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Cited by 27Traffic Prediction and Management TechniquesTransportation Planning and OptimizationTraffic control and management

Annual Average Daily Traffic Estimation from Seasonal Traffic Counts

Open access
Gastaldi, Massimiliano, Rossi, Riccardo, Gecchele, Gregorio, Lucia, Luca Della·2013·Procedia - Social and Behavioral Sciences
DOI 10.1016/j.sbspro.2013.10.610
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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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  • ML methods such as SVMs and neural networks have made it easier to classify roads with similar traffic patterns.

    “ML methods, such as SVMs and neural networks, have made it easier to classify roads with similar traffic patterns [270,454,796].”

    From Tackling climate change with machine learning (2023) · cites this paper as [270]

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Research works printed but not linked: 3

  1. Goodman, L., & Kruskal, W. (1954). Measures of association for cross-validation. Journal of the American Statistical Association, 49, 732- 764.
  2. Gulati, B. (1995). Precision of AADT estimates from short period traffic counts. M.S. thesis, University of Regina. Saskatchewan, Canada.
  3. Lingras, P. (1995). Hierarchical grouping versus Kohonen neural networks. Journal of Transportation Engineering, 121(4), 364-368.

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Authors
Gastaldi, M., Rossi, R., Gecchele, G., & Lucia, L. D.
Affiliations
University of Padua (IT)
Corresponding author
Gastaldi, Massimiliano
ORCID
Venue
Procedia - Social and Behavioral Sciences
Publisher
Elsevier BV
Published
2013-10
Volume
87
Pages
279-291
ISSN
1877-0428 (ISSN-L)
DOI
10.1016/j.sbspro.2013.10.610
OpenAlex
W2047218310
Cited by
27
References
31
Citations by year
2026: 1 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 7 · 2019: 5 · 2016: 1 · 2015: 2 · 2014: 2
FWCI
4.16
Type
conference paper
Language
en
Open access
Licence
Locations
  • Procedia - Social and Behavioral Sciences (publishedVersion, cc-by-nc-nd)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-nd)
  • INFM-OAR (INFN Catania) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Keywords
annual average daily traffic, traffic volume estimation, fuzzy set theory, artificial neural networks, uncertainty measures, non-specificity, Discord, road sections

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