Prediction of traffic intensity using machine learning techniques

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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Bibliographic Details
Main Author: Ang, Shi Xuan
Other Authors: Zhu Feng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Engineering
Civil
Online Access:https://hdl.handle.net/10356/176452
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1764522024-05-17T15:34:41Z Prediction of traffic intensity using machine learning techniques Ang, Shi Xuan Zhu Feng School of Civil and Environmental Engineering zhufeng@ntu.edu.sg Engineering Civil 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. Bachelor's degree 2024-05-16T13:36:04Z 2024-05-16T13:36:04Z 2024 Final Year Project (FYP) Ang, S. X. (2024). Prediction of traffic intensity using machine learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176452 https://hdl.handle.net/10356/176452 en TR-05 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Civil
spellingShingle Engineering
Civil
Ang, Shi Xuan
Prediction of traffic intensity using machine learning techniques
description 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.
author2 Zhu Feng
author_facet Zhu Feng
Ang, Shi Xuan
format Final Year Project
author Ang, Shi Xuan
author_sort Ang, Shi Xuan
title Prediction of traffic intensity using machine learning techniques
title_short Prediction of traffic intensity using machine learning techniques
title_full Prediction of traffic intensity using machine learning techniques
title_fullStr Prediction of traffic intensity using machine learning techniques
title_full_unstemmed Prediction of traffic intensity using machine learning techniques
title_sort prediction of traffic intensity using machine learning techniques
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176452
_version_ 1800916386550120448

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