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Recent work in decentralized, schedule-driven traffic control hasdemonstrated the ability to significantly improve traffic flow efficiency incomplex urban road networks. However, in situations where vehicle volumesincrease to the point that the physical capacity of a road network reaches orexceeds saturation, it has been observed that the effectiveness of aschedule-driven approach begins to degrade, leading to progressively highernetwork congestion. In essence, the traffic control problem becomes less of ascheduling problem and more of a queue management problem in this circumstance.In this paper we propose a composite approach to real-time traffic control thatuses sensed information on queue lengths to influence scheduling decisions andgracefully shift the signal control strategy to queue management in highvolume/high congestion settings. Specifically, queue-length information is usedto establish weights for the sensed vehicle clusters that must be scheduledthrough a given intersection at any point, and hence bias the wait timeminimization calculation. To compute these weights, we develop a model in whichsuccessive movement phases are viewed as different states of an Ising model,and parameters quantify strength of interactions. To ensure scalability, queueinformation is only exchanged between direct neighbors and the asynchronousnature of local intersection scheduling is preserved. We demonstrate thepotential of the approach through microscopic traffic simulation of areal-world road network, showing a 60% reduction in average wait times over thebaseline schedule-driven approach in heavy traffic scenarios. We also reportinitial field test results, which show the ability to reduce queues duringheavy traffic periods.

Coping with Large Traffic Volumes in Schedule-Driven Traffic Signal Control