Evidence Triangulation for Public Transport Innovation: A Mixed-Methods Study on Real- Time Information Systems
DOI:
https://doi.org/10.22399/ijcesen.3071Keywords:
Public transport, Digital systems, User satisfaction, Safety, accessibility, Mixed methodsAbstract
This study examines the effects of real-time public transport information systems using a mixed-methods approach. Combining 14 expert interviews and over 10,000 user surveys, it applies evidence triangulation to assess how digital innovation influences service perception and policy relevance. Experts rate real-time data as a high-impact governance tool for enhancing trust, accessibility, and planning reliability—especially for users with limited mobility or orientation. Quantitative analysis confirms this view: access to digital information is significantly associated with higher user satisfaction, particularly among occasional or unfamiliar riders. The study highlights transparency, predictability, and perceived control as key acceptance factors. It proposes a transferable framework for evaluating innovations in transport through the integration of expert judgment and user feedback. Findings underscore the importance of user-centered indicators and call for adaptive monitoring tools to guide investment in digital infrastructure. Real-time systems emerge as a critical component of inclusive and responsive transport strategies.
References
[1] Ajzen, I. (1991). Organizational Behavior and Human Decision Processes, The theory of planned behavior 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
[2] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
[3] Mayring, P. (2015). Qualitative Inhaltsanalyse: Grundlagen und Techniken (12. Aufl.). Weinheim: Beltz.
[4] Winer, R. S. (1986). A reference price model of brand choice for frequently purchased products. Journal of Consumer Research, 13(2), 250–256. https://doi.org/10.1086/209063
[5] Flick, U. (2011). Triangulation: Eine Einführung. 3. Aufl., Wiesbaden: VS Verlag für Sozialwissenschaften.
[6] Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.
[7] Sochor, J., Arby, H., Karlsson, M., & Sarasini, S. (2021). A top-down and bottom-up approach to sustainable mobility policy integration: A case study from Sweden. Sustainability, 13(2), 479. https://doi.org/10.3390/su13020479
[8] Fröhlich, P., Neidhardt, J., & Kunc, T. (2022). Accessible multimodal mobility: A user perspective from smart public transport systems. International Journal of Human-Computer Interaction, 38(9), 831–847.
[9] Pritchard, J. P., Szell, M., & Helbing, D. (2022). The role of digital innovation in multimodal transport systems: A behavioral science perspective. Transportation Research Part A: Policy and Practice, 157, 225–239. https://doi.org/10.1016/j.tra.2021.12.007
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
