Resilient Intelligent Traffic Systems with Learning Optimisation and Chance Constraints

This project develops learning-driven and optimisation-based methods to keep cities moving under disruption, with a core focus on chance-constrained optimisation for risk-aware decision making. You will build calibrated digital twins of urban corridors and highways and co-design prediction, control, and contingency policies for signals, routing, depots, and EV charging. Methods may include graph neural networks for network-state estimation, multiagent reinforcement learning for cooperative control, Bayesian and causal tools for uncertainty handling, evolutionary and quality-diversity search to generate robust policy portfolios, and risk-sensitive formulations (violation probabilities, quantile/CVaR objectives, distributionally robust models) to guarantee service levels under stochastic demand, incidents, and weather. 94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021

The standard University IELTS English language requirement is 6.5 overall with 6.0 in each individual element (reading, writing, listening and speaking).

International students: £23,100

UK students: £5,238

July 2026, October 2026, February 2027

1 September 2026

• Loughborough, United Kingdom