Abstract
THIS study investigated the impacts of traffic signal optimization on energy (i.e., vehicular fuel consumption) and greenhouse gas emission (i.e., Carbon dioxide) at an urban corridor. The proposed approach successfully integrated a microscopic traffic simulator, an emission and fuel consumption estimation model, and a genetic algorithm (GA) optimizer. An urban corridor consisted of four traffic signalized intersections in Charlottesville, VA was selected for a case study. Traffic signal timing plans were optimized for a sustainability measure (i.e., network-wide fuel consumption) instead of traditional delay and stops measures. Study results showed noticeable reduction in fuel consumption (20%) and CO 2 emissions (20%) when compared to those optimized by a conventional Synchro program.
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