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Developing the next generation of pedestrian behaviour models for revival of high streets and sustainable transport


Cardiff School of Computer Science & Informatics

Dr Crispin Cooper , Dr Christine Mumford Wednesday, June 30, 2021 Competition Funded PhD Project (Students Worldwide)

About the Project

Modelling the behaviour of pedestrians in city centres is of practical importance not only for sustainable transport (reducing reliance on motor vehicles addresses issues of carbon emissions, congestion and health) but also can help inform planning for town centres in a post-COVID world. High Street retail has long been in decline relative to out-of-town and online shopping; the pandemic has served to accelerate this process and the question of what a successful town centre will look like in 2030 remains open.

Our own previous research has included the first longitudinal strategic predictions of pedestrian flow [1], optimization of bus routes [2] and a classification of UK town centre types based on pedestrian sensor data [3].

We are interested in supporting a variety of projects within this space, including for example

·      Methods for calibration of existing pedestrian simulations software such as www.cardiff.ac.uk/sdna to the diverse array of sensor, census and mobile phone data now available to determine which environments are more attractive for walking and when people will choose to walk

·      Methods for inclusion of more accurate built environment data into pedestrian models, e.g. by applying image recognition to freely available street view data

We have strong links with several transport consultancies and the Government High Streets Task Force to ensure models developed are both well aligned with the needs of city planners, and any results are likely to have real world impact.

Contact for information on the project:

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

Application Information: If you would like to be considered for the School Funded Application, please submit your application before the 30th June 2021. 

In the funding field of your application, insert “I am applying for 2021 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.

Apply online: https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics - Please read the "How to apply" instructions carefully prior to application. 


Funding Notes

A School-Funded PhD Scholarship is available for entry 2021/22. If you are interested in applying for a PhD Scholarship, please follow the instructions available on our website: View Website
In the Funding field of your application, insert "I am applying for 2021 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
This project is also open to Self-Funded students worldwide.

References

[1] C. H. V. Cooper, I. Harvey, S. Orford, and A. J. F. Chiaradia, ‘Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows’, Transportation, Dec. 2019, doi: 10.1007/s11116-019-10072-0.
[2] L. Ahmed, P. Heyken-Soares, C. Mumford, and Y. Mao, ‘Optimising bus routes with fixed terminal nodes: comparing hyper-heuristics with NSGAII on realistic transportation networks’, in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, Jul. 2019, pp. 1102–1110, doi: 10.1145/3321707.3321867.
[3] C. Mumford, C. Parker, N. Ntounis, and E. Dargan, ‘Footfall signatures and volumes: Towards a classification of UK centres’, Environment and Planning B: Urban Analytics and City Science, p. 2399808320911412, Mar. 2020, doi: 10.1177/2399808320911412.

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