Simulation-Based Optimization of Maximum Green Setting under Retrospective Approximation Framework

Most traffic signal systems work under highly dynamic traffic conditions, and they can be studied adequately only through simulation. As a result, how to optimize traffic signal system parameters in a stochastic framework has become increasingly important. Retrospective approximation (RA) represents the latest theoretical development in stochastic simulation. Under the RA framework, the solution to a simulation-based optimization problem can be approached with a sequence of approximate optimization problems. Each of these problems has a specific sample size and is solved to a specific error tolerance. This research applied the RA concept to the optimal design of the maximum green setting of the multidetector green extension system. It also designed a variant of the Markov monotonic search algorithm that can accommodate the requirements of the RA framework, namely, the inheritable Markov monotonic search algorithm, and implemented the RA-based optimization engine within VISSIM. The results show that the optimized maximum green can considerably increase composite performance (reducing delay and increasing safety) compared with traditional designs. The optimization methodology presented in this paper can easily be expanded to other signal parameters.