用图神经网络快速预测交通流量均衡，提升大规模规划效率。
Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
- 基于消息传递网络构建交通分配元模型，模仿传统模拟器结构。
- 在未训练过的输入数据上仍能准确预测流量分布，泛化能力强。
- 适合需要实时决策的大规模交通规划与仿真场景。
交通分配问题是交通建模中的基础性任务，但在大规模网络中计算成本高昂。传统方法依赖迭代仿真达到均衡状态，难以实现实时或大规模情景分析。本文提出一种基于消息传递神经网络的基于学习的方法，作为随机用户均衡分配的元模型，近似其均衡流量。该模型设计模仿传统交通模拟器的算法结构，更贴近实际过程而非仅拟合数据。我们在多个基准上对比了其他深度学习方法，并通过测试模型在训练域外输入上的流量预测能力来评估其鲁棒性。该方法为加速分布外情景评估、降低大规模交通规划的计算成本、实现实时决策提供了有前景的解决方案。
原文摘要 · Abstract (English)
The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks. Traditional methods require iterative simulations to reach equilibrium, making real-time or large-scale scenario analysis challenging. In this paper, we propose a learning-based approach using Message-Passing Neural Networks as a metamodel to approximate the equilibrium flow of the Stochastic User Equilibrium assignment. Our model is designed to mimic the algorithmic structure used in conventional traffic simulators allowing it to better capture the underlying process rather than just the data. We benchmark it against other conventional deep learning techniques and evaluate the model's robustness by testing its ability to predict traffic flows on input data outside the domain on which it was trained. This approach offers a promising solution for accelerating out-of-distribution scenario assessments, reducing computational costs in large-scale transportation planning, and enabling real-time decision-making.
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