LAUSR

Abstract Commercial Vehicles crossing the international land port of entries (LPOEs) go through multiple screenings/stops contributing to the long queues at the congested border crossings. Although delay measurement has become precise, there is still a lack of predictive performance measures for stakeholders’ meaningful use. Instantaneous performance measures are after-the-fact with limited use for most stakeholders in terms of pro-active decision making. Therefore, as part of this study, we investigated new data sources such as Light-Emitting Diode Detection and Ranging (LEDDAR) and Radio Frequency Identifiers (RFIDs) for calculating border crossings performance measures. Next, we developed a percentile-based outlier detection method for reducing noise in the big datasets. Then, we explored machine learning to predict short-term wait time at a US-Mexico border crossing using Gradient Boosting Regression (GBR) and Random Forest (RF) Regression methods. Finally, GBR and RF machine learning algorithm predictions were compared and evaluated, along with a hybrid algorithm. The results encourage combining more sophisticated predictive algorithms and prediction methods on datasets. The high variability in data is a key challenge for machine learning algorithms leading to non-reliable predictions. This research helps to understand the performance of the LPOEs better and predict the magnitude of the situations when the performance deteriorates.