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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| 其他作者: | |
| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/149618 |
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| 總結: | 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. |
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