PhD Studentship - Explainable Deep Sequential Network for Flexible Airport Passenger Flow Estimation

Updated: 2 months ago
Location: Cranfield, ENGLAND
Job Type: FullTime
Deadline: 30 Sep 2022

Application deadline: 30/09/2022

Start date - 30/01/23

Fee status of eligible applicants: Any Fee Status (Uk/EU & RoW)

Duration of Award:  3 years

1st Supervisor: Dr Yang Xing   2nd Supervisor: Dr Dimitrios Panagiotakopoulos

This is an exciting opportunity for a fully funded PhD studentship in the Centre for Autonomous and Cyber-Physical Systems at Cranfield University, to develop explainable deep sequential networks for flexible airport passenger flow estimation. This PhD investigates deep time-series models and their explainability for multi-range passenger flow estimation. This research is sponsored by EPSRC and SAAB UK under the Doctoral Training Partnership Funding 2021/22.           

Accurately determining Airport passenger Flow (APF) is critical to efficiently manage airport processes and allocate resources and is fundamental to the provision of a seamless passenger journey experience. Precise APF estimation enables efficient resources assignment and planning for airport processes and functions, which will become essential to accommodate the dramatic increase that is expected in international air traffic in the next decade. However, due to the nature of air traffic, in which uncertainty and latency is a common feature, the temporal characteristic of APF has high volatility and irregularity properties, which make the APF very difficult to estimate and forecast. Therefore, an advanced framework for accurate APF estimation that considers the prediction explanation and multiple temporal behaviours is needed to optimise airport processes and resources, which in turn will enable a better integration with air traffic management processes and will result into a better passenger experience.

Currently, the estimation for APF have several limitations. One major limitation is the lack of explainable ability for the APF estimation model. An explainable network will allow the design of a transparency network that helps to clearly identify reasons behind the bottlenecks in airport operations and resources that relate to passenger flow, by providing a traceable inference process that enables effective decision-making to optimise operations. Another limitation is the lack of decoding for the relationship between long-term and short-term APF patterns as the long-term behaviour of APF. Hence, flexible APF estimation considering both long-term and short-term behaviour with sufficient explanation of the network prediction with be studied in this project.

Entry requirements

Applicants must have a first or second-class UK honours degree or equivalent in a related disciplines such as computer science, mathematics, aerospace engineering, and data science area. This project would suit someone with a strong background in machine learning (both theoretical studies and application), and autonomous systems, and hands-on approach to systems integration and out of the box thinking ability.

The candidate should be self-motivated and have excellent analytical, reporting, and communication skills as well.

Funding

This studentship opportunity is open to applicants in the UK, EU and International. There are no restrictions on nationality. The PhD is fully funded for UK applicants and partially funded for EU and International applicants.

Sponsored by EPSRC, Cranfield University, and SAAB UK, this studentship will provide a bursary of up to £18,000 (tax free) plus fees at UKRI level for three years.

How to apply  

For further information please contact:

Name: Dr Yang Xing
Email:
yang.x@cranfield.ac.uk

If you are eligible to apply for this studentship, please complete the online application form .

https://www.cranfield.ac.uk/research/phd/explainable-deep-sequential-network-for-flexible-airport-passenger-flow-estimation/?utm_channel=&utm_source=warwickuniversity&utm_medium=listing&utm_campaign=SATM_Aero_AK_PhD_SATM293_jobs_Jun22


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