Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions
Research Team: Alexandre Bayen (lead), Sulaiman Almatrudi, Kanaad Parvate, Daniel Rothchild, Upadhi Vijay, and Kathy Jang
UC Campus(es): UC Berkeley, UC Davis
Problem Statement: Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and then speed back up again; this repeated braking and accelerating leads to higher fuel consumption, and correspondingly increasingly GHG emissions. Flow smoothing, or the use of a specially designed adaptive cruise controllers to dissipate these waves, can reduce fuel consumption of all the cars on the road. By keeping all vehicles at a constant speed, flow smoothing can minimize system-wide GHG emissions.
Project Description: This project presents the results of flow-smoothing when used in simulation, discusses current work on implementing flow-smoothing in real world-highways, and presents policy discussions on how to support flow smoothing.