U.S. Xpress, MIT partner to improve driver efficiency

U.S. Xpress, MIT partner to improve driver efficiency
Graduate students in MIT’s Supply Chain Management master’s degree program are analyzing data from U.S. Xpress to help improve driver efficiency. (Courtesy: U.S. Xpress)

CHATTANOOGA, Tenn. — U.S. Xpress Inc. is collaborating with the Massachusetts Institute of Technology’s (MIT) Center for Transportation & Logistics to develop a roadmap to improve driver efficiency, according to a statement released by U.S. Xpress.

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Graduate students in the MIT Supply Chain Management master’s degree program will use statistical modeling and artificial intelligence to study U.S. Xpress company data, including GPS stats for more than 7,500 tractors; loaded and unloaded data for nearly 15,000 trailers; driver hours of service; shipper rates; appointment times; and arrival and departure trends. MIT plans to publish research findings in summer 2021.

“This partnership with MIT is another example of U.S. Xpress tapping the brightest minds in technology to help drive company innovation,” said Eric Fuller, president and CEO of U.S. Xpress. “Improving driver efficiency can ultimately enhance the driver experience and generate more value for our shipping partners.”

Per federal hours-of-service regulations, truck drivers are limited to a 14-hour shift, but just 11 of those can be spent driving. MIT’s capstone research will outline opportunities to safely maximize efficiency within that 11-hour driving window to bring more value to shippers, drivers and the company.

In addition to partnering with MIT, U.S. Xpress is taking steps to improve efficiency by developing parking locators that reduce wasted miles spend finding open parking spaces; telematics, geo-fencing and GPS tracking to route tractors to service facilities; and predictive analytics that bring tractors in for service before service failures occur on the road. In 2019, U.S. Xpress introduced Variant, a new carrier brand built on technology. Proprietary software utilizes machine learning and algorithms to automate load planning and scheduling, generating more revenue for drivers and the company.

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