Simulation-based optimization of maximum green setting under retrospective approximation framework

Pengfei Li, Montasir M. Abbas, Raghu Pasupathy, Larry Head

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalTransportation Research Record
Issue number2192
StatePublished - Dec 1 2010

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


Dive into the research topics of 'Simulation-based optimization of maximum green setting under retrospective approximation framework'. Together they form a unique fingerprint.

Cite this