用多视图图神经网络解决多类型车辆交通分配问题。
Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks
- 构建多视图异构图注意力网络，区分不同车类特征。
- 在真实城市路网中实现更快收敛与更高预测精度。
- 适合交通规划与智能交通系统研究者参考。
在大规模交通网络中，传统基于优化的方法求解交通分配问题计算成本高昂。本文针对多类型车辆场景，提出一种创新的代理模型，采用异构图神经网络结合多视图图注意力机制，针对不同车辆类别设计特定结构，并增加起点-终点之间的额外连接。同时，将基于节点的流量守恒定律融入损失函数。实验结果表明，该模型在保持流量守恒的前提下，显著提升了路段流量与利用率预测的准确性。在真实城市交通网络上的数值实验显示，本方法在用户均衡和系统最优两种设定下，均优于传统神经网络方法，在收敛速度与预测精度上表现更优。
原文摘要 · Abstract (English)
Solving traffic assignment problem for large networks is computationally challenging when conventional optimization-based methods are used. In our research, we develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved. We do so by employing heterogeneous graph neural networks which use a multiple-view graph attention mechanism tailored to different vehicle classes, along with additional links connecting origin-destination pairs. We also integrate the node-based flow conservation law into the loss function. As a result, our model adheres to flow conservation while delivering highly accurate predictions for link flows and utilization ratios. Through numerical experiments conducted on urban transportation networks, we demonstrate that our model surpasses traditional neural network approaches in convergence speed and predictive accuracy in both user equilibrium and system optimal versions of traffic assignment.
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