Artificial immune K-means grid-density clustering algorithm for real-time monitoring and analysis of urban traffic

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 vehicle...

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Veröffentlicht in:Electronics letters Jg. 49; H. 20; S. 1272 - 1273
Hauptverfasser: Chen, Chuan Ming, Pi, Dechang, Fang, Zhuoran
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage The Institution of Engineering and Technology 26.09.2013
Institution of Engineering and Technology
Schlagworte:
Applied sciences
artificial immune k means grid density clustering algorithm
artificial immune network
artificial immune systems
Artificial intelligence
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Ground, air and sea transportation, marine construction
history information
Information and communications
Memory organisation. Data processing
monitoring
pattern clustering
real time monitoring
Road transportation and traffic
Software
stability
timely analysis
traffic engineering computing
traffic flow
urban traffic conditions
vehicles data
artificial immune network
traffic flow
urban traffic conditions
traffic engineering computing
monitoring
real time monitoring
artificial immune systems
timely analysis
pattern clustering
history information
artificial immune k means grid density clustering algorithm
vehicles data
stability
Cluster analysis
Data analysis
Density measurement
Stability
Urban traffic
Cluster
K means algorithm
History
Real time
Experimental result
Traffic flow
Efficiency
Traffic management
Time analysis
Monitoring
Immune system
ISSN:0013-5194, 1350-911X, 1350-911X
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Beschreibung
Zusammenfassung: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.
Bibliographie:Chuan Ming Chen: Also with the College of Mathematics and Computer Science, Anhui Normal University, Wuhu 241003, China
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2013.2514