Deep reinforcement learning based application in traffic signal control
The rapid economic development has continuously improved the transportation network around the world. But at the same time, the substantial increase in vehicles has made traffic jams and traffic accidents increasingly serious. It is important to find a Traffic Signal Control (TSC) method which ca...
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Nanyang Technological University
2021
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sg-ntu-dr.10356-1496182023-07-04T17:11:16Z Deep reinforcement learning based application in traffic signal control Guo, Yi Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering The rapid economic development has continuously improved the transportation network around the world. But at the same time, the substantial increase in vehicles has made traffic jams and traffic accidents increasingly serious. It is important to find a Traffic Signal Control (TSC) method which can be used in Intelligent Transportation System (ITS). An effective method is to use Rein forcement Learning (RL) in TSC. In this dissertation, one of the useful and easy algorithm in Reinforcement Learning, Deep Q-Network (DQN), is used to control the traffic signals. A transportation network in Singapore is built on the PTV Vissim platform and the DQN Algorithm is implemented through MATLAB. MATLAB calls the COM of PTV Vissim and conducts co-simulation with PTV Vissim. Five groups of comparative experiments are conducted with the DQN Algorithm, which has well demonstrated the effectiveness of the DQN Algorithm in reducing traffic congestion and time delay. Master of Science (Computer Control and Automation) 2021-06-08T08:46:01Z 2021-06-08T08:46:01Z 2021 Thesis-Master by Coursework Guo, Y. (2021). Deep reinforcement learning based application in traffic signal control. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149618 https://hdl.handle.net/10356/149618 en ISM-DISS-02191 application/pdf Nanyang Technological University |
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Singapore Singapore |
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Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
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Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Guo, Yi Deep reinforcement learning based application in traffic signal control |
| description |
The rapid economic development has continuously improved the transportation
network around the world. But at the same time, the substantial increase in
vehicles has made traffic jams and traffic accidents increasingly serious. It is
important to find a Traffic Signal Control (TSC) method which can be used
in Intelligent Transportation System (ITS). An effective method is to use Rein forcement Learning (RL) in TSC. In this dissertation, one of the useful and
easy algorithm in Reinforcement Learning, Deep Q-Network (DQN), is used
to control the traffic signals. A transportation network in Singapore is built
on the PTV Vissim platform and the DQN Algorithm is implemented through
MATLAB. MATLAB calls the COM of PTV Vissim and conducts co-simulation
with PTV Vissim. Five groups of comparative experiments are conducted with
the DQN Algorithm, which has well demonstrated the effectiveness of the DQN
Algorithm in reducing traffic congestion and time delay. |
| author2 |
Wang Dan Wei |
| author_facet |
Wang Dan Wei Guo, Yi |
| format |
Thesis-Master by Coursework |
| author |
Guo, Yi |
| author_sort |
Guo, Yi |
| title |
Deep reinforcement learning based application in traffic signal control |
| title_short |
Deep reinforcement learning based application in traffic signal control |
| title_full |
Deep reinforcement learning based application in traffic signal control |
| title_fullStr |
Deep reinforcement learning based application in traffic signal control |
| title_full_unstemmed |
Deep reinforcement learning based application in traffic signal control |
| title_sort |
deep reinforcement learning based application in traffic signal control |
| publisher |
Nanyang Technological University |
| publishDate |
2021 |
| url |
https://hdl.handle.net/10356/149618 |
| _version_ |
1772826780424470528 |