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Intelligent traffic control is an important issue of the modern transportation system. However, in large-scale urban transportation systems, traditional centralized coordination methods suffer bottlenecks in both communication and computation. Decentralized control is hard if there is very limited observation to the whole network as evidences to support joint traffic coordination decisions. In this paper, we proposed a novel decentralized, multiagent based approach for massive traffic lights coordination to promote the large-scale green transportation. Considering that only the traffic from the adjacent intersections may affect the state of a given intersection one time ahead, the key of our approach is using the observations of a local intersection and its neighbors as evidences to support the traffic light coordination decisions. Therefore, we can model the interactions as decentralized agents coordinating with a decision theoretical model. Within a local intersection, constraint optimizing agents are designed to efficiently search for joint activities of the lights. Since this approach involves only local intersection cooperation, it is well scalable and easily implemented with small communication overhead. In the last section, we present our software design on this approach and based on our simulation, this approach is feasible to a large urban transportation system.

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Green-Waved traffic control is one of the most efficient strategies in allowing continuous traffic from major directions flow over multiple intersections to improve urban transportation efficiency. When the number of traffic lights scales up, traditional centralized control suffers a bottleneck in both communication and computation.Decentralized control is potentially inefficient when local traffic lights only gain very limited observations to the whole network. This paper proposes a decentralized, multi-agent based schema to adaptively control massive traffic lights, which promotes the effects of green-wave. The key is that agents use the prospection of local state one time ahead as evidence to support decisions. Noting that only the traffic from the adjacent intersections affect the next state of a given intersection, the study models the interactions as decentralized agents to cooperatively coordinate each intersection by using decision theoretical models. This paper presents the algorithm and simulation results to prove the feasibility of the approach to massive urban transportation system.