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Macroscopic traffic control and optimisation


   Department of Automatic Control and Systems Engineering

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  Dr A Selivanov  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Traffic flows are the bloodstreams of a developed country. A failing transportation system impedes economic growth and deteriorates human lives. In particular, traffic congestion increases commute times, CO2 emissions, and the incidence of car accidents. These drastically decrease the quality of life and well-being of a population.

This project aims to improve the way modern traffic control systems operate. Namely, it will strive to increase the capacity and safety of highways by eliminating such deteriorating phenomena as phantom traffic jams and stop-and-go behaviour. To this end, we will study traffic models inspired by fluid mechanics, such as the Lighthill-Whitham-Richards and Aw-Rascle-Zhang models. The main research tools are Lyapunov functionals and linear matrix inequalities.

One of the features of this project is the use of Connected and Autonomous Vehicles (CAVs). These vehicles provide in-flow sensing and actuating capabilities that are yet to be fully exploited. For example, CAVs can affect the flow speed at different points and eliminate phantom traffic jams. Moreover, communications between CAVs allow for decentralised control, which is more flexible and robust than the centralised one.


Funding Notes

This is a self-funded research project. We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution. Full details of how to apply can be found at the following link:
https://www.sheffield.ac.uk/acse/research-degrees/applyphd
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: https://www.sheffield.ac.uk/postgraduate/phd/scholarships
Prospective candidates should have a strong background in maths and control theory, should be familiar with partial differential equations, and should be proficient in MATLAB.
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