An ANN-based Prediction Model for Public Bus Journey Time

Lim, Y. T., Keoh, S. L. , Chong, Y.-W., Ibrahim, N. F. and Sharul Kamal, A. R. (2024) An ANN-based Prediction Model for Public Bus Journey Time. In: 6th International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024), Osaka, Japan, 19-22 Feb 2024, pp. 578-583. ISBN 9798350344349 (doi: 10.1109/ICAIIC60209.2024.10463440)

Abstract

Traffic congestion is a major problem in cities worldwide, especially in developing countries. It has a significant impact on the local GDP, environment, and society. Public transport is used to ease congestion but it is not efficiently implemented in developing countries. Implementing an accurate bus arrival time prediction system is a necessity to improve the standard of public transport and consumer satisfaction. In this study, we have developed a basic ANN-based prediction model for bus journey duration to estimate the bus arrival time using a case study in Johor Malaysia. The model was trained using bus fleet GPS dataset and the results shows improved accuracy compared to baseline approaches by considering factors like bus stop, time of day, month, and travel distance. Virtual bus stop is introduced for a long stretch of road and this shows promise in addressing limitations and improving performance. The simplicity of the model allows its application on any route by breaking it down into smaller segments. The final model achieves an MAE of 0.0056 and RMSE of 0.0123.

• 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’ (UTM Grant No: R.J130000.7606.4B856), and financially supported by NICT (http://www.nict.go.jp/en/index.html) and Universiti Teknologi Malaysia (UTM) Matching Grant (Q.J130000.3006.04M36).
• Keywords: Urban mobility, prediction of bus arrival time.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Keoh, Dr Sye Loong
• Authors: Lim, Y. T., Keoh, S. L., Chong, Y.-W., Ibrahim, N. F., and Sharul Kamal, A. R.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2831-6983
• ISBN: 9798350344349
• Copyright Holders: Copyright © 2024, IEEE
• First Published: First published in 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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