Exploring user perceptions at public transport stops in a SEM approach to Accessibility and Safety
DOI:
https://doi.org/10.56294/dm2025841Keywords:
Perceived safety, Accessibility, Structural Equation Modelling, Bus stops, Immersive stimuli, Urban transportAbstract
Bus stops in consolidated urban areas of the Global South often feature minimal infrastructure, compromising accessibility and safety. This study explored user perceptions of accessibility and safety at public transport stops in Quito, Ecuador, addressing gaps in subjective assessment methodologies. Using immersive audiovisual stimuli (360° videos and spatial audio), 16 real-world bus stop scenarios were replicated. A sample of 529 bus users including university students/staff personal evaluated six perceptual indicators of accessibility and safety indicators via digital surveys on tablets with noise-cancelling headphones. Structural Equation Modelling analysed relationships between latent constructs and sociodemographic and residential location variables. The results revealed that accessibility negatively influenced safety perceptions. Strong loadings for internal security and theft protection. Easy access outweighed stop size for accessibility perception. Users in living in south of Quito reported higher safety, while northern residents perceived lower safety. Group travel increased safety perceptions, and higher user volumes improved accessibility. The inverse accessibility-safety relationship highlights design trade-offs in high-density areas. Location-based heterogeneity (e.g., south Quito’s higher safety) underscores contextual influences. Immersive methods effectively captured perceptual complexity, but future research should expand to representative samples and integrate additional latent variables. Policy interventions require modular infrastructure adaptable to urban density gradients.
References
1. Yoh A, Iseki H, Smart M, Taylor BD. Hate to wait: Effects of wait time on public transit travelers’ perceptions. Transp Res Rec. 2011, 116–24. Available from: https://doi.org/10.3141/2216-13 DOI: https://doi.org/10.3141/2216-13
2. Lättman K, Friman M, Olsson LE. Perceived accessibility of public transport as a potential indicator of social inclusion. Soc Incl. 2016, 4(3): 36–45. Available from://doi.org/10.17645/si.v4i3.481 DOI: https://doi.org/10.17645/si.v4i3.481
3. Hansen WG. How Accessibility Shapes Land Use. J Am Plan Assoc. 1959, 25(2):73–6 DOI: https://doi.org/10.1080/01944365908978307
4. Lynch K. The Image of The City. Vol. 11. Cambridge: The MIT Press, 1959. 194 p. Available from: https://mitpress.mit.edu/books/image-city
5. Engwicht, D. Reclaiming our cities and towns: better living with less traffic. Philadelphia: New Society Publishers, 1992. 192 p.
6. Grava, S. Urban Transportation Systems. Choices for Communities, McGraw-Hill: New York, NY, 2003, pp. 1–12.
7. Han Y, Li W, Wei S, Zhang T. Research on Passenger’s travel mode choice behaviour waiting at bus station based on SEM-logit integration Model. Sustain. 2018, 10(6). DOI: https://doi.org/10.3390/su10061996
8. Ding J, Zhang Y, Li L. Accessibility measure of bus transit networks. IET Intell Transp Syst. 2018,12(7):682–688. DOI: https://doi.org/10.1049/iet-its.2017.0286
9. Loukaitou-Sideris A. Fear and safety in transit environments from the women’s perspective. Secur J. 2014, 27(2):242–56. Available from: http://dx.doi.org/10.1057/sj.2014.9 DOI: https://doi.org/10.1057/sj.2014.9
10. Vanier, C., de Jubainville, H. Feeling unsafe in public transportation: A profile analysis of female users in the Parisian region. Crime Prev. Community Saf. 2017, 19, 251–263. DOI: https://doi.org/10.1057/s41300-017-0030-7
11. Yavuz, N., Welch, E.W. Addressing fear of crime in public space: Gender differences in reaction to safety measures in train transit. Urban Stud. 2010, 47, 2491–2515. DOI: https://doi.org/10.1177/0042098009359033
12. Friman, M., Lättman, K., Olsson, L.E. Public Transport Quality, Safety, and Perceived Accessibility. Sustainability 2020, 12, 3563. https://doi.org/10.3390/su12093563 DOI: https://doi.org/10.3390/su12093563
13. Corazza, M.V., Musso, A., Karlsson, M.A. More accessible bus stops: Results from the 3iBS research project. In Transport Infrastructure and Systems, Dell’Acqua, G., Wegman, F., Eds., CRC Press/Taylor & Francis Group: London, UK, 2017, pp. 641–650. DOI: https://doi.org/10.1201/9781315281896-83
14. Municipio de Quito. Plan Maestro de Movilidad Sostenible del Distrito Metropolitano de Quito. Cal y Mayor Asociados. 2022, 71 p. Available from: https://gobiernoabierto.quito.gob.ec/wp-content/uploads/2023/05/PMMS-Quito_2022_2042.pdf
15. Karlsson, M.A., Corazza, M.V., Musso, A. Understanding barriers to the diffusion of innovations for accessible and safe bus systems. In Proceedings of the 14th International Conference on Mobility and Transport for Elderly and Disabled Persons—Transed 2015, Lisbon, 2015, pp. 211–226.
