Learning the Classification of Traffic Accident Types

Author

Beshah, Tibebe ; Ejigu, Dejene ; Kromer, Pavel ; Snasel, Vaclav ; Platos, Jan ; Abraham, Ajith

Author_Institution

IT Doctoral Program, Addis Ababa Univ., Addis Ababa, Ethiopia

fYear

2012

fDate

19-21 Sept. 2012

Firstpage

463

Lastpage

468

Abstract

This paper presents an application of evolutionary fuzzy classifier design to a road accident data analysis. A fuzzy classifier evolved by the genetic programming was used to learn the labeling of data in a real world road accident data set. The symbolic classifier was inspected in order to select important features and the relations among them. Selected features provide a feedback for traffic management authorities that can exploit the knowledge to improve road safety and mitigate the severity of traffic accidents.

Keywords

data analysis; fuzzy set theory; genetic algorithms; learning (artificial intelligence); pattern classification; road accidents; road traffic; traffic engineering computing; evolutionary fuzzy classifier design; feature selection; genetic programming; machine learning; real world road accident data set; road accident data analysis; road safety improvement; symbolic classifier; traffic accident severity mitigation; traffic accident type classification; traffic management authorities; Accidents; Biological cells; Genetic programming; Indexes; Injuries; Labeling; Vehicles; fuzzy rules; genetic programming; machine learning; traffic accidents;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on

Conference_Location

Bucharest

Print_ISBN

978-1-4673-2279-9

Type

conf

DOI

10.1109/iNCoS.2012.75

Filename

6337959