Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks
This work addresses the computational bottleneck in traffic assignment for urban planning and transportation management, representing an incremental improvement over existing neural network methods.
The paper tackles the computationally challenging traffic assignment problem for multi-class vehicles by developing a heterogeneous graph neural network with multi-view attention mechanisms and flow conservation constraints, achieving superior convergence speed and predictive accuracy compared to traditional neural network approaches in urban transportation networks.
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.