Abstract
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%.
Highlights
average daily traffic (AADT) is a ubiquitous traffic statistic, essential for applications such as infrastructure design and management, road safety assessments, resource allocation, economic appraisals, roadway and transport system planning, operational and environmental analyses, and transportation research (1–8)
The analysis identifies the spectrum of errors that are produced using this AADT estimation method for current traffic pattern group (TPG) assignments, addressing the first research question: What are the expected errors in current AADT estimates produced from short duration counts?
This paper presents an approach to quantify the range of errors arising from the assignment of short duration counts to TPGs and proposes a novel data-driven method to mitigate these errors
Summary
AADT is a ubiquitous traffic statistic, essential for applications such as infrastructure design and management, road safety assessments, resource allocation, economic appraisals, roadway and transport system planning, operational and environmental analyses, and transportation research (1–8). In the United States, state and municipal transportation agencies submit system-wide estimates of AADT (and vehicle-miles traveled) on their highway networks as required by the Highway Performance Monitoring System (HPMS) (9). Having access to AADT for all paved public roads in the United States by 2026 is a requirement of the 2016. 1. Calculate temporal factors at each continuous count site. 2. Group continuous count sites into traffic pattern groups (TPGs) with similar temporal factors. 3. Calculate average temporal adjustment factors for each TPG
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