Graph neural networks as strategic transport modelling alternative - A proof of concept for a surrogate

Practical applications of graph neural networks (GNNs) in transportation are still a niche field. There exists a significant overlap between the potential of GNNs and the issues in strategic transport modelling. However, it is not clear whether GNN surrogates can overcome (some of) the prevalent issues. Investigation of such a surrogate will show their advantages and the disadvantages, especially throwing light on their potential to replace complex transport modelling approaches in the future, such as the agent-based models. In this direction, as a pioneer work, this paper studies the plausibility of developing a GNN surrogate for the classical four-step approach, one of the established strategic transport modelling approaches. A formal definition of the surrogate is presented, and an augmented data generation procedure is introduced. The network of the Greater Munich metropolitan region is used for the necessary data generation. The experimental results show that GNNs have the potential to act as transport planning surrogates and the deeper GNNs perform better than their shallow counterparts. Nevertheless, as expected, they suffer performance degradation with an increase in network size. Future research should dive deeper into formulating new GNN approaches, which are able to generalize to arbitrary large networks.