Evaluating the Digital Divide in Public Transport Services: A Natural Language Processing Analysis of User Experiences in Birmingham

Huafeng Lu, Lei Zhang, Silvia Gullino and Miguel Hincapié Triviño
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Lei Zhang: University Medical Center Hamburg-Eppendorf

No hmvgt_v1, SocArXiv from Center for Open Science

Abstract: Digitalisation has transformed public transport services worldwide, yet disparities in access, skills, and outcomes continue to reinforce a digital divide in how citizens engage with these services. While previous studies have examined the digital divide in internet use and smart technologies, few have explored how these inequalities manifest in everyday interactions with digital public transport platforms. To this end, we investigated user experiences of public transport applications in Birmingham, whose digital inclusion is disproportionate to its status as a major urban and economic hub in the UK, by analysing 4,275 user reviews on public transport services collected from the Apple App Store and Google Play Store between 2016 and 2025. Using a mixed natural language processing (NLP) approach combining topic modelling and sentiment analysis, we identified key themes and emotional tones in user feedback. Results revealed three dominant topics: App Performance, Usability, and Service Satisfaction, each corresponding to the access, ability, and motivation dimensions of the digital divide. Sentiment analysis showed generally negative evaluations, particularly around technical access barriers and satisfaction issues. By integrating user-generated data with digital divide theory, these results provide novel empirical evidence of how inequalities in digital engagement persist within digital twin planning and smart city transport systems, and highlight the importance of inclusive digital design for equitable and sustainable mobility access.

Date: 2025-12-15
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:hmvgt_v1

DOI: 10.31219/osf.io/hmvgt_v1

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