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Connected vehicle technology aims to improve travel times

04 April 2015

ORNL researchers are working to develop algorithms that facilitate vehicle-to-vehicle communication, as well as communication between vehicles and traffic controls.

Visualisation of an ORNL connected vehicles simulation using decentralised control algorithms (image: Andreas Malikopoulos)

Scientists with the Urban Dynamics Institute (UDI) at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) are working to reduce travel time and fuel consumption by developing a computational framework for connected vehicle technologies.

These would facilitate vehicle-to-vehicle communication, as well as communication between vehicles and traffic controls. Vehicles would exchange information such as location, speed, and destination in order to generate individualised instructions for drivers. 

“By telling drivers the optimal speed, the best lane to drive in, or the best route to take, we can eliminate stop-and-go driving and improve safety,” says UDI deputy director, Andreas Malikopoulos, the project's principal investigator.

“As a driver, you may get additional instructions suggesting you change lanes or follow a different path that may not be the route your GPS would give you to avoid congestion.”

The first step for the project team is developing decentralised control algorithms that govern how vehicles will communicate locally among vehicles interacting directly on the road but also act globally to optimise traffic flow across a city.

The computational framework uses 'decentralised control' algorithms because, realistically, all the vehicles in a city cannot communicate information to a central control centre due to the staggering amount of data that would be involved.

“The first phase is an exploratory project. We’ll validate our framework through simulation,” Malikopoulos says.

The second phase of the project will connect the team’s communication framework with a transportation analysis simulation system that uses data analytics to simulate traffic conditions in real urban areas to predict congestion.

Simulations will predict and plan traffic flow based on large-scale data, such as the layout and population distribution of the area that reflects driver activities (for example, school zones are likely to be busier early in the morning and mid-afternoon, whereas entertainment districts are likely to be more congested during evenings and weekends), as well as the destinations and schedules of the connected vehicles in the simulation.

Phase two simulations also will allow the team to begin exploring questions related to cyber security and possible incentives for drivers to follow connected vehicle instructions, such as digital ticketing.


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