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. (2023) 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, (Accepted for Publication)

[img] Text
316812.pdf - Accepted Version
Restricted to Repository staff only

1MB

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:Accepted for Publication
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
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record