Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171885
Title: Deep learning-based traffic flow prediction and traffic management system for urban transportation networks
Authors: Zhao, Han
Keywords: Engineering::Civil engineering::Transportation
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Zhao, H. (2023). Deep learning-based traffic flow prediction and traffic management system for urban transportation networks. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171885
Abstract: Due to the growth of the population and improved living standards, a lot of urbanization policies are implemented by the governments in many countries. As a result, the total amount of vehicles on the streets is growing rapidly, and traffic congestion occurs frequently. The recurring and non-recurring congestion has resulted in high economic costs, environmental pollution and severe noise pollution. Hence, how to alleviate traffic congestion and delays have become an essential problem in this urbanized world. In this thesis, we proposed a deep learning-based traffic prediction and management system to tackle the traffic congestion problem. First, we developed a domain-adversarial-based graph network for short-term traffic flow prediction. Contrast experiments have been conducted on several open-source traffic datasets, which proved that our proposed method had made some improvements compared to other baselines. Secondly, we proposed an optimized vehicle route guidance and navigation system. The employed deep reinforcement learning method and graph convolutional network can extract several features from traffic data and topological data. The proposed route guidance system can assign the best route and re-route the vehicles in a complex urban transportation network with dynamic traffic conditions. Last but not least, we proposed a deep reinforcement learning-based traffic signal control strategy to reduce the travel delay time and queue length by adjusting phase splits. TD3 algorithm is employed to generate optimal phase splits for the next cycle of traffic signals. The performance of traffic signal control by this strategy is evaluated through several urban road networks with true traffic demand or manually generated demand via VISSIM.
URI: https://hdl.handle.net/10356/171885
DOI: 10.32657/10356/171885
Schools: School of Electrical and Electronic Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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