Abstract
Unavailability of sufficient parking spaces during various periods at rest areas results in illegal and unsafe parking at on/off-ramps and other unauthorized areas, which may lead to traffic safety hazards. In this research, the authors attempt to understand truck parking utilization by developing econometric models by using truck global positioning system (GPS) data to predict truck parking utilization at rest areas to improve truck parking management and ensure proper use of parking spaces. Count models, including Poisson and negative binomial models, were developed in addition to generalized ordered-response probit (GORP) models to understand the factors that affect truck parking utilization. The GORP model that subsumes the Poisson model as a special case was found to provide the best data fit. The model results suggest that several factors positively contribute to truck parking utilization (e.g., truck volume on the adjacent roadway, number of lanes) at rest areas, whereas factors such as on/off-ramp violations decrease truck parking utilization. In addition, parking utilization was found to vary considerably throughout the day.
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Acknowledgments
This research was partially funded by National Center for Freight and Infrastructure Research and Education (CFIRE) and USDOT, Tennessee Department of Transportation and Federal Highway Administration. The authors would like to thank ATRI for providing data and support during the research period. Computational facilities at Intermodal Freight Transportation Institute (IFTI) at University of Memphis are greatly acknowledged.
Disclaimer
Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the aforementioned agencies.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 143 • Issue 9 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: May 30, 2016
Accepted: Mar 17, 2017
Published online: Jun 29, 2017
Published in print: Sep 1, 2017
Discussion open until: Nov 29, 2017
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