  Truck parking on freight corridors faces various challenges, such as
insufficient 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, providing
accurate parking usage prediction proves to be a cost-effective solution.
Despite the existing research demonstrating satisfactory accuracy for
predicting individual truck parking site usage, few approaches have been
proposed for predicting usage with spatial dependencies of multiple truck
parking sites. We present the Regional Temporal Graph Neural Network (RegT-GCN)
as a predictive framework for assessing parking usage across the entire state
to provide better truck parking information and mitigate unauthorized parking.
The framework leverages the topological structures of truck parking site
distributions and historical parking data to predict occupancy rates across a
state. To achieve this, we introduce a Regional Decomposition approach, which
effectively captures the geographical characteristics. We also introduce the
spatial module working efficiently with the temporal module. Evaluation results
demonstrate that the proposed model surpasses other baseline models, improving
the performance by more than $20\%$ compared with the original model. The
proposed model allows truck parking sites' percipience of the topological
structures and provides higher performance.
