Open AccessArtificial immune K ‐means grid‐density clustering algorithm for real‐time monitoring and analysis of urban traffic
Author(s) -
Chen Chuan Ming,
Pi Dechang,
Fang Zhuoran
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.2514
Subject(s) - cluster analysis , partition (number theory) , data mining , computer science , grid , traffic flow (computer networking) , stability (learning theory) , algorithm , artificial intelligence , machine learning , mathematics , computer network , combinatorics , geometry
A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real‐time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicles on the roads as data points, a K ‐means grid‐density clustering algorithm is proposed based on an artificial immune network to partition the vehicles data into proper clusters, and marks the densities for monitoring and analysing the traffic conditions. Simulated experimental results show that the proposed algorithm obtains higher efficiency and stability than traditional methods.