I am working on a regression problem, where the goal is to estimate historic traffic volumes throughout a transportation network. I have traffic counters at 100 locations, so a model can learn the relation between traffic volumes and a number of explanatory variables (e.g., speeds, road characteristics, weather). Afterwards, I can apply the model to estimate historic traffic volumes in places where I don't have traffic counters.
My neural network works reasonably well, but I am wondering if there are machine learning models that could explicitly account for the topology of my road network and the fact that traffic on neighboring road links is highly correlated. I could add "traffic volume at the closest traffic counter" as an input variable to my ANN, but I am wondering if there is a more intelligent approach.
In this regard, I came across Bayesian networks, which can account for the network topology and correlation. However, they seem applicable to cases when we have sensors at 100 locations and we want to predict the traffic state (at these 100 locations) at a future time point. On the other hand, I have measurements at 100 locations and are looking to estimate traffic at a different location for the same time point.
Any suggestion is much appreciated!