Towards Prediction of Bus Arrival Time using Multi-layer Perceptron (MLP) and MLP Regressor

Xu, X., Keoh, S. L. , Seow, C. K. , Cao, Q. and Rahim, S. K. b. A. (2023) Towards Prediction of Bus Arrival Time using Multi-layer Perceptron (MLP) and MLP Regressor. In: 8th International Conference on Business and Industrial Research (ICBIR 2023), Bangkok, Thailand, 18-19 May 2023, pp. 669-674. ISBN 9798350399646 (doi: 10.1109/ICBIR57571.2023.10147614)

Abstract

Intelligent transport systems have been in research and development in recent decades. However, not all countries can afford to deploy such systems for the public usage. Conventional public transport systems such as public buses are still the main mode of public transportation system in many developing countries. Due to the issue of public transportation's inaccurate bus arrival timing, the general public still prefers private transportation. The goal of this study is to investigate the use of machine learning to improve the prediction accuracy of bus arrival timing. Two machine learning models, a multi-layer perceptron (MLP) and a MLP regressor, were compared in terms of their performance on small datasets. The experiment data was collected from Kulai-Johor Bahru Sentral bus route in Malaysia and cleaned to negate errors that influenced the accuracy of the models. The performances of the models were analysed and discussed and we observed that the MLP outperforms the MLP regressor. A limitation of this study is the small dataset that only comprises bus location data collected on a single bus route.

• Item Type: Conference Proceedings
• Additional Information: This work is the output of the ASEAN IVO project, ‘An IoT-based Public Transport Data Collection and Analytic Framework using Bluetooth Proximity Beacons’, and financially supported by NICT (http://www.nict.go.jp/en/index.html). X. Xu’s internship was supported by the University of Glasgow.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi and Keoh, Dr Sye Loong and Seow, Dr Chee Kiat
• Authors: Xu, X., Keoh, S. L., Seow, C. K., Cao, Q., and Rahim, S. K. b. A.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798350399646
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in 2023 8th International Conference on Business and Industrial Research (ICBIR)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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