Short-Term Traffic Delay Prediction at the Niagara Frontier Border Crossings: Comparing Deep Learning and Statistical Modeling Approaches

Abstract

This study focuses on the short-term prediction of traffic delays, for passenger cars, at the three Niagara Frontier Border Crossings, namely the Peace Bridge, the Lewiston–Queenston Bridge, and the Rainbow Bridge. Predictions are made for up to 60 min into the future, using a delay dataset, collected by Bluetooth readers recently installed at these three border crossings. The delay data were first analyzed to identify the factors affecting traffic delay. Next, future delays were predicted using different deep learning techniques and statistical modeling approaches, including (1) multilayer perceptron (MLP); (2) convolutional neural network (CNN); (3) long short-term memory recurrent neural networks (LSTM RNN); (4) gated recurrent unit recurrent neural network (GRU RNN); and (5) the statistical technique known as the auto-regressive integrated moving average (ARIMA) method. A comparative analysis of the prediction accuracy of the results from the different techniques revealed that the deep learning techniques were capable of predicting border traffic delays with high accuracy, resulting in a value of the mean absolute error (MAEs) of less than 3.5 min, even when predicting delays for up to 60 min into the future. The models developed in this study can serve as a part of a traveler information system that guide travelers to the crossing with the least delay, resulting in more efficient border crossing operations.