Abstract

With advances in computation and sensing, real-time adaptive control has become an increasingly attractive option for improving the operational efficiency at signalized intersections. The great advantage of adaptive signal controllers is that the cycle length, phase splits and even phase sequence can be changed to satisfy current traffic demand patterns to a maximum degree, not confined by preset limits. To some extent, traffic-actuated controllers are themselves “adaptive” in view of their ability to vary control outcomes in response to real-time vehicle registrations at loop detectors, but this adaptability is restricted by a set of predefined, fixed control parameters that are not adaptive to current conditions. To achieve the functionality of truly adaptive controllers, a set of online optimized phasing and timing parameters are needed. This dissertation proposes a real-time, on-line control algorithm that aims to maintain the adaptive functionality of actuated controllers while improving the performance of signalized networks under traffic-actuated control. To facilitate deployment of the control, this algorithm is developed based on the timing protocol of the standard NEMA eight-phase full-actuated dual-ring controller. In formulating the optimal control problem, a flow prediction model is developed to estimate future vehicle arrivals at the target intersection, the traffic condition at the target intersection is described as “over-saturated” throughout the timing process, i.e., in the sense that a multi-server queuing system is continually occupied, and the optimization objective is specified as the minimization of total cumulative vehicle queue as an equivalent to minimizing total intersection control delay. According to the implicit timing features of actuated control, a modified rolling horizon scheme is devised to optimize four basic control parameters—phase sequence, minimum green, unit extension and maximum green—based on the future flow estimations, and these optimized parameters serve as available signal timing data for further optimizations. This dynamically recursive optimization procedure properly reflects the functionality of truly adaptive controllers. Microscopic simulation is used to test and evaluate the proposed control algorithm in a calibrated network consisting of thirty-eight actuated signals. Simulation results indicate that the proposed algorithm has the potential to improve the performance of the signalized network under the condition of different traffic demand levels.