Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering
In order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9594817/ |
| _version_ | 1818835559961853952 |
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| author | Xiujuan Tian Chunyan Liang Tianjun Feng Chun Chen |
| author_facet | Xiujuan Tian Chunyan Liang Tianjun Feng Chun Chen |
| author_sort | Xiujuan Tian |
| collection | DOAJ |
| description | In order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic flow characteristics of signal-controlled intersections are fully considered, and a multi-dimensional flow matrix is constructed based on the phase traffic flow. In order to reduce the computational complexity of the model and improve the operating efficiency of the method, manifold learning Locally Linear Embedding (LLE) algorithm is brought in to reduce the dimension of the multidimensional phase flow matrix. Then, the dimensionality reduction matrix is used as input data, and signal control period is divided by using Particle Swarm Optimization combined with K-means clustering (PSO-K) algorithm. Finally, an actual intersection in a city is selected to verify the performance of the proposed method. For comparative analysis, control periods are divided based on the phase traffic flow data with 15min, 30min and 1h interval respectively. Results show that for different time intervals, the division of the proposed method is better than other methods, of which the invalid control periods are less. Besides, the optimal clustering number can be obtained, which proves the effectiveness of the new proposed method. |
| first_indexed | 2024-12-19T02:52:39Z |
| format | Article |
| id | doaj.art-79a95b4ffe094a06a8f5a4223b021abb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T02:52:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-79a95b4ffe094a06a8f5a4223b021abb2022-12-21T20:38:31ZengIEEEIEEE Access2169-35362021-01-01914761314762510.1109/ACCESS.2021.31242139594817Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means ClusteringXiujuan Tian0https://orcid.org/0000-0001-7697-7481Chunyan Liang1Tianjun Feng2Chun Chen3School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, ChinaSchool of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, ChinaSchool of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, ChinaSchool of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, ChinaIn order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic flow characteristics of signal-controlled intersections are fully considered, and a multi-dimensional flow matrix is constructed based on the phase traffic flow. In order to reduce the computational complexity of the model and improve the operating efficiency of the method, manifold learning Locally Linear Embedding (LLE) algorithm is brought in to reduce the dimension of the multidimensional phase flow matrix. Then, the dimensionality reduction matrix is used as input data, and signal control period is divided by using Particle Swarm Optimization combined with K-means clustering (PSO-K) algorithm. Finally, an actual intersection in a city is selected to verify the performance of the proposed method. For comparative analysis, control periods are divided based on the phase traffic flow data with 15min, 30min and 1h interval respectively. Results show that for different time intervals, the division of the proposed method is better than other methods, of which the invalid control periods are less. Besides, the optimal clustering number can be obtained, which proves the effectiveness of the new proposed method.https://ieeexplore.ieee.org/document/9594817/Traffic engineeringsignal controlcontrol period divisionPSO-K clusteringLLE dimension reduction |
| spellingShingle | Xiujuan Tian Chunyan Liang Tianjun Feng Chun Chen Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering IEEE Access Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction |
| title | Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering |
| title_full | Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering |
| title_fullStr | Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering |
| title_full_unstemmed | Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering |
| title_short | Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering |
| title_sort | signal control period division method based on locally linear embedding and particle swarm optimization combined with k means clustering |
| topic | Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction |
| url | https://ieeexplore.ieee.org/document/9594817/ |
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