Estimation of Annual Average Daily Truck Traffic Volume. Uncertainty treatment and data collection requirements

被引：9

作者：

Rossi, Riccardo ^{[1
]}

Gastaldi, Massimiliano ^{[1
]}

Gecchele, Gregorio ^{[1
]}

Kikuchi, Shinya ^{[2
]}

机构：

[1] Univ Padua, Dept Civil Environm & Architectural Engn, I-35131 Padua, Italy

[2] Virginia Tech, Dept Civil & Environm Engn, Falls Church, VA 22043 USA

来源：

PROCEEDINGS OF EWGT 2012 - 15TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION | 2012年 / 54卷

关键词：

Truck AADT Estimate; Artificial Neural Networks; Discord; Non-Specificity; CLASSIFICATION;

D O I：

10.1016/j.sbspro.2012.09.800

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

This paper presents an approach to estimating the Annual Average Daily Traffic (AADT) of trucks along a road section from short period traffic count (SPTC), improving the interpretability of results with the measures of non-specificity and discord. The approach was applied with data obtained in the Province of Venice, Italy, considering the characteristics of SPTCs such as duration and day of the week. The proposed method was found to produce accurate results, particularly for 72-hour SPTCs taken on weekdays. The measures of uncertainty also help to interpret the quality of estimates, and indicate the need for further data collection. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Program Committee

引用

页码：845 / 856

页数：12

共 36 条

[1] Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume
Gecchele, Gregorio
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