Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

Version 1 : Received: 14 February 2020 / Approved: 15 February 2020 / Online: 15 February 2020 (14:33:23 CET)

How to cite: Md Noor, R.; Seong Yik, N.; Kolandaisamy, R.; Ahmedy, I.; Hossain, M.A.; Alvin Yau, K.; Md Shah, W.; Nandy, T. Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus. Preprints 2020, 2020020197 (doi: 10.20944/preprints202002.0197.v1). Md Noor, R.; Seong Yik, N.; Kolandaisamy, R.; Ahmedy, I.; Hossain, M.A.; Alvin Yau, K.; Md Shah, W.; Nandy, T. Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus. Preprints 2020, 2020020197 (doi: 10.20944/preprints202002.0197.v1).

Abstract

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

Subject Areas

Support Vector Machine; Support Vector Regression; Machine learning; Prediction; Urban Smart Bus

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