Truck Parking Usage Prediction with Decomposed Graph Neural Networks

• URL: http://arxiv.org/abs/2401.12920v1
• Date: Tue, 23 Jan 2024 17:14:01 GMT
• Title: Truck Parking Usage Prediction with Decomposed Graph Neural Networks
• Authors: Rei Tamaru, Yang Cheng, Steven Parker, Ernie Perry, Bin Ran, Soyoung Ahn
• Abstract summary: We present the Regional Temporal Graph Neural Network (RegT-GCN) as a predictive framework for assessing parking usage across the entire state. The framework leverages the topological structures of truck parking site distributions and historical parking data to predict occupancy rates across a state. Evaluation results demonstrate that the proposed model surpasses other baseline models, improving the performance by more than $20%$ compared with the original model.
• Score: 16.20279799290392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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