Application of the stochastic optimization method in optimizing traffic signal control settings.

275278552Final contract report;April 2007October 2008.PDFTech ReportVTRC 09-CR3Traffic signal timingTraffic signal control systemsOptimizationVirginiaVirginia Transportation Research CouncilPark, ByungkyuLee, JoyoungMcGhee, Catherine CVirginia Transportation Research CouncilUniversity of Virginia. Center for Transportation StudiesVirginia. Dept. of TransportationUnited States. Federal Highway AdministrationUS Transportation CollectionTraffic congestion has greatly affected not only the nation's economy and environment but also every citizen's quality of life. A recent study shows that every American traveler spent an extra 38 hours and 26 gallons of fuel per year due to traffic congestion during the peak period. Of this congestion, 10% is attributable to improper operations of traffic signals. Surprisingly, more than a half of all signalized intersections in the United States needs to be re-optimized immediately to maintain peak efficiency. Even though many traffic signal control systems have been upgraded from pre-timed controllers to actuated and adaptive controllers, the traffic signal optimization software has not been kept current. For example, existing commercial traffic signal timing optimization programs including SYNCHRO and TRANSYT-7F do not optimize advanced controller settings available in the modern traffic controllers including minimum green time, extension time, and detector settings. This is in part because existing programs are based on macroscopic simulation tools that do not explicitly consider individual vehicular movements. To overcome such a shortcoming, a stochastic optimization method (SOM) was proposed and successfully applied to a signalized corridor in Northern Virginia. This study presents enhancements made in the SOM and case study results from an arterial network consisting of 16 signalized intersections. The proposed method employs a distributed computing environment (DCE) for faster computation time and uses a shuffled frog-leaping algorithm (SFLA) for better optimization. The case study results showed that the proposed enhanced SOM method, called SFLASOM, improved the total network travel times over field settings by 3.5% for Mid-Day and 2.1% for PM-Peak. In addition, corridor travel times were improved by 2.3% to 17.9% over field settings. However, when the new SOM timing plan was compared to the new field timing plan implemented in July 2008, the improvements were marginal, showing slightly over 2% reductions in individual vehicular delay