Highway Network Digital Twin for Traffic Management

Public description
Inefficiencies in road networks reduce capacity and increase journey times for users and goods. Road capacity is not used evenly over space or time: usage peaks during morning and evening rush hours lead to inefficiencies; incidents reduce capacities unexpectedly. The ability to forecast traffic patterns in real-time, linked to traffic control systems, can improve network efficiency.

Computer-based traffic models that calculate routes that individuals take allow us to replicate real-world conditions across a traffic network for a single point in time. However, these models are not well-suited to responding to unforeseen incidents, which require rapid operational decision-making to mitigate their impacts.

Traffic conditions at specific points on the network are increasingly being captured by roadside sensors that monitor traffic volumes, vehicle emissions and other information in real-time. These data are very valuable, and many are routinely used for traffic management; however, they tell us only about conditions at specific points in space and time, and not conditions across the entire network.

Techniques for processing and identifying patterns in such data, using Artificial Intelligence techniques like Machine Learning are improving, and offer opportunities to apply these data in ever more complex real-time applications.

Traffic management can be significantly improved if there is a reliable, accurate representation of the whole of the existing road network and the real-world conditions it is operating under -- a 'digital twin'. Such a digital twin becomes a powerful forecasting tool when the routes that individuals take (or should take) are accounted for.

Our proposal is to build a real-time digital twin based on existing traffic models, this growing array of roadside sensors, and state-of-the-art Machine Learning techniques in order to be able to model traffic conditions on networks in real-time.

The digital twin will allow traffic to be actively managed, increasing road capacity and facilitating smoother traffic flow, particularly during "rush hour" and during unanticipated incidents. A digital twin could, for example, be used to predict the effects on traffic flow (and therefore capacity) of changing signs or opening/closing lanes before implementing such measures. In order to develop and operate a real-time digital twin to manage and increase road capacity, we must first demonstrate that we can automatically integrate real-time sensor data, a road network representation and traffic model. We will assess the feasibility of this level of integration along with the practicality of running a real-time solution in terms of calculation speed.