Activity planning in the context of emerging modes of transport (EPSRC DTP)


Contact Professor Stephane Hess ( / Co-supervisor: Dr Chiara Calastri ( to discuss this project further informally.

Project description

The demand for travel is believed to be derived, that is to say people travel in order to perform activities or interact with people. For this reason, activity planning and time allocation represents an important area of travel behaviour research.

A class of mathematical models widely known as “choice models” have been used to analyse how people schedule their activities and how they allocate their time, as these behavioural processes involve decisions by “agents”.

Advanced time allocation choice models allow to incorporate the effect of a wide range of factors (Calastri et al.,2017) and can be paired with other tools such as activity based models, which make use of microsimulation (e.g. Vuk et al.,2016).

The advent of new forms of shared mobility and the upcoming diffusion of autonomous vehicles will likely change the way people go about their daily lives. By changing the way they travel, their activity schedule and consequently their time allocation will change.

Understanding and forecasting the effects of these changes on time use is of crucial importance to allow policy makers to make informed decisions on the provision of public transport and infrastructures. For example, if people can work or rest comfortably during their commute on autonomous vehicles, they might be willing to accept a longer commute and to move further away from their workplace, with important consequences for the need for road infrastructures and urban sprawl.

This PhD project will focus on developing new choice models as well as investigating the suitability of existing modelling tools to address the new behavioural challenges represented by the emerging modes of transport entering the market, including shared mobility and autonomous vehicles.

The student will first test current models and subsequently modify them to develop new model structures to correctly represent and forecast market changes.

References Calastri, C., Hess, S., Daly, A., & Carrasco, J. A. (2017). Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data. Transportation Research Part A: Policy and Practice, 104, 1-20. Vuk, G., Bowman, J. L., Daly, A., & Hess, S. (2016). Impact of family in-home quality time on person travel demand. Transportation, 43(4), 705-724.

Entry requirements

You must have achieved a bachelor degree with a 2:1 (hons) or equivalent, or a good performance in a Masters level course preferably in a quantitative discipline. We also recognise relevant industrial and academic experience.

Desired skills:

  • Strong numerical aptitude
  • Some experience in computer programming
  • Interest in choice and behavior modelling

If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.

How to apply

Formal applications for research degree study should be made online through the university's website.

If you require any further information, please contact the Graduate School Office e:, or t: +44 (0)113 343 35326.

We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.