Methodology to Characterize Ideal Short-Term Counting Conditions and Improve AADT Estimation Accuracy Using a Regression-Based Correcting Function

Authors

Miguel Figliozzi, Portland State UniversityFollow
Pam Johnson, Portland State UniversityFollow
Christopher M. Monsere, Portland State UniversityFollow
Krista Nordback, Portland State UniversityFollow

Published In

Journal of Transportation Engineering

Document Type

Citation

Publication Date

5-2014

Subjects

Bicycle commuting -- United States, Urban transportation -- United States, Cycling -- Route choice, Traffic signs and signals -- Control systems

Abstract

Transportation agencies’ motor vehicle count programs tend to be well established and robust with clear guidelines to collect short-term count data, to analyze data, develop annual average daily traffic (AADT) adjustment factors, and to estimate AADT volumes. In contrast, bicycle and pedestrian traffic monitoring is an area of work for most transportation agencies. In most agencies, there are a low numbers of counting sites and limited agency experience to manage a city-wide or state-wide system of collecting, processing, and using nonmotorized data. Short duration counts are used to estimate longer duration volumes such as AADT. Because bicycle or pedestrian short-term counts vary dramatically over time and significantly more than motorized vehicle counts, the direct application of motorized vehicle AADT estimation methods may be inadequate. The goal of this paper is to present a methodology that will enhance, if needed, existing AADT estimation methods widely employed for motorized vehicle counts. The proposed methodology is based on the analysis of AADT estimation errors using regression models to estimate a correcting function that accounts for weather and activity factors. The methodology can be applied to any type of traffic with high volume variability but in this research is applied to a permanent bicycle counting station in Portland, Oregon. The results indicate that the proposed methodology is simple and useful for finding ideal short-term counting conditions and improving AADT estimation accuracy.

Description

© 2014 American Society of Civil Engineers

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Unaffiliated researchers can access the work here: http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000663

DOI

10.1061/(ASCE)TE.1943-5436.0000663, 04014014

Persistent Identifier

http://archives.pdx.edu/ds/psu/20786

Citation Details

Figliozzi, M., Johnson, P., Monsere, C., and Nordback, K. (2014). "Methodology to Characterize Ideal Short-Term Counting Conditions and Improve AADT Estimation Accuracy Using a Regression-Based Correcting Function." J. Transp. Eng.