The National Academies

NCHRP 07-30 [Anticipated]

Assignment of Short-Duration Traffic Volume Counts to Adjustment Factor Groups

  Project Data
Source: North Carolina Department of Transportation
Funds: $500,000
Staff Responsibility: Amir N. Hanna
Fiscal Year: 2021

This project has been tentatively selected and a project statement (request for proposals) is expected in August 2020. The project statement will be available on this site. The problem statement below will be the starting point for a panel of experts to develop the project statement.

Annual average daily traffic (AADT) is one of the most widely used data inputs in transportation engineering. Transportation agencies use AADT to meet data reporting requirements, allocate resources, better inform decision-making, and support various agency functions. State departments of transportation (DOTs) are required to report AADT every year to the Highway Performance Monitoring System (HPMS) for the full extent of mainlines, samples, and ramps on all Federal-aid facilities (Federal Highway Administration, Traffic Monitoring Guide--TMG). In addition, the 2016 Highway Safety Improvement Program (HSIP) Final Rule requires States to have access to AADT for all paved public roads by year 2026. Transportation agencies estimate AADT using variations of a traditional method that was first introduced by Drusch in 1966 and is recommended by FHWA’s TMG. The traditional approach combines traffic data from permanent and portable traffic counting equipment. Continuous count sites (CCSs) collect traffic data 24 hours a day, seven days a week for all days of the year or extended periods of time.1 Because of the high installation, operation, and maintenance cost of CCSs, agencies tend to install them at select locations and conduct short-duration counts at locations that have not been counted. The goal is to adjust and expand the short-duration counts to obtain accurate AADT estimates. The main steps of the traditional method are (a) Gather traffic volume data from CCSs and calculate adjustment factors (e.g., seasonal, monthly, day-of-week, axle, etc.) for each CCS--widely known as the “factoring” step; (2) Establish monthly pattern groups that are homogenous. Create factor groups based on one or multiple grouping approaches--widely known as the “grouping” step; (3) Compute adjustment factors (e.g., hour-of-day factor, month of year factor) for each group (from the factors of the CCSs contained in each group); (4) Assign short-duration counts to the previously determined factor groups; agencies typically base the assignment task on the location, functional class or other characteristics of the roadway section where a count was taken--widely known as the “assignment” step; and (5) Multiply the average daily traffic (ADT) of a short-duration count with the appropriate group adjustment factor(s) to generate an AADT estimate.

One caveat of the traditional AADT estimation process is that the accuracy of the predictions is subject to errors inherent within each step of the process. Prior research has shown that the “assignment” step is the most critical element in the AADT estimation process. Potential ineffective allocation of short-duration counts to factor groups may triple the prediction error, yet, a small number of studies have dealt with the improvement of the assignment procedure. Because of limited research and knowledge on this topic, current guidelines are not prescriptive on how short-duration counts should be assigned to factor groups. Previous studies have concluded that statistical methods are necessary to support the assignment step, which is subject to human errors stemming from engineering judgment. This research will fill this gap by determining the most effective assignment methods that agencies can use to improve the accuracy of AADT estimates derived from short-term counts. The problem statement directly relates to the scope, objectives, and seven core data principles (valuable, available, reliable, authorized, clear, efficient, accountable) of the AASHTO Committee on Data Management and Analytics.

The literature reveals a limited number of studies that have concentrated on the assignment process. Though some of these methods have produced promising results, the majority of past studies are limited in scope and objectives; examine, validate, and compare a small number of methods; focus on small regions and transportation networks that have specific characteristics; use limited data from a small number of carefully selected CCSs; and consider short study periods. As a result, it is difficult to generalize past research findings and results and draw safe conclusions about the most effective count assignment methods. An assignment method that has proved to be effective in one region or state may not necessarily be effective in a different part of the country that exhibits different traffic, roadway, demographic, socioeconomic, and/or weather characteristics. Because of those limitations, there is limited guidance on how short-duration counts should be assigned to factor groups. To fill this gap, there is a need to conduct a comprehensive and in-depth study that will have wider scope and validate the performance of several assignment methods for different transportation networks nationwide. The need for this research study is described in a recent TRB e-Circular.

The objective of this project is to determine the most effective methods of assigning traffic volume counts to adjustment factor groups. The project should be conducted in two Phases. Phase I, Research includes Task 1, Review the current state of practice and state of the art and identify candidate assignment methods for further examination; Task 2, Select up to five states to apply the assignment methods identified in Task 1; Task 3, Gather and process data needed to apply the assignment methods identified in Task 1; Task 4, Apply the assignment methods selected in Task 1 and validate their performance; and Task 5, Develop project deliverables, including a Phase II Work Plan. Phase II, Implementation, includes Task 6, Conduct at least five pilot studies with three state and two local public agencies to apply the most appropriate method(s) at each agency and Task 7, Develop Phase II project deliverables, including a Final Guidebook that will provide separately for each assignment method all the necessary information and elements (e.g., pros, cons, data inputs, methodological considerations, assumptions, anticipated accuracy, software requirements, implementation time and costs) to help transportation agencies select and implement the most appropriate assignment method(s).

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