Data Mining Methods for Traffic Monitoring Data Analysis: A case study

被引：30

作者：

Gecchele, Gregorio ^{[1
]}

Rossi, Riccardo ^{[1
]}

Gastaldi, Massimiliano ^{[1
]}

Caprini, Armando ^{[1
]}

机构：

[1] Univ Padua, Dept Struct & Transportat Engn, I-35131 Padua, Italy

来源：

STATE OF THE ART IN THE EUROPEAN QUANTITATIVE ORIENTED TRANSPORTATION AND LOGISTICS RESEARCH, 2011: 14TH EURO WORKING GROUP ON TRANSPORTATION & 26TH MINI EURO CONFERENCE & 1ST EUROPEAN SCIENTIFIC CONFERENCE ON AIR TRANSPORT | 2011年 / 20卷

关键词：

Clustering analysis; AADT Estimation; Factor Approach; Traffic Monitoring; CLASSIFICATION;

D O I：

10.1016/j.sbspro.2011.08.052

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

Presented in this paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads, aimed at the estimation of Annual Average Daily Traffic (AADT). The analysis was carried out using data available from fifty-four Automatic Traffic Recorder (ATR) sites in the Province of Venice (Italy) and separated adjustment factors for passenger and truck vehicles in the grouping process. Errors in AADT estimation from 24-h sample counts indicate that model-based clustering methods give slightly better results compared to other tested methods, identifying a significant ATRs classification. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Organizing Committee.

引用

页码：455 / 464

页数：10

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