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Cited by 10Traffic Prediction and Management TechniquesTransport Systems and TechnologyTransportation Planning and Optimization

Use of Support Vector Machines to Assign Short-Term Counts to Seasonal Adjustment Factor Groups

Tsapakis, Ioannis, Schneider, William H.·2015·Transportation Research Record: Journal of the Transportation Research Board
DOI 10.3141/2527-02
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Abstract

The traditional method of estimating annual average daily traffic (AADT) involves several steps, with the most critical being the assignment process that involves allocating short-term counts to groups of seasonal adjustment factors (SAFs). The accuracy of AADT estimates highly depends on the assignment step, which is subject to errors stemming from human judgment. Support vector machines (SVMs), a supervised learning and statistical method, are employed to construct a series of assignment models that are compared with the traditional method and discriminant analysis (DA) models. Traffic volume data obtained from permanent traffic recorders are used to train and validate the models. The analysis is conducted with SAFs calculated for each direction of travel and separately for two-way traffic. The results reveal that the Gaussian kernel-based SVM model yields the lowest errors, improving AADT accuracy by 65% and decreasing the standard deviation of absolute percentage error by 73.7% over the traditional method. Another finding is that the assignment errors of the directional volume-based analysis are lower than those of the total volume-based analysis by 41.8%. The comparison between the two model parameters examined—the average daily traffic and the hourly factors—indicates that the combined use of both parameters in SVMs is more effective than when hourly factors are used alone. However, in the case of DA, the opposite results are obtained. A possible explanation is that the SVM kernels transfer data from the input space to a feature space and thus provide the ability to assign counts effectively with different types of data within the same model.

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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 [796]

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Authors
Tsapakis, I., & Schneider, W. H.
Affiliations
  • Texas A&M University System (US)
  • University of Akron (US)
Corresponding author
Tsapakis, Ioannis
ORCID
Tsapakis, Ioannis
Venue
Transportation Research Record: Journal of the Transportation Research Board
Publisher
SAGE Publications
Published
2015-01
Volume
2527
Issue
1
Pages
8-17
ISSN
  • 0361-1981 (ISSN-L)
  • 2169-4052
DOI
10.3141/2527-02
OpenAlex
W2244103319
Cited by
10
References
32
Citations by year
2024: 1 · 2022: 5 · 2021: 2 · 2020: 1 · 2016: 1
FWCI
0.45
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
Transportation Research Record Journal of the Transportation Research Board (publishedVersion)
Funders
  • Ohio Department of Transportation
  • Federal Highway Administration
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Mechanical Engineering › Transport Systems and Technology
  • Social Sciences › Transportation › Transportation Planning and Optimization
Keywords
annual average daily traffic, support vector machine, discriminant analysis, traffic volume estimation, Gaussian kernel, two-way traffic

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