This brief summarizes research findings from the University of California, Riverside, on a prediction-based, adaptive connected eco-driving strategy to account for real-world uncertainties.
Yosemite National Park attracts 4.5 million visitors a year, 60% of whom spend at least some time in Yosemite Valley where many of the park’s natural wonders are found. Bicycles have been a popular travel alternative within the valley. The purpose of this project is to explore the potential for bicycling to play a larger role in the effort to manage Yosemite Valley traffic and reduce environmental impacts.
The purpose of this research is to develop real-time algorithms to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers.
With rapid population growth and urban development, traffic congestion has become an inescapable issue, especially in large cities. Many congestion reduction strategies have been proposed in the past, ranging from roadway extension to transportation demand management programs. In particular, congestion pricing schemes have been used as negative reinforcements for traffic control. This project studies a different approach of offering positive incentives to drivers to take alternative routes.
Researchers at the University of Southern California developed a real-time, distributed algorithm for offering personalized incentives to individual drivers to make socially optimal routing decisions.
This project will investigate how connectivity provided by vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) technologies can be used to develop traffic flow control systems that will enhance mobility and safety, and reduce queues at ramps with positive benefits to transportation efficiency and environment.
