Application of Regression Analysis for Identifying Factors That Affect Seasonal Traffic Fluctuations in Southeast Florida

Li, Min-Tang; Zhao, Fang; Wu, Yifei

doi:10.3141/1870-20

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…One objective of a statewide traffic monitoring program is to accurately estimate the annual average daily traffic (AADT) for many roadway segments within the state. As a result of the importance of coverage counts and the estimation of the AADTs, there has been a diversity of research that incorporates traditional techniques, neural networks, principal component analysis, Bayesian methods, and regression analysis . The majority of the Departments of Transportation (DOTs) in the USA implement a traditional method developed by Drusch in 1966 .…”

Section: Introductionmentioning

confidence: 99%

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Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Tsapakis

Schneider

Nichols

et al. 2012

J of Advced Transportation

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SUMMARY The precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

“…In the fourth step, after the ACV F and the CV ADT are calculated, a final model is developed with a general form based on Equation (7). According to this equation, the two variables are weighted differently and produce a WCV.…”

mentioning

confidence: 99%

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Tsapakis

Schneider

Nichols

et al. 2012

J of Advced Transportation

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“…Other factors, such as demographics, socioeconomics, and land use types, have also been investigated as possibly explanations of seasonal traffic variations [2,3,17,18] . These factors, if understood and quantified, may aid in the assignment of SFs from one or more TTMSs to a PTMS, which may potentially reduce the data collection effort required and improve the accuracy of AADT estimations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Believing that there is a connection between land use and seasonal traffic variations, Li et al [17] employed a regression method to model the relationship between SFs and land use variables with limited TTMSs data on urban roads in South Florida. A number of influential variables were identified, including the concentration of seasonal households and retired households with high income, hotel/motel population, and retail employment.…”

Section: Literature Reviewmentioning

confidence: 99%

Seasonal Factor Assignment Based on the Similarity of Hourly Traffic Patterns and Influential Variables

Lu¹,

Yang²,

Yang³

et al. 2013

JSW

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Average Annual Daily Traffic is typically estimated by applying seasonal factors (SFs) to short-term counts. SFs are obtained from continuous count sites and assigned to short-term count sites. This assignment procedure is usually empirical and subjective. Some previous studies have attempted to establish relationships between SFs and influential variables to provide an objective and data-driven alternative for SF assignment. However, in rural areas, SFs are difficult to model due to low land use intensity and, sometimes, significant through traffic. This paper presents a study of relationships between monthly SFs and hourly traffic patterns, land use, and other variables, using data from 116 continuous counters in rural areas throughout Florida. It is found that hourly traffic patterns are related to traffic seasonality and can be used to improve the modeling of influential variables that affect SF. The influential variables are then used for seasonal factor assignment and estimation. The proposed method achieved an average error of four percent, with 95 percent of the estimated monthly SFs having an error of no more than ten percent

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“…In addition, these characteristics (profiles of seasonal variations) are used in road network reliability analysis [17], in the new approach of determining design volumes based on generalized costs (economic analyses) [2], and in making decisions within the Intelligent Transportation Systems and general traffic management. The most effective way of grouping sections of roads is through a combination of expert knowledge (on the basis of functional/geographical placement [3 -5, 13, 15, 16, 22]) with one of the mathematical methods (cluster analysis, discriminant analysis, multiple linear regression, genetic algorithms, artificial neural networks, or hybrid models such as the combination of the fuzzy sets theory with neural networks [1,4,5,6,8,9,10,12]). As explanatory variables, data describing road sections (functional class, location, number of lanes), road users (traffic character, destination), and demographic and socioeconomic characteristics are commonly used.…”

Section: Introductionmentioning

confidence: 99%

Factors Determining Seasonal Variations in Traffic Volumes

Spławińska

2017

Archives of Civil Engineering

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The characteristics of seasonal variations in traffic volumes are used for a variety of purposes, for example to determine the basic parameters describing annual average daily traffic – AADT, and design hourly volume – DHV, analyses of road network reliability, and traffic management. Via these analyses proper classification of road sections into appropriate seasonal factor groups (SFGs) has a decisive influence on results. This article, on the basis of computational experiments (models of artificial neural networks, discriminatory analysis), aims to identify which factors have the greatest impact on the allocation of a section of road to the corresponding SFG, based on short-term measurements. These factors are presented as qualitative data: the Polish region, spatial relationships, functions of road, cross-sections, technical class; and quantitative data: rush hour traffic volume.

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Application of Regression Analysis for Identifying Factors That Affect Seasonal Traffic Fluctuations in Southeast Florida

Cited by 14 publications

References 4 publications

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

Seasonal Factor Assignment Based on the Similarity of Hourly Traffic Patterns and Influential Variables

Factors Determining Seasonal Variations in Traffic Volumes

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