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Truck parking on freight corridors faces various challenges, such asinsufficient parking spaces and compliance with Hour-of-Service (HOS)regulations. These constraints often result in unauthorized parking practices,causing safety concerns. To enhance the safety of freight operations, providingaccurate parking usage prediction proves to be a cost-effective solution.Despite the existing research demonstrating satisfactory accuracy forpredicting individual truck parking site usage, few approaches have beenproposed for predicting usage with spatial dependencies of multiple truckparking sites. We present the Regional Temporal Graph Neural Network (RegT-GCN)as a predictive framework for assessing parking usage across the entire stateto provide better truck parking information and mitigate unauthorized parking.The framework leverages the topological structures of truck parking sitedistributions and historical parking data to predict occupancy rates across astate. To achieve this, we introduce a Regional Decomposition approach, whicheffectively captures the geographical characteristics. We also introduce thespatial module working efficiently with the temporal module. Evaluation resultsdemonstrate that the proposed model surpasses other baseline models, improvingthe performance by more than $20\%$ compared with the original model. Theproposed model allows truck parking sites' percipience of the topologicalstructures and provides higher performance.

Truck Parking Usage Prediction with Decomposed Graph Neural Networks