Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability

2024-10-05

arXiv.org

AI Summary

To address low prediction accuracy and poor inference efficiency in urban parking demand forecasting amid rapidly increasing vehicle populations, this paper proposes a dynamic parking graph prediction framework grounded in real-time service capability modeling. Methodologically, it introduces a novel formulation of user parking preferences based on real-time service accessibility; designs a joint graph coarsening and temporal autoencoding module for dimensionality reduction—preserving both structural integrity and spatiotemporal feature fidelity while compressing graph scale; and integrates graph attention mechanisms with spatiotemporal graph convolutional networks, augmented by a pretrained encoder-decoder restoration module to enhance generalization. Evaluated on a large-scale real-world dataset from Shenzhen, the framework achieves a 46.8% improvement in prediction accuracy and a 30.5% speedup in inference latency, with performance gains becoming more pronounced as graph scale increases.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

With the sharp increase in the number of vehicles, the issue of parking difficulties has emerged as an urgent challenge that many cities need to address promptly. In the task of predicting large-scale urban parking data, existing research often lacks effective deep learning models and strategies. To tackle this challenge, this paper proposes an innovative framework for predicting large-scale urban parking graphs leveraging real-time service capabilities, aimed at improving the accuracy and efficiency of parking predictions. Specifically, we introduce a graph attention mechanism that assesses the real-time service capabilities of parking lots to construct a dynamic parking graph that accurately reflects real preferences in parking behavior. To effectively handle large-scale parking data, this study combines graph coarsening techniques with temporal convolutional autoencoders to achieve unified dimension reduction of the complex urban parking graph structure and features. Subsequently, we use a spatio-temporal graph convolutional model to make predictions based on the coarsened graph, and a pre-trained autoencoder-decoder module restores the predicted results to their original data dimensions, completing the task. Our methodology has been rigorously tested on a real dataset from parking lots in Shenzhen. The experimental results indicate that compared to traditional parking prediction models, our framework achieves improvements of 46.8% and 30.5% in accuracy and efficiency, respectively. Remarkably, with the expansion of the graph's scale, our framework's advantages become even more apparent, showcasing its substantial potential for solving complex urban parking dilemmas in practical scenarios.

Problem

• Urban Parking Prediction
• Accuracy and Efficiency
• Large-scale Data Processing

Innovation

• Real-time Information Integration
• Temporal Convolutional Autoencoder
• Spatiotemporal Graph Convolutional Model