Enhancing Urban Traffic Flow Management and Analysis through Deep learning Techniques

Vincent, Caroline (2023) Enhancing Urban Traffic Flow Management and Analysis through Deep learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

As urban areas grow and vehicle count surge, conventional traffic control systems struggle to keep up with the growing complexity of traffic flow dynamics. The use of deep learning techniques to enhance the analysis and prediction of urban traffic patterns is explored in this work. This research conducts a series of experiments using Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Convolutional Neural Networks (CNN) to forecast traffic patterns and analyse spatio-temporal data. The experiment makes use of a comprehensive dataset of UK road traffic counts from 2017 to 2021. The aim of the research to predict traffic dynamics based on vehicle influence, traffic fluctuations during peak and non-peak hours, and geospatial traffic flow analysis around Trafalgar Square. These studies have already been used in number of studies with smaller or aggregated data sets. These factors have already been used in a number of studies with smaller or aggregated data sets. The findings reveal that deep learning models perform much better in accuracy and adaptability to temporal and spatial traffic flow variations than traditional methods. The LSTM and GRU models surpass in capturing the temporal dependencies of traffic flow than the CNN model. This research not only contribute to the field of intelligent transportation systems by offering a deep learning framework for predicting traffic but also explains the potential for these models to be combined into real-world traffic management solutions, helping in the end to create smarter city designs.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Taimur UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HE Transportation and Communications > Urban Transportation
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 May 2025 08:38
Last Modified:	26 May 2025 08:38
URI:	https://norma.ncirl.ie/id/eprint/7638

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