16. Banister, D. Unsustainable Transport, Routledge: London, UK, 2005, pp. 124–128. DOI: https://doi.org/10.4324/9780203003886
17. Cervero, R., Guerra, E., Al, S. Beyond mobility: planning cities for people and places, Island Press: Washington, DC, USA, 2017, pp. 109–117. DOI: https://doi.org/10.5822/978-1-61091-835-0_7
18. Hong JY, Jeon JY. Exploring spatial relationships among soundscape variables in urban areas: a spatial statistical modelling approach. Landsc Urban Plan. 2017, 157:352–64. Available from: http://dx.doi.org/10.1016/j.landurbplan.2016.08.006 DOI: https://doi.org/10.1016/j.landurbplan.2016.08.006
19. Hong JY, Lam B, Ong Z ting, Ooi K, Gan W, Kang J, et al. Effects of contexts in urban residential areas on the pleasantness and appropriateness of natural sounds. Sustain Cities Soc. 2020, 63. Available from: https://doi.org/10.1016/j.scs.2020.102475 DOI: https://doi.org/10.1016/j.scs.2020.102475
20. Garzón L, Bravo-Moncayo L, Arellana J, Ortúzar J de D. On the relationships between auditory and visual factors in a residential environment context: A SEM approach. Front Psychol. 2023,14. DOI: https://doi.org/10.3389/fpsyg.2023.1080149
21. Arellana J, Garzon L, Estrada J, Cantillo V. On the use of virtual immersive reality for discrete choice experiments to modelling pedestrian behaviour. J Choice Model. 2020,37:1–18. DOI: https://doi.org/10.1016/j.jocm.2020.100251
22. Long JS. Confirmatory factor analysis. Vol. 33, Quantitative Applications in the Social Sciences. Newbury Park: Sage Publications, 1983. 1–88 p. DOI: https://doi.org/10.4135/9781412983778
23. Rosseel Y. Lavaan: an R package for structural equation modeling. J Stat Softw. 2012,48(2). DOI: https://doi.org/10.18637/jss.v048.i02
24. Cerny BA, Kaiser HF. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behav Res. 1977,12:43–7. DOI: https://doi.org/10.1207/s15327906mbr1201_3
25. Nunnally JC, Bernstein IH. Psychometric Theory. 3rd ed. Vol. 5. New York: McGraw-Hill, 1968. 431 p. DOI: https://doi.org/10.2307/1161962
26. Hair JF, Babin BJ, Anderson RE, Anderson RE, Black WC. Multivariate Data Analysis. 8th Ed. Andover: Cengage, 2018. 834 p. Available from: https://www.cengage.com/c/multivariate-data-analysis-8e-hair/9781473756540PF/
27. Zhao W, Kang J, Xu H, Zhang Y. Relationship between contextual perceptions and soundscape evaluations based on the structural equation modelling approach. Sustain Cities Soc. 2021,74:103192. Available from: https://doi.org/10.1016/j.scs.2021.103192. DOI: https://doi.org/10.1016/j.scs.2021.103192
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Copyright (c) 2025 Luis Garzon , Zamir Mera , Ramiro Rosero , Christiam Garzon (Author)
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