Toward a crowdsourcing solution to estimate border crossing times using market-available connected vehicle data

Effectively monitoring border crossing time is of great importance to various stakeholders. Border crossing information systems currently implemented along the United States-Mexico border require a large installed base of sensors, costly for installation and maintenance. This study provides a preliminary assessment of market-available connected vehicle (CV) data in border crossing time estimation. We evaluated one week of CV data collected at the Paso del Norte (PDN) port of entry (POE). We used a set of big data analytic tools to process big CV datasets and generated CV-based border crossing times (CV-Time). Then, we evaluated the correlation between the CV-Time and the existing Bluetooth-generated border crossing times (Bluetooth-Time) at the PDN POE. Last, we built a regression model to estimate the Bluetooth-Time (ground truth data) based on CV-based variables. The results demonstrate that the CV-Time is strongly correlated with the Bluetooth-Time, with a correlation rate of approximately 0.89. This study demonstrates that the market-available CV data is a potential data source for monitoring border crossing times.