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Spatio-Temporal Demand Prediction for Food Delivery Using Attention-Driven Graph Neural Networks

Created by
  • Haebom

Author

Rabia Latief Bhat, Iqra Altaf Gillani

Outline

This paper emphasizes the importance of accurate demand forecasting for improving the efficiency and responsiveness of food delivery platforms, and proposes a demand forecasting model that considers spatial heterogeneity and temporal variation. We capture spatial-temporal dependencies by using a graph neural network (GNN) framework with urban delivery areas as nodes and spatial proximity and order flow patterns between areas as edges. Through an attention mechanism, we dynamically weight the influence of adjacent areas to focus on the most relevant areas during forecasting, and learn temporal trends and spatial interactions together to adapt to changing demand patterns. We verify the high accuracy of the proposed model through experiments on a real food delivery dataset, and show that the framework is a scalable and adaptive solution that supports proactive vehicle deployment, resource allocation, and dispatch optimization.

Takeaways, Limitations

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Takeaways:
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Empirically demonstrating the superiority of a spatial-temporal demand forecasting model using graph neural networks.
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Improved prediction accuracy and increased computational efficiency through attention mechanisms.
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Presenting practical solutions that contribute to improving the operational efficiency of food delivery platforms.
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Providing a new approach to proactive resource management and optimization.
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Limitations:
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Lack of clear description of the nature and scope of the dataset used.
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Further research is needed on the model's generalization performance and applicability to other types of delivery services.
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Lack of consideration of the cost and complexity of implementing and operating models in real-world delivery environments.
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Lack or inadequate consideration of factors that affect forecast accuracy (e.g. weather, events).
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