Project Title

Feasibility of Estimating Commodity Flows on Highways with Existing and Emerging Technologies

Collaborating Universities

Marshall University
One John Marshall Drive
Huntington, WV 25755

Old Dominion University
1 Old Dominion University
Norfolk, VA 23529

Principal Investigator(s)

Cetin, Mecit (ODU)

Total Project Costs


Start Date


Completion Date



In the opening session of the 2016 North American Travel Monitoring Exhibition and Conference, Tianjia Tang (FHWA Chief of Travel Monitoring and Surveys Division) discussed the need to better translate all of the traffic data being collected into usable information, particularly in the area of performance measures. He specifically emphasized the need to estimate commodity flows of freight movements on the highway network because right now, delay performance measures are typically quantified in terms of the value of time for drivers. There are no systematic ways to quantify the value of goods being transported on specific highways or the cost of delays for those deliveries. In order for an agency to adequately allocate resources to reduce freight delay on the highway network, they need to know the quantity and type of freight/commodity traveling on that corridor. In a connected vehicle world, it is likely that a vehicle’s cargo information could be communicated to the infrastructure for agencies to archive, but there is an existing need to collect this information for planning and monitoring purposes using the technology currently available.

Research Goals and Objectives:  Each unique commodity (e.g., livestock, fuel, machinery, etc.) are hauled in a specific type of trailer. Therefore, if the trailer type can be identified, this will narrow the possible commodity types. The goal of this research project is to determine whether the trailer type can be automatically identified using existing technologies, which is a necessary component of estimating the type of commodity being hauled. This goal will be achieved through the following objectives.

  1. Develop a reference library of unique trailer types and their axle characteristics (e.g., number, spacing, weight) by analyzing existing data sets that consist of weigh‐in‐motion (WIM) data and vehicle photo.
  2. Statistically match individual vehicles to corresponding reference vehicles based on axle characteristics obtained from WIM data.
  3. Develop an algorithm for automatically identifying commercial vehicle trailer type from a side‐fire LiDAR sensor.

Web Links to Reports and to the Project website