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
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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
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.
Application of Support Vector Machine in Bus Travel Time Prediction, International Journal of Systems Engineering.
Vol. 2, No. 1,
2018, pp. 21-25.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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