Message Passing Neural Networks for Traffic Forecasting
Arian Prabowo Hao Xue Wei Shao Piotr Koniusz Flora D. Salim
A road network, in the context of traffic forecasting, is typically modeled
as a graph where the nodes are sensors that measure traffic metrics (such as
speed) at that location. Traffic forecasting is interesting because it is
complex as the future speed of a road is dependent on a number of different
factors. Therefore, to properly forecast traffic, we need a model that is
capable of capturing all these different factors. A factor that is missing from
the existing works is the node interactions factor. Existing works fail to
capture the inter-node interactions because none are using the message-passing
flavor of GNN, which is the one best suited to capture the node interactions
This paper presents a plausible scenario in road traffic where node
interactions are important and argued that the most appropriate GNN flavor to
capture node interactions is message-passing. Results from real-world data show
the superiority of the message-passing flavor for traffic forecasting. An
additional experiment using synthetic data shows that the message-passing
flavor can capture inter-node interaction better than other flavors.