Improving passenger's experience using tactical and real-time train scheduling and routing algorithms

   School of Mathematics and Physics

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  Dr Banafsheh Khosravi  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Mathematics and Physics and will be supervised by Dr Banafsheh Khosravi.

The work on this project could involve:

  • Investigating generic railway scheduling and routing algorithms to improve passenger experience
  • Extending existing tactical and real-time algorithms by looking at train connections, delay management, and train cancellations to improve reliability for passengers
  • Develop a decision support system to integrate railway scheduling and routing algorithms in tactical and real-time levels

Project description

With greenhouse gas emissions from transport representing about 21 percent of the UK domestic emissions, usage of low carbon transport has never been of higher priority. Rail travel has low carbon footprint, but concerns surrounding overcrowding, rising ticket prices and tardiness can severely discourage travellers from using the service. Railway traffic management concerns the efficient use of the railway network and involves decisions relating to train times, order and route in both tactical and real-time levels. Such decisions are made to ensure a predefined service level and are subject to a number of operational and safety constraints. Scheduling and routing algorithms are two classes of techniques that can provide solutions for such complex problems that can be used to improve passenger experience through reductions in delay times, cancellations, and missed connections.

Railway planning is a complex process which conforms to a strategic/tactical/operational classification. The strategic level is about resource acquisition. The tactical level concerns resource allocation. The role of real-time management is to monitor the planned timetable and respond to the disruptions to get back to the original plan. Scheduling and routing algorithms in tactical and real-time levels can minimise total delays, alongside decreasing cancellations and missed connections.

The aim of this study is to develop generic algorithms to improve the efficiency of railway traffic management in tactical and real-time levels. Firstly, we extend existing railway scheduling and routing algorithms to model more realistic constraints in tactical level. Furthermore, the usage of current infrastructure, specifically junctions, is investigated to support real-time traffic management. This allows junction use for disruptions management and recovering to the predetermined timetable as efficiently as possible. Finally, through development of a decision support system, we can integrate train scheduling and routing to make more robust optimal decisions, in comparison to separate suboptimal solutions.

General admissions criteria

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in Operational Research, or Computer Science, or Mathematics, or Engineering. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

You should have a strong quantitative background. Having computer programming experience is desired.

How to Apply

We’d encourage you to contact Dr Banafsheh Khosravi ([Email Address Removed]) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

Please quote project code SMAP5990521 when applying.

Mathematics (25)

Funding Notes

Self-funded PhD students only.
Please for tuition fee information and discounts.
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