Estimation of traffic congestion level via FN-DBSCAN algorithm by using GPS data

Author

Diker, Ahmet Can ; Nasibov, Efendi

Author_Institution

Dokuz Eylul Univ., Izmir, Turkey

fYear

2012

Firstpage

1

Lastpage

4

Abstract

Determination of traffic congestion level is one of the fundamental problems in Intelligent Transportation Systems (ITS). In this paper, fuzzy based data mining technique, namely, Fuzzy Neighborhood Density-Based Spatial Clustering of Applications with Noise (FN-DBSCAN) was performed to cluster road segments with traffic congestion level. Data were collected from portable navigation device in probe car on selected roads in Izmir. Six clusters were obtained as a result of experimental study and these clusters were named traffic congestion levels. It is considered that this paper will provide a contribution to related work.

Keywords

Global Positioning System; automated highways; data mining; fuzzy set theory; pattern clustering; road traffic; traffic engineering computing; FN-DBSCAN algorithm; GPS data; ITS; fuzzy based data mining technique; fuzzy neighborhood density-based spatial clustering of applications with noise; intelligent transportation systems; portable navigation device; traffic congestion level determination; traffic congestion level estimation; FN-DBSCAN; clustering; data mining; intelligent transportation systems; traffic congestion;

fLanguage

English

Publisher

ieee

Conference_Titel

Problems of Cybernetics and Informatics (PCI), 2012 IV International Conference

Conference_Location

Baku

Print_ISBN

978-1-4673-4500-2

Type

conf

DOI

10.1109/ICPCI.2012.6486279

Filename

6486279