An interdisciplinary team of researchers at the University of Arkansas will use $1.5 million to develop a new traffic-sensing technology to track cargo as it moves on roadways and through ports.
The money was awarded by Inter-Modal Holding LLC, an Ohio-based holding company that specializes in transportation, commerce, technology and related infrastructure. The project will help business leaders and government agencies better plan for future investment in infrastructure and economic development, according to a news release.
The project, led by Sarah Hernandez, assistant professor of civil engineering, is unique because it allows researchers to track commodity movement without having to stop traffic or cut into roadways to install equipment, the release shows. UA researchers will use a combination of LIDAR and video sensing equipment and have developed an algorithm that can distinguish among trailer types, including container, platform, livestock, dump and others to provide insights into commodity flows along a roadway or port.
LIDAR, which is similar to radar, uses pulsed, infrared laser light to detect distant objects and measure the distance to those objects.
“Our inland waterways move a significant amount of freight and rely equally on efficient water- and land-side transportation systems,” Hernandez said. “This project will provide detailed truck volume data for the roads used to access inland waterway ports. With this data, we can better design pavements, manage port operation and direct funds to support better highway connectivity.”
The project will help to improve efficiencies through a better understanding of how water and truck transport systems interact, Hernandez said.
Researchers include an interdisciplinary team comprising faculty, research associates and students from the department of civil engineering, computer science and computer engineering, and the Center for Advanced Spatial Technologies.
The goal is to develop a network of interconnected data collection systems to monitor and manage inland waterway activity including port and terminal operations, vessel movements and vehicle activity.
“The low-cost sensor developed in this project has the potential to deploy across the U.S. so state transportation agencies and private industry can better understand the demand and usage of critical inland waterways and supporting highway infrastructure,” Hernandez said. “What’s currently missing in decisions regarding multi-modal water and truck investments is a clear understanding of how the two modes interact. This research, through our specialized traffic senor, will inform multimodal system operations and management.”
The research will generate data to be a driver for future policy decisions, the release shows.
“This project will develop a traffic senor that measures truck activity in such a way that trucks, drivers and fleets remain anonymous but still provide the level of data needed to create policy and prioritize transportation investments for efficient freight movements,” Hernandez said. “Public transportation agencies and private firms and operators need to understand when, where and what freight is moving. This information can be used to design targeted policies to promote critical industries and to identify and select infrastructure projects that support critical or underserved industries.”
The research will focus on the Upper Ohio River Valley region, a major transportation system for the United States, according to the release. The original work was sponsored by the Maritime Transportation Research and Education Center (MarTREC), which is based at the UA.
The project requires expertise from across UA departments to complete, Hernandez said.
“This team is very much interdisciplinary consisting of civil engineers, computer scientists and computer engineers, geographers and photogrammetry experts,” she said. “The senor we are designing requires hardware and software development that has to meet highway and traffic standards. There are also aspects related to communications protocols, power adaption and field data collection. This means we need to pool expertise from a broad and diverse team.”