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Group activity and travel scheduling with shared autonomous vehicles - Project ID SEBE0023


   School of Engineering and the Built Environment (SEBE)

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  Dr A Fonzone, Dr G Fountas  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Although we do not know yet what mobility patterns will look like with fully automated vehicles, what we do know is that the future will be different from the present. By now it is clear that fully automated vehicles will worsen traffic conditions unless they are shared. Full automation makes sharing somehow easier: for instance, an autonomous vehicle may take a wife to work before returning to take the husband to the gym. But the husband could also decide to take a bus for the first part of his journey and then meet the autonomous vehicle half way. Or he may change the time of the gym to travel with his wife. An interesting and complex problem, which becomes even more complex if larger groups of people share the same vehicle, or if one considers that different activities have difference importance and that the duration of a journey is somehow random.

Depending on their skills and interests, the student will study the behaviour underpinning the scheduling problem, or develop an approach to solve it. A successful project may lead the way to the development of an app to plan group activities and journeys with shared autonomous vehicles.

Qualified applicants are encouraged to contact the supervisors informally to discuss the application. The position will remain open until filled.

Academic qualifications
A first degree (at least a 2.1) ideally in Transport/Civil Engineering, Operational Research, Computer Science, Geography, Cognitive Sciences (other degrees will be considered if the applicant can show the relevance to the project) with a good fundamental knowledge of transport planning and/or travel behaviour and/or optimisation and/or machine learning and/or data analysis.

English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s Policy are available online. https://www.napier.ac.uk/research-and-innovation/research-degrees/application-process

Essential attributes:
• Experience of fundamentals of transport planning and/or travel behaviour and/or optimisation and/or machine learning and/or data analytics
• Competent in Matlab and/or Python and/or Java and/or C++ and/or SAS/SPSS/R
• Knowledge of decision making models
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:
• Experience with research



Funding Notes

No funding available at the moment: self funded students only.

References

Ho, C., & Mulley, C. (2015) Intra-household interactions in transport research: a review. Transport Reviews 35 (1) p.33-55

Hyland, M. F., & Mahmassani, H.S. (2017) Taxonomy of shared autonomous vehicle fleet management problems to inform future transportation mobility. Transportation Research Record: Journal of the Transportation Research Board 2653 pp. 26-34

Sperling, D. (2018) Three Revolutions: Steering Automated, Shared, and Electric Vehicles to a Better Future. Island Press: Washington DC
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