Project code



School of Sport, Health and Exercise Science

Start dates

October 2021

Closing date

4 May 2021 (12.00pm GMT)

Applications are invited for a fully-funded three year PhD to commence in October 2021.

The PhD will be based in the School of Sport, Health and Exercise Science and will be supervised by Dr Martina Navarro, Dr Peter Howell and Professor Djamila Ouelhadj.

Candidates applying for this project may be eligible to compete for a Portsmouth Global PhD scholarship. Successful candidates will receive a scholarship to cover tuition fees at an international rate for three years, a stipend in line with the UKRI rate (£15,609 for 2021/22), and one return flight to London during the duration of the course. Bursary recipients will also receive  £1,500 for project costs/consumables for the duration of the programme.

The work on this project will

  • Examine VSS that allow cyclists to safely approach and cross road junctions.
  • Investigate whether a training programme that orients riders’ VSS to where and when to look at when deciding to cross a road is effective.
  • Investigate the effects of cycling speed, path width and cycling experience on VSS and performance of cyclists.

Most road accidents involving cyclists occur at road junctions, caused by several factors including behaviour of another road user, cyclist distraction and narrow visual focus, and narrow bicycle path2.Therefore, the ability to anticipate other road user behaviours and to decide whether (or not) to cross a road is crucial to prevent accidents. During cycling, performers scan two regions, the “near” region for stability and/or online control, and the “far” region for hazard perception. Allocating visual attention in the near region increases with greater immediate task complexity (e.g. cycling along a narrow path) and decreases with improvements in a performer’s skill level (e.g. experienced cyclists).Visual attentional demands towards the far region increases with greater travel speed and situation unpredictability. In this case, an increase in the demands of both online control (e.g. narrow paths)and situational information (e.g. unpredictable driver behaviour) increases attentional demand, which is associated with greater probability of accident. A cyclist’s ability to predict other road users’ behaviour has been reported as a protective skill against fatal traffic accident involvement, especially needed when approaching and crossing road junctions. The ability to judge whether to cross a road and/or brake to avoid collisions and obstacles is greatly influenced by individual differences in visual scanning strategies (VSS, when and where cyclists look in the environment).Scanning is related to task complexity, expertise and environmental features, such as bicycle speed or cycle path characteristics, depending on levels of road traffic intensity3. Research in human performance has reported benefits from training programmes to improve VSS7. Therefore, identification of cyclists’ VSS associated with safe/successful road crossings could inform the design of training packages to increase cyclists’ road skills and safety. Additionally, a better comprehension of the intrinsic relationship between performer perception-action and environmental constraints when crossing roads in cycling can offer valuable information to city planners, by helping to prevent traffic accidents. 

Entry requirements

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 an appropriate subject. 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.

How to apply

We’d encourage you to contact Dr Martina Navarro ( 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. 

If you want to be considered for this funded PhD opportunity you must quote project code SHES6161021 when applying.