  Appropriate traffic regulations, e.g. planned road closure, are important in
congested events. Crowd simulators have been used to find appropriate
regulations by simulating multiple scenarios with different regulations.
However, this approach requires multiple simulation runs, which are
time-consuming. In this paper, we propose a method to learn a function that
outputs regulation effects given the current traffic situation as inputs. If
the function is learned using the training data of many simulation runs in
advance, we can obtain an appropriate regulation efficiently by bypassing
simulations for the current situation. We use the graph convolutional networks
for modeling the function, which enable us to find regulations even for unseen
areas. With the proposed method, we construct a graph for each area, where a
node represents a road, and an edge represents the road connection. By running
crowd simulations with various regulations on various areas, we generate
traffic situations and regulation effects. The graph convolutional networks are
trained to output the regulation effects given the graph with the traffic
situation information as inputs. With experiments using real-world road
networks and a crowd simulator, we demonstrate that the proposed method can
find a road to close that reduces the average time needed to reach the
destination.
