Yoshitaka Kuwata, David M. Hart, Paul R. Cohen
When a traffic management system involves many thousands of vehicles using hundreds of streets and highways, it can be difficult or impossible to tell whether the network is flowing smoothly and to predict how modifications to dynamic control parameters will affect the system. For large-scale traffic management problems it is both necessary and difficult to assess the current state of the system and to predict the effects of modifications. In the absence of informed intervention, a system can evolve into a pathological state or process, in which vehicle progress slows or stops completely. We describe an interactive control system whose purpose is to help human controllers steer the evolving state of the network away from a pathology - avoiding the pathology if it has not yet materiAi~ zed, and disabling it quickly if it has.