Connected vehicles that receive future state intersection signal control data to support predictive cruise control functions can reduce fuel consumption 24 to 47 percent.
Using simulation case studies to estimate the impacts of predictive cruise control.
Made Public Date
02/09/2016

766

Pleasantburg Drive
Greenville
South Carolina
United States
Identifier
2015-01033
TwitterLinkedInFacebook

Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time

Summary Information

This study evaluated the potential of connected vehicles to predict future states of intersection signal control logic to optimize intersection approach speeds and improve fuel economy and trip time while maintaining safe headways between vehicles. The performance of a predictive cruise control (PCC) algorithm was evaluated using a simulation model to emulate 10 intersections having decentralized fixed signal phase and timing (SPaT) control in Greenville, South Carolina.

Simulations were run with and without PCC functions. Performance was evaluated for scenarios with single and multiple connected vehicles operating on the network.

FINDINGS
  • In a suburban driving scenario, predictive cruise control (PCC) equipped vehicles used 47 percent less fuel and generated 56 percent fewer CO2 emissions compared to baseline conditions without PCC. Travel times were similar even though the PCC vehicle traveled a longer distance.
  • In a city driving scenario PCC vehicles used 24 to 29 percent less fuel compared to the baseline.

Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time

Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time
Publication Sort Date
05/01/2011
Author
Asadi, Behrang and Ardalan Vahidi
Publisher
IEEE Transactions on Control Systems Technology
Other Reference Number
Vol. 19, No. 3