Assessment of Truck Parking Demand and Safety During Normal and Severe Weather Conditions in Nebraska

Authors

• Nathan Huynh, University of Nebraska - LincolnFollow
• Li Zhao, University of Nebraska-LincolnFollow
• Zhenghong Tang, University of Nebraska - LincolnFollow
• Aida Riahifar, University of Nebraska - Lincoln
• Jahangeer Jahangeer, University of Nebraska - LincolnFollow

ORCID IDs

Huynh: 0000-0002-4605-5651

Zhao: 0000-0003-1914-1041

Tang: 0000-0001-7603-0394

Riahifar: 0000-0001-8478-1040

Jahangeer: 0000-0002-5070-1265

Date of this Version

1-2026

Document Type

Article

Citation

Hunch, N., Zhao, L., Tang, Z., Riahifar, A., and Jahangeer, J. (2026). Assessment of Truck Parking Demand and Safety During Normal and Severe Weather Conditions in Nebraska, NDOT Research Report  SPR-FY24(036).

Abstract

This project examined truck parking capacity, demand, and utilization patterns along I-80 in Nebraska. The objectives were to (1) document the capacity of public and private truck parking facilities along I-80, (2) analyze spatial and temporal trends in parking demand, (3) develop models to predict occupancy at parking facilities, (4) identify clusters of undesignated parking during inclement weather, and (5) assess whether truck parking shortages contribute to truck-involved crashes. Within a one-mile buffer of I-80, 21 public facilities and 49 private facilities were identified. The spatiotemporal analysis of these public and private truck parking facilities using the National Agriculture Imagery Program and Google Earth Imagery data between 2010 and 2022 showed a higher parking occupancy rate at private facilities, with several private facilities experiencing 100% occupancy or higher. Public facilities near Omaha had a consistently high occupancy rate. To enable NDOT to estimate the occupancy rate of a facility, public or private, expansion factors were first developed using the 2022 ATRI GPS data. The number of trucks parked at public and private facilities was then estimated by multiplying the number of GPS-identified trucks that remained stationary for more than 60 minutes by the corresponding expansion factor. Building on these estimates, a hybrid Bayesian modeling framework was developed to predict occupancy. Parking facilities were clustered based on their inter-facility distances, resulting in groups containing either single or multiple facilities. Accordingly, a dynamic time series model was developed for individual facilities, while a panel model with time fixed effects and distance-based spatial lags was developed for multi-facility clusters. Results indicate that lagged occupancy is the strongest predictor, with spatial effects also significant in the panel model. Both models achieved strong predictive performance. During inclement weather, trucks were found to park in four locations: facility entry and exit areas, off-road sites, ramps, and shoulders. Lastly, statistical analysis showed no significant relationship between truck-involved crashes and parking shortages.