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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15291 |
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Table of Contents:
- 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.