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.
| Published in | International Journal of Systems Engineering (Volume 2, Issue 1) |
| DOI | 10.11648/j.ijse.20180201.15 |
| Page(s) | 21-25 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Public Transport, Bus Travel Time Prediction, Support Vector Machine, Machine Learning
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APA Style
Zhang Junyou, Wang Fanyu, Wang Shufeng. (2018). Application of Support Vector Machine in Bus Travel Time Prediction. International Journal of Systems Engineering, 2(1), 21-25. https://doi.org/10.11648/j.ijse.20180201.15
ACS Style
Zhang Junyou; Wang Fanyu; Wang Shufeng. Application of Support Vector Machine in Bus Travel Time Prediction. Int. J. Syst. Eng. 2018, 2(1), 21-25. doi: 10.11648/j.ijse.20180201.15
AMA Style
Zhang Junyou, Wang Fanyu, Wang Shufeng. Application of Support Vector Machine in Bus Travel Time Prediction. Int J Syst Eng. 2018;2(1):21-25. doi: 10.11648/j.ijse.20180201.15
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Download@article{10.11648/j.ijse.20180201.15,
author = {Zhang Junyou and Wang Fanyu and Wang Shufeng},
title = {Application of Support Vector Machine in Bus Travel Time Prediction},
journal = {International Journal of Systems Engineering},
volume = {2},
number = {1},
pages = {21-25},
doi = {10.11648/j.ijse.20180201.15},
url = {https://doi.org/10.11648/j.ijse.20180201.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijse.20180201.15},
abstract = {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.},
year = {2018}
}
TY - JOUR T1 - Application of Support Vector Machine in Bus Travel Time Prediction AU - Zhang Junyou AU - Wang Fanyu AU - Wang Shufeng Y1 - 2018/08/01 PY - 2018 N1 - https://doi.org/10.11648/j.ijse.20180201.15 DO - 10.11648/j.ijse.20180201.15 T2 - International Journal of Systems Engineering JF - International Journal of Systems Engineering JO - International Journal of Systems Engineering SP - 21 EP - 25 PB - Science Publishing Group SN - 2640-4230 UR - https://doi.org/10.11648/j.ijse.20180201.15 AB - 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. VL - 2 IS - 1 ER -
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