Traffic condition recognition using the k-means clustering method

被引：66

作者：

Montazeri-Gh, M. ^{[1
]}

Fotouhi, A. ^{[1
]}

机构：

[1] Iran Univ Sci & Technol, Sch Mech Engn, Syst Simulat & Control Lab, Dept Mech Engn, Tehran, Iran

来源：

SCIENTIA IRANICA | 2011年 / 18卷 / 04期

关键词：

Traffic condition recognition; Hybrid electric vehicle; Driving feature; k-means clustering; Driving data collection; GPS; DRIVING PATTERN-RECOGNITION; PARALLEL HYBRID VEHICLE; ENERGY MANAGEMENT AGENT; PERFORMANCE; DESIGN; MODEL;

D O I：

10.1016/j.scient.2011.07.004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents a methodological approach to traffic condition recognition, based on driving segment clustering. Traffic condition recognition has many applications to various areas, such as intelligent transportation, adaptive cruise control, pollutant emissions dispersion, safety, and intelligent control strategies in hybrid electric vehicles. This study focuses on the application of driving condition recognition to the intelligent control of hybrid electric vehicles. For this purpose, driving features are identified and used for driving segment clustering, using the k-means clustering algorithm. Many combinations of driving features and different numbers of clusters are evaluated, in order to achieve the best traffic condition recognition results. The results demonstrate that traffic conditions can be correctly recognized in 87 percent of situations using the proposed approach. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B. V. All rights reserved.

引用

页码：930 / 937

页数：8

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