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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xiujuan Tian, Chunyan Liang, Tianjun Feng, Chun Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/79a95b4ffe094a06a8f5a4223b021abb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:79a95b4ffe094a06a8f5a4223b021abb
record_format	dspace
spelling	oai:doaj.org-article:79a95b4ffe094a06a8f5a4223b021abb2021-11-18T00:07:49ZSignal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering2169-353610.1109/ACCESS.2021.3124213https://doaj.org/article/79a95b4ffe094a06a8f5a4223b021abb2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594817/https://doaj.org/toc/2169-3536In 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.Xiujuan TianChunyan LiangTianjun FengChun ChenIEEEarticleTraffic engineeringsignal controlcontrol period divisionPSO-K clusteringLLE dimension reductionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147613-147625 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Traffic engineering signal control control period division PSO-K clustering LLE dimension reduction Electrical engineering. Electronics. Nuclear engineering TK1-9971 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
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.
format	article
author	Xiujuan Tian Chunyan Liang Tianjun Feng Chun Chen
author_facet	Xiujuan Tian Chunyan Liang Tianjun Feng Chun Chen
author_sort	Xiujuan Tian
title	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_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_sort	signal control period division method based on locally linear embedding and particle swarm optimization combined with k-means clustering
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/79a95b4ffe094a06a8f5a4223b021abb
work_keys_str_mv	AT xiujuantian signalcontrolperioddivisionmethodbasedonlocallylinearembeddingandparticleswarmoptimizationcombinedwithkmeansclustering AT chunyanliang signalcontrolperioddivisionmethodbasedonlocallylinearembeddingandparticleswarmoptimizationcombinedwithkmeansclustering AT tianjunfeng signalcontrolperioddivisionmethodbasedonlocallylinearembeddingandparticleswarmoptimizationcombinedwithkmeansclustering AT chunchen signalcontrolperioddivisionmethodbasedonlocallylinearembeddingandparticleswarmoptimizationcombinedwithkmeansclustering
_version_	1718425231469576192

Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering

Ejemplares similares