Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.
Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations.
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%.
A short‐duration vehicle classification count program provides essential data for developing a system‐wide understanding of truck traffic volume. In responding to increasingly urgent truck traffic data needs, transportation agencies face the challenge of implementing improvements with constrained resources. Within this context, this paper develops and applies a decision support tool that reveals trade-offs amongst program design parameters. The Tool enables decision‐makers to simultaneously consider two broad and inter‐related program objectives, namely, to achieve a target level of classification count coverage and to minimize changes in resource requirements. The Tool incorporates five decision input parameters (coverage, technology type, count duration, frequency, and counting cycle) and produces information concerning count accuracy, the number of equipment units required to implement the program, the classification counting season duration, costs, and considerations such as count redundancy and data timeliness. The paper presents Manitoba’s short-duration count program as a case study and evaluates three program options.
The use of rail profile measurements for the planning and specification of rail-grinding activities normally involves comparing the existing and desired rail profiles within a rail segment. In current practice, a somewhat subjective approach is used to select a measured profile—usually located near the midpoint of the segment—that represents the profiles throughout the rail segment. Industry-standard rail profile data were used to develop an automated procedure for calculating a representative average (mean) rail profile for a rail segment. The procedure was verified by comparing the calculated average with an expected profile. Then, it was validated by comparing the calculated average profiles of 42 in-service rail segments (10 tangents and 32 curved segments) with the Corresponding median rail profiles for each segment, chosen subjectively. Validation results indicated that the coordinates comprising the mean and median profiles differed by less than 1%, on average. Agreement was stronger for tangent rail segments than for curved rail segments, as expected. Therefore, validation demonstrated that the procedure yields results comparable with current practice while it improves the objectivity and repeatability of the decisions that support rail-grinding activities. The procedure also offers the opportunity for integration with existing software tools to help automate the specification of grinding activities that minimize metal removal and prolong rail life.
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