by David Salgado Manzano and Jeff Shelton
If you spend much time along the border, especially during peak crossing times, you can find yourself waiting for hours to get through a checkpoint. And while improved trade between the U.S. and Mexico is a good thing, one down side is that the increased traffic is adding to the problem.
Excessive wait times at the border can have tremendous negative impacts on our mobility, economy and air quality. As transportation researchers with a focus on land ports of entry (LPOEs), it’s our goal to lessen the congestion — thereby improving travel times and, hopefully, reducing pollution — at border crossings while boosting the economic opportunity increased trade brings with it.
One way to understand what causes operational problems at LPOEs is through traffic simulation models that replicate and forecast operational conditions. (It’s much more cost effective to simulate operational conditions than measure them constantly in the real world, as long as you can simulate that reality in a reliable, accurate way.) Unfortunately—because of the unique characteristics of border crossings—standard travel-demand models don’t really tell us much. We need models that provide higher levels of detail, or simulation-based models that capture realistic vehicle and pedestrian movement over a large area (like an entire city).
Software can help us create traffic-simulation models that helps us figure out where inefficiencies occur, and that’s the first step to
suggesting solutions to the problem of long wait times. But which software package does that most effectively in terms of forecasting traffic conditions at LPOEs? To find that out, we’re currently performing a comparative analysis on three different simulation models using data collected in the field to measure each of the three packages’ accuracy and reliability. Running different scenarios will give us output to be compared to field data—the reality we know to be true—to determine which program is best able to represent that reality via accurate border crossing-traffic simulations.
Also, we want to determine how accurately the programs forecast conditions at both a local and regional level. For the latter, we’re specifically looking at the El Paso/Juarez binational region as a case study to test a series of forecasting scenarios (e.g., impacts of bridge closures) to determine how cross-border freight and passenger vehicles are impacted on a regional scale and how disruptions to the transportation system can be captured and predicted in a simulated environment.
That’s important, because regional decision-makers are asking more and more complex questions regarding the LPOEs. Having a reliable model that incorporates all the border crossing variables can help predict how changes in one of them—like increasing the number of border inspection stations, for example—will impact wait times.
Armed with that information, local agencies can develop more-effective strategies to reduce wait times and decrease local pollution, all while facilitating continued growth in cross-border trade. And achieving those things can encourage a vital economy of opportunity for local residents, enhanced profits for businesses both at the border and beyond, and improved health for citizens in the region.
David Salgado Mazano is an associate transportation researcher and Jeff Shelton is an associate research scientist with TTI’s Center for International Intelligent Transportation Research.