United States Department of Transportation
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Developing Predictive Border Crossing Delay Models
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2019-04-01
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[PDF-4.69 MB]
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Edition:Final, January 2014 - January 2019
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Abstract:In recent years, and as a result of the continued increase in travel demand across the border coupled with the need for tighter security and inspection procedures after September 11, border crossing delay has become a critical problem with tremendous economic and social costs. This project aims at taking advantage of the wealth of data, now available thanks to the recent advances in sensing and communications, to develop predictive models which can be used to predict the delay a passenger car or a truck is likely to encounter by the time the vehicle arrives at the border. Specifically, the project first developed an Android smartphone application to collect, share and predict waiting time at the three border crossings. Secondly, models, based on state-of-the-art Machine Learning (ML) techniques, were developed for interval prediction of short-term traffic volume at the border; these models were then utilized to determine optimal staffing levels at the border. Finally, by taking advantage of Bluetooth, border delay data recently collected at the three Niagara Frontier borders, the project developed deep learning models for the direct prediction of border delay. The suite of models and tools developed under this work have the potential to revolutionize border crossing management, balance traffic load at the three crossings, and help travelers avoid significant border delays.
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