Artificial Intelligence for Autonomic Urban Traffic Control

More than half of the world's population now resides in cities, and global urbanisation continues at a steady pace. This trend exacerbates mobility issues, with the average UK citizen losing 115hrs annually to traffic congestion, with an estimated annual cost to the economy of more than £8bn. Furthermore, urban traffic poses a significant health threat and is a major contributor to greenhouse gases.

Currently deployed traffic control techniques are unable to cope with the increased traffic demand and the highly unpredictable post-pandemic traffic patterns, as they rely on simplified traffic models and operate in a purely reactive mode.

The use of Artificial Intelligence, coupled with the wide availability of data, large-scale interconnectivity, and emerging modes of transport such as Connected Autonomous Vehicles, can trigger a shift from reactive to proactive urban traffic control. By leveraging new and complementary AI approaches, the proposed line of research aims to design and develop an urban traffic management and control framework with a holistic view of the controlled region's conditions.

This framework will enable proactive actions to prevent environmental and mobility issues, while also supporting effective operations to mitigate observed problems.