Noor, Rafidah Md and Rasyidi, Nadia Bella Gustiani and Nandy, Tarak and Kolandaisamy, Raenu (2021) Campus shuttle bus route optimization using machine learning predictive analysis: A case study. Sustainability, 13 (1). ISSN 2071-1050, DOI https://doi.org/10.3390/su13010225.
Full text not available from this repository.Abstract
Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Time prediction; Machine learning; ANN; SVM; Shuttle bus; Route optimization |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 25 Feb 2022 07:41 |
| Last Modified: | 25 Feb 2022 07:41 |
| URI: | http://eprints.um.edu.my/id/eprint/26400 |
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