Short-Term Traffic Delay Prediction at the Niagara Frontier Border Crossings: Comparing Deep Learning and Statistical Mo
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ORIGINAL PAPER
Short‑Term Traffic Delay Prediction at the Niagara Frontier Border Crossings: Comparing Deep Learning and Statistical Modeling Approaches Rishabh Chauhan1 · Yunpeng Shi2 · Andrew Bartlett3 · Adel W. Sadek4 Received: 10 February 2020 / Revised: 2 July 2020 / Accepted: 7 July 2020 © Springer Nature Singapore Pte Ltd. 2020
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. Keywords Border crossing delay prediction · Deep learning · Multilayer perceptron · Convolutional neural networks · Recurrent neural networks · Auto-regressive integrated moving average (ARIMA)
Introduction * Adel W. Sadek [email protected] Rishabh Chauhan [email protected] Yunpeng Shi [email protected] Andrew Bartlett [email protected] 1
Department of Civil and Materials Engineering, University of Illinois at Chicago, 842 W Taylor St. 2095 ERF, Chicago, IL 60607, USA
2
Department of Civil, Structural & Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA
3
Niagara International Transportation Technology Coalition (NITTEC), 93 Oak Street, Buffalo, NY 14203, USA
4
Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, 204 Ketter Hall, Buffalo, NY 14260, USA
In 2018, more than 26 million passenger cars entered the U.S. through the US–Canada border, with around 4.9 million passenger cars crossing the Niagara Frontier crossing alone (Bureau of Transportation Statistics 2019). In recent years, and specifically after the tragic events of September 11, screening at the US–Canada border, which was earlier mostly seen just as a formality between friends, changed drasti
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