Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree

Li, Min-Tang; Zhao, Fang; Chow, Lee-Fang

doi:10.1061/(asce)0733-947x(2006)132:8(654)

Cited by 20 publications

(13 citation statements)

References 25 publications

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“…In this study, additional variables such as hotels, population, and the ratio of seasonal to permanent households help explain the seasonal traffic patterns [20]. The findings of this study show that the decision tree is an objective method limited by the potential for insufficient data [20]. Jin et al [21] developed a k nearest neighbor classification method and concluded that unweighted k nearest neighbor models produced more accurate AADT estimates than traditional non-cluster methods [21].…”

mentioning

confidence: 90%

“…Li et al . developed a “fuzzy logic” decision tree for the Florida DOT to assign seasonal groups to short‐term counts. In this study, additional variables such as hotels, population, and the ratio of seasonal to permanent households help explain the seasonal traffic patterns .…”

Section: Introductionmentioning

confidence: 99%

“…In this study, additional variables such as hotels, population, and the ratio of seasonal to permanent households help explain the seasonal traffic patterns . The findings of this study show that the decision tree is an objective method limited by the potential for insufficient data . Jin et al .…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [20] developed a "fuzzy logic" decision tree for the Florida DOT to assign seasonal groups to short-term counts. In this study, additional variables such as hotels, population, and the ratio of seasonal to permanent households help explain the seasonal traffic patterns [20]. The findings of this study show that the decision tree is an objective method limited by the potential for insufficient data [20].…”

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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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confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…As in direct conversion method, the regression model is not powerful enough to estimate SF directly with the identified influential factors. Li et al [19] developed a fuzzy decision tree to classify a count site based on the value of selected variables that were identified in regression analyses. However, to validate the assignment results requires the considerable effort of collecting additional monthly short-term counts.…”

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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Road safety performance measures and AADT uncertainty from short-term counts

Milligan

Montufar

Regehr

et al. 2016

Accident Analysis & Prevention

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Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree

Cited by 20 publications

References 25 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

Road safety performance measures and AADT uncertainty from short-term counts

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