Mobility and Environmental Benefits Were Assessed by Testing Prototype Automated Vehicle Flow Smoothing Controllers Using a Previously Calibrated Microscopic Simulation Model of the I-210 in California.
Los Angeles, California, United States
Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions
Summary Information
The incorporation of emerging technologies such as automated vehicles (AVs) can effectively reduce greenhouse gas (GHG) emissions by encouraging mode shift or improving vehicle efficiency. Flow smoothing (a technique that uses a type of adaptive cruise control) is a method that can be implemented to smooth stop-and-go traffic waves and reduce emissions.
A simulation testbed using traffic data from 2018-2019 representing a half mile segment of the I-210 highway in Los Angeles was developed to assess the impacts of a prototype flow smoothing control system. One objective was to assess the effects of deploying AVs at various market penetration rates (10, 20, 30 percent) on the average miles per gallon (MPG) of non-AVs in the traffic stream. The system was designed to preserve a pre-tuned target speed to avoid large gaps between vehicles. When a gap opened, the headway of the front vehicle was recognized by the controller to reduce wave instability by keeping all vehicles at a constant speed.
METHODOLOGY
A calibrated microsimulation model of I-210 was used to simulate approximately five minutes of traffic flow on the highway. Initially, the simulation was started with free-flowing traffic, then traffic was observed with flow smoothing-controlled vehicles inserted at various penetration rates. Vehicle models estimated fuel economy embedded within the simulation software. These models calculate the average MPG for simulated vehicles. Specifically, the energy consumption of electric vehicles was converted to an effective MPG by applying the National Resources Canada method and using the 2018 California electricity profile provided by the Energy Information Administration (EIA). The percentage of each vehicle model represented was determined by the 2018 registration data from the California Department of Motor Vehicles (DMV) and the Federal Highway Administration (FHWA), and 2019 sales data from the California New Car Dealers Association (CNCDA). Seven vehicle classes were used in the simulation, including three types of passenger vehicle classes, average light duty vehicle, average heavy-duty vehicle, passenger alternative fuel vehicle, and zero emission vehicle.
FINDINGS
- The average fuel economy achieved by the simulated human drivers increased from 19 MPG to 36 MPG, when the proportion of flow smoothing AVs increased from zero to 30 percent.
- The simulation results revealed a 50 percent increase in average speeds, when flow smoothing AV penetration rate increased from zero to 30 percent. This finding indicated a possible increase in vehicle miles traveled (VMT) by at most 30 percent.
- The average trip time in the simulations conducted decreased by 25 percent as the AV penetration rate increased from 0 to 30 percent.
- Among all of the vehicle types investigated, passenger alternative fuel vehicles and non-electric passenger vehicles improved the most in fuel economy, by about two times when flow smoothing AV penetration rate increased from zero to 30 percent. Electric vehicles, on the other hand, exhibited a less effect when implementing flow smoothing (about 1.2 times) which was likely owing to their regenerative braking that already resulted in significant energy savings.
