For shippers, knowing how long it takes to move freight is important to their bottom line. But until now, accurately predicting the time it takes to cross the border hasn’t been possible.
Though careful vetting of shipments crossing the border is necessary to maintain security, unreliable border wait times can cause major slowdowns for freight traveling from Mexico to the United States. In 2008, the Texas A&M Transportation Institute (TTI) demonstrated a system to the Federal Highway Administration that accurately, reliably leveraged technology to collect border wait time data.
Over the course of several research projects, TTI researchers created a solution that uses radio-frequency identification (RFID) technology, currently present in most trucks, to measure border wait times. Deployed at seven commercial ports of entry across Texas, this system provides anyone interested — and especially U.S. Customs and Border Protection — with reliable estimates via the website TTI created.
Before the website was available, shippers relied on the free travel-time estimates provided by Google to predict cross-border travel times, but the time spent at the crossing was not included in Google’s estimate. TTI’s approach uses the border wait times from the website combined with travel times from Google to provide a better travel-time prediction.
“We’re supplying that missing piece of the puzzle,” says TTI Software Developer Swapnil Samant. “With the machine-learning algorithm we developed, we can predict accurate travel times from origin to destination and post them to the website for a given 24-hour period. And we refine the estimate every half hour.”
By updating the estimate 48 times a day, TTI can provide shippers with an accurate travel-time estimate for commercial vehicles passing through border checkpoints. And it wouldn’t have been possible, Samant says, without the expertise of Jose Rivera Montes De Oca, a Texas A&M University graduate student studying math.
Beginning with data generated in 2013, the algorithm takes reams of historical data and predicts the expected wait time at the border for a given day and time. That means more efficient cross-border supply chains, and that can mean a better bottom line for U.S. manufacturers and, potentially, savings for consumers.
“Sometimes the field of mathematics is so theoretical, you can’t really explain what you do to other people,” says Rivera Montes De Oca. “But I can point to the website and show them how my work makes a difference. My time at TTI has been amazing. If I could work here the rest of my life, I would.”
Originally published as “Student Insights Lead to Research Innovations: TTI Provides More Reliable Cross-Border Travel Time Estimates” in the Texas Transportation Researcher, Volume 53, Number 1 (2017).