A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction. PROMET [Internet]. 2015Aug.31 [cited 2020Oct.21];27(4):291-00. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1577
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Articles