用图神经网络预测外卖需求，兼顾空间邻近与时间变化。
Spatio-Temporal Demand Prediction for Food Delivery Using Attention-Driven Graph Neural Networks
- 将城市区域建模为图，用注意力机制动态捕捉相邻区域影响。
- 在真实数据集上预测准确率显著优于传统方法。
- 适合需要优化配送调度和资源分配的平台使用。
精准的需求预测对提升外卖平台效率至关重要，空间异质性和订单量的时间波动直接影响运营决策。本文提出一种基于注意力机制的图神经网络框架，将外卖环境建模为图结构：节点代表城市配送区域，边反映空间邻近关系及历史订单流动模式。注意力机制动态加权邻近区域的影响，使模型在预测时聚焦最相关的区域。时空依赖关系联合学习，使模型能适应不断变化的需求趋势。在真实外卖数据集上的大量实验表明，该模型在预测未来订单量方面具有高精度优势。该框架提供了一种可扩展、自适应的解决方案，支持城市外卖运营中的主动车队调度、资源分配与派单优化。
原文摘要 · Abstract (English)
Accurate demand forecasting is critical for enhancing the efficiency and responsiveness of food delivery platforms, where spatial heterogeneity and temporal fluctuations in order volumes directly influence operational decisions. This paper proposes an attention-based Graph Neural Network framework that captures spatial-temporal dependencies by modeling the food delivery environment as a graph. In this graph, nodes represent urban delivery zones, while edges reflect spatial proximity and inter-regional order flow patterns derived from historical data. The attention mechanism dynamically weighs the influence of neighboring zones, enabling the model to focus on the most contextually relevant areas during prediction. Temporal trends are jointly learned alongside spatial interactions, allowing the model to adapt to evolving demand patterns. Extensive experiments on real-world food delivery datasets demonstrate the superiority of the proposed model in forecasting future order volumes with high accuracy. The framework offers a scalable and adaptive solution to support proactive fleet positioning, resource allocation, and dispatch optimization in urban food delivery operations.
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