Preliminary Eco-Signal Timing modeling results on the El Camino Real network in California.
Palo Alto, California, United States
Preliminary Eco-Traffic Signal Timing Modeling Results
Summary Information
The AERIS Eco-Traffic Signal Timing application is envisioned to be similar to current traffic signal systems; however the application’s objective is to optimize the performance of traffic signals for the environment. The application collects data from vehicles, such as vehicle location, speed, and emissions data using connected vehicle technologies. It then processes these data to develop signal timing strategies focused on reducing fuel consumption and overall emissions at the intersection, along a corridor, or for a region. The application evaluates traffic and environmental parameters at each intersection in real-time and adapts so the traffic network is optimized using available green time to serve the actual traffic demands while minimizing the environmental impact.
Preliminary simulation and modeling was conducted for this application using a 6 mile segment of El Camino Real in Northern California. The corridor contains 27 signalized intersections operating actuated coordinated signal timing plans; however for the purposes of this analysis, the baseline conditions assumed fixed timing plans. The modeling team used a genetic algorithm to optimize the traffic signal timing plans for the corridor with the objective of reducing fuel consumption and emissions. The genetic algorithm determined an optimal cycle length for the corridor, green times for each phase, and signal offsets for each signalized intersection. Phase sequences were not changed. To determine the optimal timing plans, outputs from the Paramics microsimulation model were sent to an API that interfaced with the Environmental Protection Agency’s MOtor Vehicle Emissions Simulator (MOVES) model. Traffic and emissions outputs from Paramics and MOVES, respectively, were then sent to the genetic algorithm which developed new timing plans. These new timing plans were then send back to Paramics and the process continued for numerous iterations until the genetic algorithm determined an optimal timing plan that reduced CO2 emissions for the entire corridor. Sensitivity analysis included varying the following parameters: penetration rate of connected vehicles, congestion levels, percentage of trucks, and optimizing for emissions versus delay. The method used to determine optimized timing plans for this study considered an offline optimization approach. More advanced connected vehicle applications and algorithms may perform the optimization online, similar to adaptive signal control systems but leveraging connected vehicle data and technologies.
FINDINGS
- There is a up to 5 percent improvement in fuel consumption and environmental measures at full connected vehicle penetration, while a 1 percent to 4 percent at partial connected vehicle penetration in a fully coordinated network.
- Optimizing for the environment resulted in a 5 percent fuel consumption reduction, whereas optimizing for mobility resulted in 2 percent reductions in fuel consumption.
- Driving a typical vehicle 8,000 miles per year on arterials equates to $70 of saving per year per vehicle.
- SUV (lower MPG) savings are $110 per year per driver.
- A fleet operator with 150 vehicles would save $16,500 per year.
