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

TL;DR

This paper introduces an attention-based graph neural network that models spatial and temporal dependencies to accurately forecast food delivery demand, improving operational efficiency.

Contribution

It presents a novel attention-driven GNN framework that captures dynamic spatial-temporal demand patterns in food delivery environments.

Findings

Outperforms existing demand prediction models in accuracy

Effectively captures spatial heterogeneity and temporal fluctuations

Supports scalable and adaptive demand forecasting

Abstract

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.