Stochastic Optimization for Coordinated Actuated Traffic Signal Systems

Existing state-of-the-practice traffic signal timing-optimization programs rely on macroscopic and deterministic models to represent traffic flow, including coordinated actuated traffic signal systems. One distinct shortcoming of such an approach is its inability to account for the stochastic nature of traffic, such as the variability in traffic demand, driver behavior, vehicular interarrival times, vehicle mix, and so forth. In addition, the existing traffic signal timing-optimization programs for coordinated actuated traffic signal systems still focus on four basic traffic signal timing parameters (i.e., cycle length, green times or force-off points, offsets, and phase sequences). Studies have shown that actuated signal settings such as minimum green time, vehicle extension, and recall mode are also important parameters in traffic signal operations. This study presents the development of a stochastic-optimization method for coordinated actuated traffic signal systems. The proposed method accounts for stochastic variability by using a well-calibrated microscopic simulation model, CORSIM, instead of a macroscopic and deterministic model, and it simultaneously optimizes actuated signal settings and the four traffic signal timing parameters by adopting a genetic algorithm with special decoding schemes. The proposed method was applied to a real-world arterial network in Charlottesville, Virginia. The performance of the proposed method was compared with that of an existing traffic signal timing-optimization program, Synchro, using a well-calibrated microscopic simulation model, VISSIM. The results indicated that the proposed method outperforms the existing timing plan and the Synchro-optimized traffic signal timing for the tested arterial network.