An investigation of machine learning methods for prediction bus travel time of Mongolian public transportation

Nyamjav Jargalsaikhan; Katsutsugu Matsuyama

doi:10.1117/12.2566800

1 June 2020 An investigation of machine learning methods for prediction bus travel time of Mongolian public transportation

Nyamjav Jargalsaikhan, Katsutsugu Matsuyama

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Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115151S (2020) https://doi.org/10.1117/12.2566800
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

Many public bus services have their timetable which provide time information of arrival and/or departure at waypoints along the route. On the other hand, there are some bus services that do not have fixed time schedule, such as in Ulaanbaatar, the city of Mongolia. In this case, a lot of confusion occurs for the passengers. Prediction of bus travel time can help to provide services such as efficient scheduling for the passengers of their trips by avoiding to wait a long time. For this purpose, we investigate some machine learning methods to predict bus travel time. Concretely, for bus travel data, we employ three regression methods: linear regression (LR), support vector regression (SVR) and artificial neural network (ANN) to predict travel time. The performances of these machine learning methods are estimated and compared using conventional measures such as mean absolute error and root mean squared error. In a quantitative study, the artificial neural network is the best model having errors less than 1 minute in most cases. We also performed a qualitative study to investigate the details of our prediction results by using heatmap visualizations. Our visualization results offer easily grasping the tendency of travel time and error values.

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Nyamjav Jargalsaikhan and Katsutsugu Matsuyama "An investigation of machine learning methods for prediction bus travel time of Mongolian public transportation", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115151S (1 June 2020); https://doi.org/10.1117/12.2566800

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KEYWORDS

Machine learning

Visualization

Data modeling

Artificial neural networks

Lawrencium

Performance modeling

Global Positioning System

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