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.
