In the transportation research business, we use the term “performance measurement” when we measure how a certain aspect of our transportation system is operating. For example, through crash reporting we can tell you with certainty how safe a road is operating, or, through commute times, how well traffic is flowing. Safety and mobility of a roadway are fairly easy to determine.
But when it comes to measuring performance of a land port of entry (LPOE) used for freight transport, things get a lot more complicated thanks to the complexity of border crossing operations. Our goal is to provide the information needed for various stakeholders so our borders can operate as efficiently as possible.
Our Center for International Intelligent Transportation Research (CIITR) team is tackling a first-of-its-kind border crossing mobility measurement project designed to vastly improve border-specific mobility measures needed by the US Custom and Border Protection, truckers and shippers, the Texas Department of Transportation, the El Paso Metropolitan Planning Organization, the City of El Paso, and El Instituto Municipal de Investigación y Planeación.
Because of previous CIITR projects at the Zaragoza-Ysleta Bridge, real-time traffic volumes at the LPOEs in El Paso, Texas, are archived and aggregated by time intervals. And crossing times for US-bound commercial vehicles are now available through the Border Crossing Information System (BCIS). While a lot of new data is available, wait times and crossing times do not provide historic and future trends, which are necessary for short-term and long-term decision making. And, there is no single performance measure that can compare multiple LPOEs.
Through this project, entitled Developing Adaptive Border Crossing Mobility Measures and Short-Term Travel Time Prediction Model Using Multiple Datasets, we investigated and developed freight mobility performance measures designed to present a realistic picture of border crossing performance. Specifically, we developed a short-term wait time estimation algorithm, created a methodology to identify outliers and cleaned the volume and travel-time datasets from BCIS. We performed data-driven and time-based trend analysis, and developed a consistent score using multiple performance measures for comparison.
We believe this work will help offer a new and better way of measuring performance at border crossings. The resulting benefits will help stakeholders be better prepared for making mobility-related decisions during disasters, short-term disruptions or otherwise congested conditions at LPOEs. And shippers will be better able to schedule and plan their freight movement.
Although this project made much progress in developing border crossing performance measures, scores and trends, we need a way to get this information to the stakeholders. We envision a single product, perhaps similar to the BCIS website, where stakeholders can find this new information so they can make decisions useful to them.
First, we need to interview the stakeholders so we can understand the type of performance measures they need so we can help customize and prioritize the data. And that will be our goal going forward as we continue this vital work.