Model-based traffic control

Abstract Urban traffic control is a difficult problem because of the complex interdependence of control decisions. Known techniques for achieving global control are based on parameter optimization techniques or heuristic expert systems. Optimization fails in severely congested traffic situations which require a change in global strategy. Heuristic expert systems require knowledge of all possible traffic situations, which is difficult to obtain, especially when construction and incidents cause frequent changes to the traffic network. In this paper, we show how the techniques of model-based diagnosis can be used to select coordinated control plans for networked systems of this kind Suitable local control strategies are those whose underlying assumptions are consistent with other control strategies, the state of the road network, and traffic flow. We describe a system which uses an assumption-based truth maintenance system (ATMS) to compute suitable strategies. The system has been tested both on synthetic examples and on simulations using actual data, and results are encouraging.