Investigating Emission Reduction Benefit From Intersection Signal Optimization

Abstract

Most existing intersection signals are timed based on delay minimization. However, minimizing delay does not necessarily lead to the minimization of emissions at an intersection. No study has focused on the difference or the trade-off between delay based and emissions based signal optimization. Delay-based optimization typically uses macroscopic flow conditions such as traffic demands, saturation flow rates, and average delay. However, the latest emission model, MOVES (Motor Vehicle Emission Simulation), requires second-by-second individual vehicle speed profiles, which makes the model difficult to formulate directly in an emission-based signal optimization problem. This study first develops a methodology to derive vehicle profiles given macroscopic inputs so that MOVES can be applied to estimating emissions. Then an optimization methodology of signal timing is developed and solved with a genetic algorithm. The objective function of the optimization problem considers both delay and emissions, with the signal timing elements being the decision variables. Through a case study, the air quality benefit by reducing vehicle emissions through intersection signal control is demonstrated, and the trade-off between operational and emission performance measures is investigated. Furthermore, the air quality benefit from intersection signal control is discussed under different scenarios of cycle lengths, percentages of turning vehicles, and traffic demands on major/minor roads.

Keywords:

• Emissions
• MOVES
• Optimization
• Signal Timing

Acknowledgments

The authors acknowledge the National Science Foundation of China for supporting part of this study in the form of the Overseas Scholar Cooperation Research Program (project 51128802).