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

Milligan, Craig; Montufar, Jeannette; Regehr, Jonathan D.; Ghanney, Bartholomew

doi:10.1016/j.aap.2016.09.013

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Moreover, based on the distribution of errors, 95% of these estimates would be within 17% of the true AADT at the corresponding site. These results corroborate earlier findings by Milligan et al ( 22 ), which estimated the MAPE to be 6.7% using a similar dataset, but show a higher precision than the 95% confidence limits presented by Krile and Schroeder ( 21 ), which were −22.47% to 25.09%. The errors identified here encapsulate each of the sources described in Figure 1 and represent the total expected error associated with using short duration count data to estimate AADT.…”

Section: Discussionsupporting

confidence: 92%

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Data-Driven Approach to Quantify and Reduce Error Associated with Assigning Short Duration Counts to Traffic Pattern Groups

Grande

Paramita

Regehr

2021

Transportation Research Record

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Traffic monitoring agencies collect traffic data samples to estimate annual average daily traffic (AADT) at short duration count sites. The steps to estimate AADT from sample data introduce error that manifests as uncertainty in the AADT statistic and its applications. Past research suggests that the assignment of a short duration count site to a traffic pattern group (TPG), characterized by known traffic periodicities, represents a significant but poorly quantified source of error. This paper presents an approach to quantify the range of errors arising from such assignments and to mitigate these errors using a novel data-driven assignment method. The approach uses simulated 48-hour short duration counts sampled from continuous count sites with known AADT to develop a benchmark of the total error expected when AADT is estimated from such samples. Likewise, the analysis produces a set of AADT estimates using temporal factors from pre-defined TPGs to quantify the range of assignment errors. The data-driven assignment method aims to mitigate these errors by minimizing the absolute mean deviation in AADT estimates produced from multiple short duration counts in a single year. The approach is applied to traffic data collected in Manitoba, Canada, as a case study. The results indicate that the mean absolute error from 48-hour short duration counts is 6.40% of the true AADT and that improper assignment can lead to a range in mean absolute errors of 9%. When applied to previously unassigned sites, the data-driven assignment method reduced mean absolute errors from 10.32%, using a conventional assignment method, to 7.86%.

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Section: Discussionsupporting

confidence: 92%

“…Gadda et al 17, Sharma et al (18), Schroeder (21), Milligan et al (22) factors applied) to 19.35%. They suggested that classifying the count sites into different categories based on the functional classification, lane count, and area types would help to reduce the AADT estimation error.…”

Section: Sharma and Allipuram (5) Krile Andmentioning

confidence: 99%

Data-Driven Approach to Quantify and Reduce Error Associated with Assigning Short Duration Counts to Traffic Pattern Groups

Grande

Paramita

Regehr

2021

Transportation Research Record

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“…We note that Milligan et al also provide a summary of the AADT estimation errors from several older studies from the literature (25). These studies either do not report the uncertainty range or do so for conditions that are not commonly encountered in practice (i.e., shortterm count duration of a month or greater).…”

Section: Aadt Obsmentioning

confidence: 99%

“…Milligan et al characterized the accuracy of AADT estimated from short-term counts using data from 69 permanent count stations in Manitoba, Canada ( 25 ). They performed their study in a similar manner to the FHWA study but, instead of computing expansion factors from a factor group containing multiple permanent count stations, they computed the expansion factors from the single closest permanent count station belonging to the same traffic pattern group as the short-term count site (they term this the “IPC method”).…”

Section: Introductionmentioning

confidence: 99%

Method for Estimating the Monetary Benefit of Improving Annual Average Daily Traffic Accuracy in the Context of Road Safety Network Screening

Zarei

Hellinga

2022

Transportation Research Record: Journal of the Transportation R

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Conventional road safety network screening (NS) relies on measures of historical crash data and annual average daily traffic (AADT) as a measure of exposure to develop safety performance functions. AADT is typically estimated from short-term counts and contains error which can negatively affect NS outcomes and result in the inefficient allocation of safety improvement resources. In this paper, we propose a simulation-based method for quantifying the monetary benefit of improving AADT accuracy. The results of applying the method under various conditions show that crash data sets with higher sample mean and dispersion parameter values are more sensitive to AADT error and consequently benefit more from improving AADT accuracy. The use of the proposed method is illustrated through the application to a real-world example.

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“…Therefore, obtaining a higher accuracy of annual average daily traffic (AADT) volume is a challenging task. However, due to the inevitable errors of statistical tools and methods, the use of AADT point values is unreliable [ 6 , 7 , 8 ]. In addition, measurement uncertainty is considered to be widespread in the field of road engineering [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Calculation Method of Permanent Deformation of Asphalt Mixture Based on Interval Number

2021

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There are great uncertainties in road design parameters, and the traditional point numerical calculation results cannot reflect the complexity of the actual project well. Additionally, the calculation method of road design theory based on interval analysis is more difficult in the use of uncertain design parameters. In order to simplify the calculation process of the interval parameters in the road design theory, the asphalt pavement design is taken as the analysis object, and the permanent deformation of the asphalt mixture is simplified by combining the interval analysis theory. Considering the uncertainty of the design parameters, the data with boundaries but uncertain size are expressed in intervals, and then the interval calculation formula for the permanent deformation of the asphalt mixture is derived, and the interval results are obtained. In order to avoid the dependence of interval calculation on the computer code, according to the interval calculation rule, the interval calculation method with the upper and lower end point values as point operations is proposed. In order to overcome the contradiction between interval expansion results and engineering applications, by splitting the multi-interval variable formulas, the interval variable weights are reasonably given, and the synthesis of each single interval result realizes a simplified calculation based on interval variable weight assignment. The analysis results show that the interval calculation method based on the point operation rule is accurate and reliable, and the simplified method based on the interval variable weight assignment is effective and feasible. The simplified interval calculation method proposed in this paper provides a reference for the interval application of road design theory.

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

Cited by 10 publications

References 14 publications

Data-Driven Approach to Quantify and Reduce Error Associated with Assigning Short Duration Counts to Traffic Pattern Groups

Data-Driven Approach to Quantify and Reduce Error Associated with Assigning Short Duration Counts to Traffic Pattern Groups

Method for Estimating the Monetary Benefit of Improving Annual Average Daily Traffic Accuracy in the Context of Road Safety Network Screening

Calculation Method of Permanent Deformation of Asphalt Mixture Based on Interval Number

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