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Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study
| Content Provider | MDPI |
|---|---|
| Author | Noor, Rafidah Rasyidi, Nadia Nandy, Tarak Kolandaisamy, Raenu |
| Copyright Year | 2020 |
| Description | 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. |
| Starting Page | 225 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su13010225 |
| Journal | Sustainability |
| Issue Number | 1 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-29 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Transportation Science and Technology Time Prediction Machine Learning Ann Svm Shuttle Bus Route Optimization |
| Content Type | Text |
| Resource Type | Article |
