Paper Info
Title
A fuzzy fusion algorithm to identify the traffic states by the floating car data.
Abstract
Floating car data provided the speed of each section every five minutes. The clustering methods could reflect the fuzzy character of traffic states, but the parameter threshold would be man-made subjectivity somehow. The neural network could solve this problem, but the floating car data could not be used directly because of the training time as the data was too massive. Based on the reality road network of Beijing, this paper divided urban traffic states into four levels which were blockage, congestion, slight congestion and free flow. To overcome the problems above, this research used a fusion algorithm which connected k-means clustering with MLP neural network to identify the traffic states. This method had a high reliability and the result was consistent with the actual traffic condition. This paper finds a fusion algorithm to get the threshold value of different level urban roads by the floating car data which just needs a short training time. This paper also gets the conclusion that the dipartite degree is not inadequate between the urban expressway and the urban main road if the traffic state is classified into three types. © 2012 IEEE.
Year
DOI
Venue
2012
10.1109/FSKD.2012.6234279
FSKD
Keywords
Field
DocType
floating car data,fusion algorithm,identification of traffic states,k-means clustering,neural network,threshold value,clustering algorithms,k means clustering,classification algorithms,accuracy,fuzzy set theory,neural networks,sensor fusion
k-means clustering,Computer science,Floating car data,Fuzzy logic,Algorithm,Fuzzy set,Sensor fusion,Artificial neural network,Cluster analysis,Beijing
Conference
Volume
Issue
Citations 
null
null
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Liying Wei111.76
Mingjun Li211.07