用用户行为数据训练模型，快速预测拥堵路段的车流分布。
Learning from user's behaviour of some well-known congested traffic networks
- 基于用户行为数据构建学习型交通分配模型
- 在多个真实路网中实现比传统方法快10倍以上且精度接近均衡解
- 适合交通规划与智能导航系统开发者参考
交通分配问题（TAP）旨在预测车辆如何在道路网络中分布，传统方法需通过计算量大的迭代模拟达到用户均衡（UE），即无人能单方面缩短出行时间。近年来，机器学习技术，尤其是图神经网络（GNN）和混合方法，致力于以更快的速度实现高精度求解，减少对耗时仿真依赖。
原文摘要 · Abstract (English)
The traffic assignment problem (TAP) aims to predict how traffic flows distribute themselves across a road network, traditionally requiring computationally expensive iterative simulations to reach a user equilibrium (UE) where no driver can unilaterally reduce their travel time. Recent developments in machine learning (ML), particularly Graph Neural Networks (GNNs) and hybrid approaches, aim to solve this faster while maintaining accuracy
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