Simulation-Based Robust Optimization for Signal Timing and Setting

摘要

The performance of signal timing plans obtained from traditional approaches for pre-timed (fixed-time or actuated) control systems is often unstable under fluctuating traffic conditions. This report develops a general approach for optimizing the timing of pre-timed signals along arterials under day-to-day demand variations or uncertain traffic future growth. Based on a cell-transmission representation of traffic dynamics, a stochastic programming model is formulated to determine cycle length, green splits, phase sequences and offsets to minimize the expected delay incurred by high-consequence scenarios of traffic demand. The stochastic programming model is simple in structure but contains a large number of binary variables. Existing algorithms, such as branch and bound, are not able to solve it efficiently, particularly when the optimization horizon is long and the network size is large. Consequently, a simulation-based genetic algorithm is developed to solve the model. The model and algorithm are validated and verified in two networks. It is demonstrated that the resulting robust timing plans perform better against high-consequence scenarios without losing optimality in the average sense. More specifically, the plans reduce substantially the mean excess delay across the high-consequence scenarios without compromising the average delay across all scenarios under both congested and uncongested traffic conditions.