Public transport challenges and technology-assisted accessibility for visually impaired elderly residents in urban environments

TL;DR

This study explores how AI and real-time data can improve public transport accessibility for visually impaired elderly residents in Edinburgh, addressing existing challenges through data analysis and user interviews.

Contribution

It combines spatial analysis and qualitative interviews to assess transport accessibility issues and explores AI-based solutions for visually impaired elderly users.

Findings

Transport system is highly centralised in Edinburgh.

Participants rely on memory-based navigation.

Lack of accessible real-time travel information.

Abstract

Independent navigation is a core aspect of maintaining social participation and individual health for vulnerable populations. While historic cities such as Edinburgh, as the capital of Scotland, often feature well-established public transport systems, urban accessibility challenges remain and are exacerbated by a complex landscape, especially for groups with multiple vulnerabilities such as the blind elderly. With limited research examining how real-time data feeds and developments in artificial intelligence can enhance navigation aids, we address this gap through a mixed-methods approach. Our work combines statistical and machine learning techniques, with a focus on spatial analysis to investigate network coverage, service patterns, and density through live Transport for Edinburgh data, with a qualitative thematic analysis of semi-structured interviews with the mentioned target group. The results demonstrate the highly centralised nature of the city's transport system, the significance of memory-based navigation, and the lack of travel information in usable formats. We also find that participants already use navigation technology to varying degrees and express a willingness to adopt artificial intelligence. Our analysis highlights the importance of dynamic tools in terms of sensory and cognitive needs to meaningfully improve independent travel.