Title :
Bus travel time prediction model with ν - support vector regression
Author :
Wang, Jing-Nan ; Chen, Xu-Mei ; Guo, Shu-Xia
Author_Institution :
MOE Key Lab. for Urban Transp. Complex Syst. Theor. & Technol., Beijing JiaoTong Univ. Beijing, Beijing, China
Abstract :
Bus travel time prediction is a vital part for both bus operation optimizing system and information service system. This paper reviews existing bus travel time prediction models and analyzes the strengths and weaknesses of each model. A bus travel time prediction model based on nu - Support Vector Regression is proposed, which uses the departure time of bus from origin stop that can reflect traffic conditions for a certain degree as the input data. The performance of the proposed model is tested it in a case study of Express bus line 104 in Beijing. The result validates the effectiveness and efficiency of the model.
Keywords :
prediction theory; regression analysis; road traffic; support vector machines; traffic information systems; Beijing Express bus line 104; bus travel time prediction model; information service system; nu-support vector regression; Artificial neural networks; Intelligent transportation systems; Laboratories; Machine learning; Predictive models; Road transportation; Support vector machines; Testing; Traffic control; USA Councils; ν - support vector regression; Machine Learning Techniques; bus travel time; prediction; support vector machines;
Conference_Titel :
Intelligent Transportation Systems, 2009. ITSC '09. 12th International IEEE Conference on
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-5519-5
Electronic_ISBN :
978-1-4244-5520-1
DOI :
10.1109/ITSC.2009.5309844
