This paper presents an approach to estimation of the Annual Average Daily Traffic (AADT) from a one-week seasonal traffic count (STC) of a road section, with the aim of improving the interpretability of results with measures of non-specificity and discord. The proposed method uses fuzzy set theory to represent the fuzzy boundaries of road groups and measures of uncertainty. Neural networks are used to assign a road segment to one or more predefined road groups. The approach was tested with data obtained in the Province of Venice, Italy, for the period of the year in which STCs are taken. The method was found to produce accurate results.

Annual Average Daily Traffic Estimation from Seasonal Traffic Counts

GASTALDI, MASSIMILIANO;ROSSI, RICCARDO;GECCHELE, GREGORIO;DELLA LUCIA, LUCA
2013

Abstract

This paper presents an approach to estimation of the Annual Average Daily Traffic (AADT) from a one-week seasonal traffic count (STC) of a road section, with the aim of improving the interpretability of results with measures of non-specificity and discord. The proposed method uses fuzzy set theory to represent the fuzzy boundaries of road groups and measures of uncertainty. Neural networks are used to assign a road segment to one or more predefined road groups. The approach was tested with data obtained in the Province of Venice, Italy, for the period of the year in which STCs are taken. The method was found to produce accurate results.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2683877
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