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.

穿越国际陆路入境口岸 (LPOE) 的商用车辆经过多次安检/停靠，导致在拥挤的过境点排长队。尽管延迟测量已经变得精确，但仍然缺乏对利益相关者有意义的使用的预测性能测量。即时绩效衡量是事后的，大多数利益相关者在主动决策方面的使用有限。因此，作为本研究的一部分，我们调查了新的数据源，例如发光二极管检测和测距 (LEDDAR) 和射频标识符 (RFID)，用于计算过境性能指标。接下来，我们开发了一种基于百分位数的异常值检测方法，用于减少大数据集中的噪声。然后，我们探索了机器学习，以使用梯度提升回归 (GBR) 和随机森林 (RF) 回归方法预测美墨边境口岸的短期等待时间。最后，对 GBR 和 RF 机器学习算法预测以及混合算法进行了比较和评估。结果鼓励在数据集上结合更复杂的预测算法和预测方法。数据的高度可变性是导致不可靠预测的机器学习算法的关键挑战。这项研究有助于更好地了解 LPOE 的性能，并预测性能恶化时情况的严重程度。以及混合算法。结果鼓励在数据集上结合更复杂的预测算法和预测方法。数据的高度可变性是导致不可靠预测的机器学习算法的关键挑战。这项研究有助于更好地了解 LPOE 的性能，并预测性能恶化时情况的严重程度。以及混合算法。结果鼓励在数据集上结合更复杂的预测算法和预测方法。数据的高度可变性是导致不可靠预测的机器学习算法的关键挑战。这项研究有助于更好地了解 LPOE 的性能，并预测性能恶化时情况的严重程度。