Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study

Rafidah Md Noor, Nadia Bella Gustiani Rasyidi, Tarak Nandy and Raenu Kolandaisamy
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Rafidah Md Noor: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Nadia Bella Gustiani Rasyidi: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Tarak Nandy: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
Raenu Kolandaisamy: Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia

Sustainability, 2020, vol. 13, issue 1, 1-24

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.

Keywords: time prediction; machine learning; ANN; SVM; shuttle bus; route optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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