Many urban areas suffer from traffic congestion. Intuitively, it may seem that a road expansion (e.g., the opening of a new road) should always improve the traffic conditions. However, in reality, a new road can actually worsen traffic congestion. It is therefore extremely important that before we start a road expansion project, we first predict the effect of this project on traffic congestion.
Traditional approach to this prediction is based on the assumption that for any time of the day, we know the exact amount of traffic that needs to go from each origin city zone A to every other destination city zone B (these values form an OD-matrix), and that we know the exact capacity of each road segment. Under this assumption, known efficient algorithms produce the equilibrium traffic flows.
In reality, the road capacity may unpredictably change due to weather conditions, accidents, etc. Drivers take this uncertainty into account when planning their trips: e.g., if a driver does not want to be late, he or she may follow a slower route but with a guaranteed arrival time instead of a (on average) faster but unpredictable one. We must therefore take this uncertainty into account in traffic simulations. In this paper, we describe algorithms that take this uncertainty into account.