2019
DOI: 10.1177/0361198119833347
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Estimating Traffic Volume for Local Streets with Imbalanced Data

Abstract: Annual average daily traffic (AADT) is an important measurement used in traffic engineering. Local streets are major components of a road network. However, automatic traffic recorders (ATRs) used to collect AADT are often limited to arterial roads, and such information is, therefore, often unavailable for local streets. Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient. A challenge is that an under-represented … Show more

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Cited by 10 publications
(6 citation statements)
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“…A simple alternative is to use parametric regression methods (e.g., ordinary linear regression [OLS]). Regression models are easy to develop and their interpretation is mostly straightforward (6,(18)(19)(20)(21). Examples of this approach include Mohamad et al, who applied OLS for county roads using data collected within 40 selected counties in Indiana, and Zhao and Chung, who adopted OLS to evaluate a variety of factors collected in Broward County, Florida (19,20).…”
Section: Related Workmentioning
confidence: 99%
“…A simple alternative is to use parametric regression methods (e.g., ordinary linear regression [OLS]). Regression models are easy to develop and their interpretation is mostly straightforward (6,(18)(19)(20)(21). Examples of this approach include Mohamad et al, who applied OLS for county roads using data collected within 40 selected counties in Indiana, and Zhao and Chung, who adopted OLS to evaluate a variety of factors collected in Broward County, Florida (19,20).…”
Section: Related Workmentioning
confidence: 99%
“…Thus, estimation is done at the federal, state, and local levels (Jessberger et al, 2016). Chen et al (2019) acknowledge the importance of AADT data estimation for traffic engineering. Chen et al (2019) suggest that AADT data are essential for transportation planning and traffic monitoring and guarantee cost savings to data collection.…”
Section: Discussionmentioning
confidence: 99%
“…In contrast, population and non-retail employment have negative signs as opposed to findings from other studies. Finally, Chen et al ( 40 ) used a generalized linear mixture model (GLMM) and the synthetic minority over-sampling technique (SMOTE) with 20 variables in Seattle, U.S.A. The findings show that 17 and 15 variables, respectively, are statistically significant with the “spatial weighted volume” having the highest positive coefficient.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The spatial weighted volume is defined as the “the sum of other roads’ weighted AADT divided by the reciprocal of squared Euclidean distance, in counts/sq. feet” ( 40 ). Roadway characteristics also indicate high positive signs, while five variables for the GLMM and seven for the SMOTE have negative coefficients, with local streets and one-way roads exhibiting the highest.…”
Section: Literature Reviewmentioning
confidence: 99%