Prediction of traffic intensity using machine learning techniques
Congestion occurs in densely populated areas, where road capacity is insufficient to accommodate the demands of trips. Congestion is also a leading traffic issue all around the world. Therefore, the management of traffic flow intensity is crucial to combat the persistent congestion issues. With...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176452 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Congestion occurs in densely populated areas, where road capacity is insufficient to accommodate the
demands of trips. Congestion is also a leading traffic issue all around the world. Therefore, the
management of traffic flow intensity is crucial to combat the persistent congestion issues. With the
presence of big data generated on traffic flow, machine learning has arisen as a promising avenue for
addressing engineering challenges, particularly in the civil industry. This study aims to establish a
machine-learning based prediction model that can predict traffic flow in the Central Business District
(CBD) area in Singapore. Results from this research demonstrated that the machine learning
algorithms, trained by past traffic flow data, can reasonably predict future traffic flow, and provide
valuable insights for future development. |
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