Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case Study Article Swipe
Rafidah Md Noor
,
Nadia Bella Gustiani Rasyidi
,
Tarak Nandy
,
Raenu Kolandaisamy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/su13010225
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/su13010225
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.
Related Topics
Concepts
Bottleneck
Public transport
Support vector machine
Artificial neural network
Service (business)
Computer science
Field (mathematics)
Intelligent transportation system
Fleet management
Machine learning
Transport engineering
Operations research
Artificial intelligence
Engineering
Embedded system
Mathematics
Pure mathematics
Economics
Economy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su13010225
- https://www.mdpi.com/2071-1050/13/1/225/pdf
- OA Status
- gold
- Cited By
- 25
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3117040772
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3117040772Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su13010225Digital Object Identifier
- Title
-
Campus Shuttle Bus Route Optimization Using Machine Learning Predictive Analysis: A Case StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-29Full publication date if available
- Authors
-
Rafidah Md Noor, Nadia Bella Gustiani Rasyidi, Tarak Nandy, Raenu KolandaisamyList of authors in order
- Landing page
-
https://doi.org/10.3390/su13010225Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/13/1/225/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2071-1050/13/1/225/pdfDirect OA link when available
- Concepts
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Bottleneck, Public transport, Support vector machine, Artificial neural network, Service (business), Computer science, Field (mathematics), Intelligent transportation system, Fleet management, Machine learning, Transport engineering, Operations research, Artificial intelligence, Engineering, Embedded system, Mathematics, Pure mathematics, Economics, EconomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
25Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 3, 2023: 5, 2022: 5, 2021: 8Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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