Traffic signal control with deep reinforcement learning
Traffic congestion, one of the problems that impact a large population of mankind, has incurred loss in terms of time, fuel, money and pollution. The solution lies in traffic signal control (TSC), optimized control is the way to mitigate traffic congestion. With numerous efforts and research i...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7105/1/fyp_CS_2025_LJYZ.pdf http://eprints.utar.edu.my/7105/ |
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| Summary: | Traffic congestion, one of the problems that impact a large population of mankind, has
incurred loss in terms of time, fuel, money and pollution. The solution lies in traffic
signal control (TSC), optimized control is the way to mitigate traffic congestion. With
numerous efforts and research in this area, the methods have evolved from
transportation theory to deep reinforcement learning (DRL) approaches over the years.
This report presents extensive research on various direction in this domain and
identifies the gap of previous research and real-world deployment. The project restudies
the nature of the problem, and therefore, propose a new formulation of Markov decision
process (MDP) and framework in TSC to improve efficiency and generalizability of the
algorithm in various scenario. Furthermore, this project explores the improvement of
Soft Actor Critic (SAC) with gradient-based meta learning (GBML) method.
Comprehensive experiments are conducted on Simulation of Urban Mobility (SUMO)
to evaluate the effectiveness of the algorithm. |
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