defining-the-future-of-urban-road-traffic-control | Engineering

Brief description

Engineers from Southampton’s historic Transportation Research Group (TRG) are tapping into the latest machine learning techniques to create an intelligent model for major cities’ evolving and growing traffic needs.

Explore this case study

The challenge

Most of the existing algorithms used worldwide to control traffic lights were initially developed in the 1970s when limited real time data was available and the focus was simply on maximising the number of vehicles that could pass through a junction.
The world has changed since then with a much greater focus on making things better for all users of the transport system, including cyclists, pedestrians and public transport.
But to make traffic systems that could transform the performance of road networks in the world’s busiest cities requires development of the next generation of control systems based on Artificial Intelligence (AI).

What we do

This ground-breaking research funded by Siemens Mobility is part of a long running partnership dating back to the creation of the first urban traffic control centres around 30 years ago.
Within this research we are seeking to take advantage of increasing amounts of real-time data on objects’ movement including pedestrians and cyclists, as well and motorised vehicles.
Through matching real-time sensor data flows to underlying theories of movement it becomes possible to improve predictions of how locations of urban road users evolve over time.
These predictions can then form the basis of new algorithms that estimate and learn from the likely consequences of different control approaches.

Our impact

The approaches being developed within this research are aimed to form a core component of the next generation of urban traffic control systems worldwide.
They are currently in the process of being incorporated into the Sitraffic FUSION suite to help urban authorities deliver more efficient, safer and greener road systems for all road users around the world.