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http://dx.doi.org/10.12815/kits.2022.21.3.42

A Study on the Application of Machine Learning to Improve BIS (Bus Information System) Accuracy  

Jang, Jun yong (Public Transportation Division, Sejong City hall)
Park, Jun tae (Dept. of Transportation Systems Engineering, University of Transportation Korea)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.3, 2022 , pp. 42-52 More about this Journal
Abstract
Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.
Keywords
Bus Arrival Information; Machine Learning; Prediction Algorithm; Long Short-Term Memory Units; RMSE;
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Times Cited By KSCI : 1  (Citation Analysis)
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