Please enter verification code
Application of Support Vector Machine in Bus Travel Time Prediction
International Journal of Systems Engineering
Volume 2, Issue 1, June 2018, Pages: 21-25
Received: Jun. 28, 2018; Accepted: Jul. 12, 2018; Published: Aug. 1, 2018
Views 1682      Downloads 191
Zhang Junyou, Traffic College, Shandong University of Science and Technology, Qingdao, China
Wang Fanyu, Traffic College, Shandong University of Science and Technology, Qingdao, China
Wang Shufeng, Traffic College, Shandong University of Science and Technology, Qingdao, China
Article Tools
Follow on us
The travel time between bus stops has obvious characteristics of time interval distribution, and the bus is a typical space-time process object, and its operation has a state transition. In order to predict the travel time between bus stations accurately, a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus stations. Through a large number of GPS data in different periods of time for a reasonable classification summary bin selected the appropriate kernel function to verify. The algorithm is verified by the actual operation data of No. 6 bus in Qingdao Economic and technological Development Zone. The results show that the results of support vector machine model operation are basically in agreement with the actual measured data, and the accuracy is relatively high, and it can even be used to predict bus travel time.
Public Transport, Bus Travel Time Prediction, Support Vector Machine, Machine Learning
To cite this article
Zhang Junyou, Wang Fanyu, Wang Shufeng, Application of Support Vector Machine in Bus Travel Time Prediction, International Journal of Systems Engineering. Vol. 2, No. 1, 2018, pp. 21-25. doi: 10.11648/j.ijse.20180201.15
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
J. Bai Cong, Peng Zhongren. Bus travel time prediction based on dynamic model. Computer Engineering and Applications, 2016, (3), pp. 103-107.
J. Fu Hua, Yu Xiang, Lu Wanjie. Ant Colony Particle Swarm Optimization Algorithm and LS-SVM Gas Emission Forecast. Journal of Sensing Technology, 2016, (3), pp. 373-377.
J. Li Chunxiang, Ding Xiaoda, Ye Jihong. Prediction of pulsating wind speed based on hybrid ant colony and particle swarm optimization LS-SVM. Vibration and shock, 2016, (21), pp. 131-136.
J. Peng Xinjian, Weng Xiaoxiong. Bus travel time prediction based on firefly algorithm optimized BP neural network. Journal of Guangxi Normal University, 2015 (07), pp. 28-36.
J. Xiao Yang, Gao Xiaoli, Li Ting. Intelligent bus data system design. Information technology and information technology, 2015 (07), pp. 51-52.
C. Ma Z L, Ferreira L, Mesbah M. Modelling Bus Traval Tine Reliability Using Supply and Demand Data from AUTOMATIC Vehicle Location and Smart Card Systems. Transportation Research Board 94th Annual Meeting, 2015 (15-0402)
D. Yin Tingting. Research on Bus Dispatching Rules Based on Big Data. Beijing: School of Transportation and Transportation, Beijing Jiaotong University, 2015.
J. Liu Siwen. Thinking about public transportation big data. Urban public transportation, 2015 (9), pp. 21-23.
M. Ran Bin, Chen Xianghui, Zhang Jian. General Theory and Practice of Wisdom Highway. Beijing: China Communications Press, 2015, 23-26.
J. Brata A H, Liang D, Pramono S H. Software Development of Automatic Data Collection for Bus Route Planning System. International Journal of Electrical and Computer Engineering (IJECE), 2015, 5 (1), pp. 150-157.
J. Kumar B A, Mothukuri S, Vanajakshi L. Analytical approach to identify the optimum inputs for a bus travel time prediction method. Transportation Research Record. Journal of the Transportation Research Board, 2015, 2535, pp. 25-34.
J. Ibarra Rojas O J, Delgado F, Giesen R. Planning, operation, and control of bus transport systems: A literature review. Transportation Research Part B Methodological, 2015, pp. 38-75.
J. Chen X, Hellinga B, Chang C. Optimization of headways with stop skipping control: a case study of bus rapid transit system. Journal of Advanced Transportation, 2015, 49 (3), pp. 385-401.
D. Wang Yunhai. Research on Modern Enterprise Logistics scheduling Model and Monitoring. Zhejiang University of Technology, 2015.
D. Jiao Yunlong. Research on Route Optimization of Logistics vehicles based on Travel time Prediction. Dalian Maritime University, 2015.
J. Liu Weiming, Lei Huanyu, Zhai Cong, Li Songsong. Prediction of Expressway Short-term Travel time based on PSO-LSSVM. Highway and automobile transportation, 2017 (03), pp. 36-39+48.
J. Wang Xiang, Chen Xiaohong, Yang Xiangmei. Prediction of Expressway Short-Time Travel time based on K-nearest neighbor algorithm. Chinese Journal of Highway, 2015, 28 (01), pp. 102-111.
J. Zhang Xiuli, Xia Bin. A study of Sleeping stages based on CNN-LSTM Network. Microcomputer and Application, 2017, 36 (17), pp. 88-91.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186