Research Project:

Evaluation of Different Methods to Calculate Heavy-Truck VMT

Final Report

PDF version 821k
HTML version 300k

Tech Transfer Summary

http://www.ctre.iastate.edu/pubs/t2summaries/heavy_truck_vmt.pdf 561k

Principal Investigator

Shauna Hallmark
Iowa State University
(515) 294-5249
shallmar@iastate.edu

External Project Contact

Dave Plazak
Iowa State University
(515) 296-0814
dplazak@iastate.edu

Project Objective

To evaluate whether current sampling techniques are statistically representative of roadway attributes, and to develop a methodology for transportation-related agencies to share intersection-related asset data.

Project Abstract

Reliable estimates of heavy-truck volumes are important in a number of transportation applications. Estimates of truck volumes are necessary for pavement design and pavement management. Truck volumes are important in traffic safety. The number of trucks on the road also influences roadway capacity and traffic operations. Additionally, heavy vehicles pollute at higher rates than passenger vehicles. Consequently, reliable estimates of heavy-truck vehicle miles traveled (VMT) are important in creating accurate inventories of on-road emissions.

This research evaluated three different methods to calculate heavy-truck annual average daily traffic (AADT) which can subsequently be used to estimate vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa DOT were used to estimate AADT for two different truck groups (single-unit and multi-unit) using the three methods. The first method developed monthly and daily expansion factors for each truck group. The second and third methods created general expansion factors for all vehicles.

Accuracy of the three methods was compared using n-fold cross-validation. In n-fold cross-validation, data are split into n partitions, and data from the nth partition are used to validate the remaining data. A comparison of the accuracy of the three methods was made using the estimates of prediction error obtained from cross-validation. The prediction error was determined by averaging the squared error between the estimated AADT and the actual AADT.

Overall, the prediction error was the lowest for the method that developed expansion factors separately for the different truck groups for both single- and multi-unit trucks. This indicates that use of expansion factors specific to heavy trucks results in better estimates of AADT, and, subsequently, VMT, than using aggregate expansion factors and applying a percentage of trucks. Monthly, daily, and weekly traffic patterns were also evaluated. Significant variation exists in the temporal and seasonal patterns of heavy trucks as compared to passenger vehicles. This suggests that the use of aggregate expansion factors fails to adequately describe truck travel patterns.

Task Descriptions, Milestone, and Dates

Heavy Truck VMT

Document Process for VMT and ESAL Estimation, October 2002
Document Process for Other States, January 2003
Collect Additional Counts, April 2003
Develop Statistical Method, April 2003
Compare Methods and Identify Deficiencies, July 2003
Develop Improvements, October 2003
Develop Recommendations, October 2003

Roadway Geometry

Evaluate Common Methods, October 2002
Identify Specific Data Uses, October 2002
Evaluate Sampling Methods, January 2003
Develop Procedures to Collect Representative Samples, April 2003
Report Results, July 2003

Data Integration

Inventory Sources of Data, January 2003
Develop System Architecture, April 2003
Create the Data Integration Tool, October 2003

Final Report, January 2004

Student Involvement (e.g., Thesis, Assistantships, Paid Employment)

Graduate Students (30 month equivalent)
Undergraduate Students (100 hours total)

Relationship to Other Projects

A pilot study is being conducted in the city of Des Moines to collect an inventory of roadway features for signalized intersections along arterials in the study area. These data will be available for comparison and analysis purposes.

Technology Transfer Activities

Distribution of the final report will be available online to interested parties. Technical briefs, journal articles, workshop presentations, or other methods will be presented as appropriate. The data integration tool will be made available to interested parties.

Potential Benefits of the Project

The project could potentially offer Improved methods for collecting more accurate heavy truck VMT, predicting pavement performance, and scheduling maintenance and rehabilitation. Another benefit of the project is an assessment of whether current data collection and sampling techniques provide adequate data for various asset management and other applications that use inventoried roadway feature data.

Budget

$119,500 ($80,000/year equivalent)

TRB Keywords

Intersection, inventory, data collection, heavy-truck traffic, asset management, decision support