DISTRIBUTED APPROACH TO OPTIMIZED CONTROL OF STREET TRAFFIC SIGNALS

The paper describes our long-term activity aimed at the control of traffic signals by a network of distributed processors situated at street intersections. Every processor runs an identical expert system and communicates directly with the four adjacent processors. (However, each expert system may need a somewhat different knowledge base to correspond to the geometry and the average traffic pattern of the associated intersection). Messages can reach all indefinitely distant processors, modulated by the needs of intervening ones. The information transmitted can be raw data, processing information, or expert advice. The rule base of the expert systems has a natural segmentation, corresponding to different prevailing traffic patterns and the respective control strategies. Multidimensional learning programs optimize both the hierarchy of the rules and the parameters embedded in individual rules. Different measures of effectiveness can be selected as the criterion for optimization. Traffic scenarios are automatically generated for a "characteristic period"-for a certain part of the day (e.g., morning rush hours), a certain type of day in the week (e.g., regular work day), and a certain season of the year (e.g., vacation time). The result of our first implementation, a running prototype simulation program, has proven the feasibility and utility of the approach.