Traffic prediction using time related association rules and vehicle routing

Author

Zhou, Huiyu ; Mabu, Shingo ; Shimada, Kaoru ; Hirasawa, Kotaro

Author_Institution

Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitatyushu, Japan

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

2203

Lastpage

2208

Abstract

This paper describes a methodology and results of traffic prediction by extracting important time related association rules using an evolutionary algorithm named Genetic Network Programming(GNP). The extracted rules provides an useful mean to investigate the future traffic density of traffic networks and hence to develop traffic navigation systems. The proposed methodology is implemented and experimentally evaluated using a large scale real-time traffic simulator SOUND/4U. The routing algorithm combined with the traffic prediction results is studied using the environment of SOUND/4U.

Keywords

data mining; genetic algorithms; real-time systems; road vehicles; traffic engineering computing; GNP; SOUND/4U; evolutionary algorithm; extracted rules; genetic network programming; large scale real-time traffic simulator; routing algorithm; time related association rules; traffic density; traffic navigation systems; traffic networks; traffic prediction; vehicle routing; Association rules; Economic indicators; Prediction algorithms; Predictive models; Routing; Vehicles; Genetic Network Programming(GNP); Time Related Association Rule Mining; Traffic Density Prediction and Routing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6084004

Filename

6084004