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 PhD project aims to improve the way modern traffic control systems operate. The candidate will study macroscopic traffic models described by partial differential equations (PDEs). The distinctive feature of this PhD project is that it will use the real-world data collected by the Sheffield Urban Flows Observatory (https://urbanflows.ac.uk/). The list of tasks to be considered is as follows:
- Data processing. To process the raw traffic data from the Sheffield Urban Flows Observatory to eliminate inaccurate measurements and reconstruct the traffic density.
- Model comparison. To identify which model better describes the traffic data by comparing different versions of the Lighthill-Whitham-Richards and Aw-Rascle-Zhang models.
- Model identification. To identify model parameters guaranteeing better compliance of the processed data with the PDE model.
- Traffic reconstruction. To develop real-time observers reconstructing traffic behaviour using a limited number of measurements provided by road sensors.
- Fault detection. To develop and apply methods for automatic fault detection that identify sensors with inaccurate/wrong measurements.
- Traffic control and optimisation. To increase the capacity and safety of highways by eliminating such deteriorating phenomena as phantom traffic jams and stop-and-go behaviour. The controllers will act via speed limits, traffic lights, and Connected and Autonomous Vehicles (CAVs) present in the traffic.
If you would like to learn about the PhD project more or have any questions, please, feel free to contact the project supervisor, Dr Anton Selivanov, at [Email Address Removed].
- A. Ferrara, S. Sacone, and S. Siri, Freeway Traffic Modelling and Control. Springer, 2018.
- M. Treiber and A. Kesting, Traffic Flow Dynamics. Springer, 2013.
- M. Papageorgiou, “Some remarks on macroscopic traffic flow modelling,” Transportation Research Part A: Policy and Practice, vol. 32, no. 5, pp. 323–329, 1998.
- D. Helbing, “Traffic and related self-driven many-particle systems,” Reviews of Modern Physics, vol. 73, no. 4, pp. 1067–1141, 2001.
Strong mathematical background (in calculus, linear algebra, ODEs, and functional analysis) and familiarity with PDEs are essential. Experience in control theory is desirable but not mandatory – a mathematically literate candidate can quickly fulfil possible gaps. Most importantly, we are looking for candidates passionate about maths and fundamental research in general.
Applicants are required to hold a BSc/MSc degree in mathematics or engineering. If the degree is not from an English-speaking country, the applicant needs an overall IELTS grade of 6.5 with a minimum of 6.0 in each component (or equivalent). For further details, visit https://www.sheffield.ac.uk/postgraduate/phd/apply.
The University of Sheffield is a Russel Group university in the Top 100 World Universities according to the QS World University Rankings. It is located in the centre of the UK, right next to the Peak District National Park. The Department of Automatic Control and Systems Engineering (ACSE) is the only department in the UK dedicated to Control Engineering. The standard duration of a PhD in the UK is 3.5 years. To learn more about student life in Sheffield, visit https://www.sheffield.ac.uk/sheffield-guide.
Informal enquiries are encouraged and should be addressed to Dr Anton Selivanov at [Email Address Removed].
- You can apply for this project here: https://www.sheffield.ac.uk/postgradapplication/
- Suitable candidates will be invited for an online interview in December/January.
- Candidates who pass the interview will be invited to apply for the University of Sheffield PhD scholarship. The application deadline is 25 January 2023.
- Start date: Autumn Semester 2023.