Control Method Research For Area Traffic Signal Control Based On Temporal Planning

Traffic is very close to people's life. In most of large and secondary cities, the number of vehicles is increasing rapidly with the stepwise enhancement of social economy and the continuous development of automotive industry. Therefore, more and more traffic congestion has happened. It is an urgent problem to study how to alleviate traffic jams and improve the efficiency of transportation system. Traditionally, physical expansion of traffic network is a main means to solve the congestion and jams. But this method will cause expensive cost and destroy urban environment, furthermore, the effect is not very obvious. The other way to alleviate traffic jams is to enhance the efficiency of traffic facilities and implement rational traffic control strategies. With the incessant improvement of Artificial Intelligence and advanced control theory, more and more AI method has been placed on traffic control domain. Signal control at intersections is a primary component of traffic management and it plays an important role in the whole urban traffic. Thus, it possesses very important significance for urban traffic management to develop an accurate and effective signal control system. As an important branch of AI, AI planning theory has been developed rapidly. It provides a new way of solving traffic signal control problem. Existing AI planning system already have new breakthrough in both problem describing and problem solving, so the AI planning system can deal with problems with temporal constraint and resource constraint. Hence, there is great significance to combine AI planning theory with practical problems. In this paper, we combined temporal planning theory with area traffic control problem. A domain model based on temporal planning and resource planning has been established. It can process traffic control problems which associated with temporal constraint and resource constraint. Generating planning solution by the temporal planning system, obtained control strategy, proved the correctness and the effectiveness of the planning model.