In the industrial world, digital twins are commonly used to explore how changes may unfold when delivered in practice. The benefits of the approach are that you can examine a wide range of different approaches in simulated form and therefore save money on practically building each alternative.
A recent project from Imperial College London aims to produce a similar outcome for the deployment of self-driving fleets and their impact on urban mobility. The simulations aim to provide insight into things such as emissions, congestion, and public transport.
Various scenarios
The researchers analyzed tens of thousands of scenarios, all of which were fed with real-world data. The aim was to understand how effective and efficient such fleets could be and the impact on other factors of urban mobility.
A report has been produced showcasing the data from the simulations while also providing things such as an autonomous vehicle build manual, a data infrastructure framework, and driver safety guidelines.
“The deployment of autonomous vehicle technologies has the potential to revolutionize mobility in cities around the world,” the researchers say. “Through the SHIFT project, we had an opportunity to study their potential impacts on the rest of the transport network in an unprecedented level of detail.”
“Using the tools that we developed, stakeholders can now plan better for the deployment of autonomous vehicle technologies and be better prepared for the future.”
Mobility data
The simulations were based on data that was harvested from real-world travel patterns and road usage, with decades worth of research on passenger behavior used to understand how passengers respond to changes in things such as price and travel times.
The simulations show that autonomous vehicles are likely to boost congestion and waste energy as they’re capable of operating non-stop without breaks. This could result in a lot of “empty miles” completed. The researchers developed algorithms designed to help reduce this possibility and generally optimize the fleet so that it operates only where demand is present.
“Proper management of autonomous vehicle fleets is essential to minimizing energy consumption and environmental impacts,” they explain. “We want to take advantage of the potential to increase passenger occupancy and avoid vehicles cruising around looking for passengers, thereby reducing vehicle kilometers traveled.”
Because of the digital nature of the simulations, the researchers believe it’s an approach that could be easily applied to various other transport problems, whether it’s the deployment of other autonomous technologies or even the electrification of public transport.
“The motivation for deploying large fleets of AVs in cities is to reduce individual car ownership, freeing up space on the roads,” they say. “For the public to choose this option, the services provided by such fleets must be reasonably priced and to accommodate their needs, responding to demand.”
“To do this, the vehicles should be managed as efficiently as possible, with their deployment aligned with user demand while having minimal impact on factors like congestion and emissions. Our model will help fleet operators meet these conditions, providing multiple benefits for city dwellers.”
The researchers now plan to examine how autonomous vehicle deployments could be made safer as part of a project to develop the first virtual driving test for autonomous vehicles.
