Truck Parking Usage Prediction with Decomposed Graph Neural Networks

• URL: http://arxiv.org/abs/2401.12920v2
• Date: Mon, 12 Aug 2024 20:33:46 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: Truck parking on freight corridors faces the major challenge of insufficient parking spaces. It has been shown that providing accurate parking usage prediction can be a cost-effective solution to reduce unsafe parking practices. This paper presents the Regional Temporal Graph Neural Network (RegT-CN) to predict parking usage across the entire state.
• Score: 15.291200515217513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Truck parking on freight corridors faces the major challenge of insufficient parking spaces. This is exacerbated by the Hour-of-Service (HOS) regulations, which often result in unauthorized parking practices, causing safety concerns. It has been shown that providing accurate parking usage prediction can be a cost-effective solution to reduce unsafe parking practices. In light of this, existing studies have developed various methods to predict the usage of a truck parking site and have demonstrated satisfactory accuracy. However, these studies focus on a single parking site, and few approaches have been proposed to predict the usage of multiple truck parking sites considering spatio-temporal dependencies, due to the lack of data. This paper aims to fill this gap and presents the Regional Temporal Graph Neural Network (RegT-GCN) to predict parking usage across the entire state to provide more comprehensive truck parking information. The framework leverages the topological structures of truck parking site locations and historical parking data to predict the occupancy rate considering spatio-temporal dependencies across a state. To achieve this, we introduce a Regional Decomposition approach, which effectively captures the geographical characteristics of the truck parking locations and their spatial correlations. Evaluation results demonstrate that the proposed model outperforms other baseline models, improving performance by more than 20%.

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