DocumentCode
2901580
Title
Modeling and clustering network-level urban traffic status based on traffic flow assignment ratios
Author
Li Qu ; Jianming Hu ; Yi Zhang
Author_Institution
Dept. of Autom., Tsinghua Univ., Beijing, China
fYear
2010
fDate
19-22 Sept. 2010
Firstpage
551
Lastpage
556
Abstract
The detected traffic data for single point or link cannot satisfy the needs for network-level traffic status information with the rapid development of the traffic control and guidance systems. This paper proposed a modeling and clustering method for network-level urban traffic status based on the dynamic traffic flow assignment ratios. The traffic assignment ratio matrix model integrates traffic status, topology and relation between links, with the dynamic traffic assignment ratios estimated by Linear Programming. The network-level traffic status is clustered by Self-Organizing Map and the typical patterns are discovered. The experiment proves the efficiency and applicability of this method for network-level traffic status modeling and analyzing.
Keywords
linear programming; pattern clustering; road traffic; self-organising feature maps; traffic information systems; dynamic traffic assignment ratios; guidance systems; linear programming; network-level traffic status modeling; network-level urban traffic status clustering; self-organizing map; traffic assignment ratio matrix model; traffic control; traffic data detection; traffic flow assignment ratios; Analytical models; Estimation; Hidden Markov models; Network topology; Neurons; Optimization; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
Conference_Location
Funchal
ISSN
2153-0009
Print_ISBN
978-1-4244-7657-2
Type
conf
DOI
10.1109/ITSC.2010.5625105
Filename
5625105
Link To Document