Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addresse...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/1/425 |
| _version_ | 1797499534696251392 |
|---|---|
| author | Hyunjin Joo Yujin Lim |
| author_facet | Hyunjin Joo Yujin Lim |
| author_sort | Hyunjin Joo |
| collection | DOAJ |
| description | Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network, which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters. |
| first_indexed | 2024-03-10T03:48:46Z |
| format | Article |
| id | doaj.art-eb20c701a6a74a8c99c53382bdb4a367 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:48:46Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb20c701a6a74a8c99c53382bdb4a3672023-11-23T11:12:50ZengMDPI AGApplied Sciences2076-34172022-01-0112142510.3390/app12010425Intelligent Traffic Signal Phase Distribution System Using Deep Q-NetworkHyunjin Joo0Yujin Lim1Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, KoreaDepartment of IT Engineering, Sookmyung Women’s University, Seoul 04310, KoreaTraffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network, which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.https://www.mdpi.com/2076-3417/12/1/425intelligent traffic signal controlreinforcement learningdeep Q-networkmulti-intersectionthroughput |
| spellingShingle | Hyunjin Joo Yujin Lim Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network Applied Sciences intelligent traffic signal control reinforcement learning deep Q-network multi-intersection throughput |
| title | Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network |
| title_full | Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network |
| title_fullStr | Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network |
| title_full_unstemmed | Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network |
| title_short | Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network |
| title_sort | intelligent traffic signal phase distribution system using deep q network |
| topic | intelligent traffic signal control reinforcement learning deep Q-network multi-intersection throughput |
| url | https://www.mdpi.com/2076-3417/12/1/425 |
| work_keys_str_mv | AT hyunjinjoo intelligenttrafficsignalphasedistributionsystemusingdeepqnetwork AT yujinlim intelligenttrafficsignalphasedistributionsystemusingdeepqnetwork |