用均衡机制增强神经网络，大幅提升交通流预测精度。
WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning
- 融合经典神经网络与均衡层，通过参数化延迟函数预测交通均衡。
- 在静态场景下预测误差降低72%，动态场景下降低23%。
- 适合交通规划、智能导航等需要高精度流量预测的场景。
优化交通系统的核心在于预测交通流量，但计算交通均衡仍具高计算成本。为此，我们提出WardropNet——一种结合组合优化与神经网络的新架构，可实现快速准确的交通流预测。该模型由传统神经层和后续均衡层构成：前者预测均衡问题中延迟函数的参数，后者基于这些参数求解交通均衡。通过监督学习最小化实际流量与预测输出的差异，并采用适配均衡几何结构的Bregman散度，实现端到端训练。在真实与简化交通场景中，WardropNet显著优于纯学习方法：在静态预测中平均提升达72%，动态预测中平均提升23%。
原文摘要 · Abstract (English)
When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this background, we introduce a novel combinatorial optimization augmented neural network architecture that allows for fast and accurate traffic flow predictions. We propose WardropNet, a neural network that combines classical layers with a subsequent equilibrium layer: the first ones inform the latter by predicting the parameterization of the equilibrium problem's latency functions. Using supervised learning we minimize the difference between the actual traffic flow and the predicted output. We show how to leverage a Bregman divergence fitting the geometry of the equilibria, which allows for end-to-end learning. WardropNet outperforms pure learning-based approaches in predicting traffic equilibria for realistic and stylized traffic scenarios. On realistic scenarios, WardropNet improves on average for time-invariant predictions by up to 72% and for time-variant predictions by up to 23% over pure learning-based approaches.
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