Predicting Estimated Time of Arrival Using Boosting Models

Kam, S.-H., Chong, Y.-W., Ibrahim, N. F., Keoh, S. L. , Phon-Amnuaisuk, S. and Sharul Kamal, A. R. (2024) Predicting Estimated Time of Arrival Using Boosting Models. In: 6th International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2024), Osaka, Japan, 19-22 Feb 2024, pp. 467-472. ISBN 9798350344349 (doi: 10.1109/ICAIIC60209.2024.10463273)

Abstract

Estimating the time of arrival (ETA) in public transportation can be challenging due to incomplete data and the complex nature of the urban environment. This study aims to address persistent criticism of the poor punctuality problem in Malaysian buses through the modeling of bus arrival time predictions. The study uses geographical and time data to predict bus arrival times through several boosting models. The data cleaning method enhanced data quality by eliminating invariable entries, segmenting the bus route for a more granular analysis, and encoding the data for improved structure and reliability. Through the implementation of Boruta for feature selection, relevant variables crucial for prediction were identified, contributing to the model's precision. The results highlighted LightGBM's superiority over AdaBoost and XGBoost, exhibiting the highest accuracy and a balanced level of complexity. This integrated methodology not only presents a robust prediction model but also showcases a potential practical implementation.

• Item Type: Conference Proceedings
• Additional Information: This publication is the output of ASEAN IVO (https://www.nict.go.jp/en/asean_ivo/Project_List_of_ASEAN_IVO.html) project, “An IoT-based public transport data collection and analytics framework using Bluetooth proximity beacons” and financially support by NICT (http://www.nict.go.jp/en/ index.html).
• Keywords: Bus arrival prediction, Boosting, Boruta, LightGBM, XGBoost, AdaBoost.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Keoh, Dr Sye Loong
• Authors: Kam, S.-H., Chong, Y.-W., Ibrahim, N. F., Keoh, S. L., Phon-Amnuaisuk, S., 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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