Forecasting Truck Parking Using Fourier Transformations
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
Truck-based transportation is the predominant mode used to transport goods and raw materials within the United States. While trucks play a major role in local commerce, a significant portion of truck activity is also long haul in nature. Long-haul truck drivers are continuously faced with the problem of not being able to secure a safe parking spot since many rest areas become fully occupied, and information about parking and availability is limited. Truck drivers faced with full parking lots/facilities either continue driving until a safe parking spot is located or park illegally. Both scenarios pose a hazard to the truck driver, as well as the surrounding road users. Disseminating forecasts of parking availability to truck drivers may help mitigate this hazard, since many truck drivers plan their parking in advance of arrival. Building on 1 year of nearly continuous truck parking data collection, this paper proposes and demonstrates a method for developing a dynamic forecasting model that can predict truck parking occupancy for any specified time within the present day, using only truck parking occupancy data from a trucking logistics facility in the northern San Joaquin Valley during 2016. Different versions of the dynamic model were studied and verified against successive weekdays with performance measured using the root-mean-square error (RMSE). Results indicated that for a particular day, the maximum error can range between 13 and 40 trucks, about 5% of the absolute maximum capacity of the facility.
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Data Availability Statement
All data, models, and code generated or used during the study are available from the corresponding author by request (raw truck parking occupancy data, processed truck parking occupancy data, code used to train the Fourier model and generate forecasts, and code used to compute performance metrics).
Acknowledgments
This research was generously supported at the Transportation Sustainability Research Center at the University of California, Berkeley, by the Federal Highway Administration and the California Department of Transportation. The research sponsors had no involvement in the study design; collection, analysis, and interpretation of data; writing of the report; and in the decision to submit the paper for publication. We would also like to thank Veltin Dupont, who suggested the promise of Fourier methods for this problem, as well as Matt Hanson, Jim Sells, and Rachel Finson, who all contributed to this project. Each has been supportive of truck parking research and applications in California, and their contributions to this initiative are highly valued.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 1, 2018
Accepted: Mar 6, 2020
Published online: May 31, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 31, 2020
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