Using AI to improve the accessibility and mobility of people living in rural areas: An intelligent demand-responsive service

Full Description

Improving rural accessibility, reducing car dependency, and tackling climate change have been central themes in transport research for decades.

Yet, private cars remain the dominant mode of travel in rural areas, largely due to the limited reliability and efficiency of available public transport.

Demand Responsive Transport (DRT) has long been presented as a flexible alternative to conventional services, but its potential has never been fully realised.

Low ridership and inconsistent performance often undermine the viability of such systems, raising critical questions about their role in addressing climate targets and supporting the Net Zero agenda.

This project seeks to evaluate the performance of existing DRT systems, with a particular focus on rural contexts where conventional public transport struggles to meet community needs.

The study will systematically identify operational gaps, governance challenges, and user-related barriers that affect the effectiveness of DRT.

By engaging with local authorities, operators, and users, the project will map out the strengths and weaknesses of current systems and develop a set of actionable recommendations.

In parallel, the research will explore how smart technologies can transform DRT into a more reliable, efficient, and user-centred service.

Innovations such as artificial intelligence, predictive analytics, and autonomous vehicles offer significant opportunities to optimise routes, reduce costs, and improve service reliability.

Human cognition and user behaviour will be placed at the centre of this exploration, ensuring that technological solutions align with the lived realities of rural residents.

Ultimately, the project aims to design a framework for a next-generation smart DRT model tailored to rural areas.

This framework will not only contribute to reducing car dependency but also support broader policy goals on equity, accessibility, and climate change mitigation.

Academic qualifications

First degree (minimum 2:1 classification) in Engineering, Computing, AI

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.

Essential attributes:

• Good fundamental knowledge of engineering

• Good analytical skills

• Proficient in English

Desirable attributes:

• AI knowledge

When applying, please quote the application reference “SCEBE1125” on your form.

APPLICATION CHECKLIST

• Completed application form

• CV

• 2 academic references, using the Postgraduate Educational Reference Form (download)

• Research project outline of 2 pages (list of references excluded).

The outline may provide details about

• Background and motivation of the project.

The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.

• Research questions or objectives.

• Methodology: types of data to be used, approach to data collection, and data analysis methods.

• List of references.

The outline must be created solely by the applicant.

Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.

• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• “SCEBE1125” as project code.

• the advertised title as project title

For informal enquiries about this PhD project, please contact W.Saleh@napier.ac.uk

PhD Start Date: October 2026

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?mP9MDnTs1Rwm8ftb3WVhDhXtraMQwXSUMdHC9wIc34es5bJqXf