Continuous Outreach and Engagement Contributes to the Success of Technology Deployment.

The Florida DOT identified several potential opportunities to advance research in the area of automated freight data collection.

Date Posted

04/30/2021

Statewide, Florida, United States

Statewide, Florida,

United States

Identifier

2021-L01022

Fueling Freight Movement: Emerging Technologies Help Clarify South Florida’s Regional Petroleum Supply Chain - Freight Demand Modeling and Data Improvement Implementation Support

Summary Information

In this study, Florida DOT (FDOT) District 4 used license plate recognition and video image processing to distinguish truck and rail fuel tankers from regular semi-trailers and rail cars in South Florida. Petroleum shipment information through the twelve counties in South Florida were also collected and approximated using Florida Department of Revenue (FDOR) tax records, Global Positioning System (GPS) truck probe data, and driver surveys to understand petroleum flows from Port Everglades.

This study led to the following lessons learned:

“Collaboration between public and private sector stakeholders resulted in an overall better understanding of the individual segments of the petroleum transport community, including shippers, carriers, and customers of the supply and demand chain.”
To successfully engage the private sector, which has less interest in longer-range infrastructure planning, continuous outreach and engagement is important. To better engage private industry, proactive outreach activities and messages are essential to clearly communicate the benefit of participation.
Limitations and challenges should be considered when using video image processing technology; for example, working through a vendor for the video image processing can hinder the full understanding of the image recognition process, due to the proprietary process and algorithms used by the vendor. The lack of transparency can be time consuming to independently validate the data produced through the technology.
Regression analyses can be suitable to compensate for any gaps in data.