How does private vehicle users perceive the public transport service quality in large metropolitan areas? A European comparison
Metadata
Show full item recordEditorial
Elsevier
Materia
Service quality Satisfaction Private transport users Car users Ordered logit Market segments European cities
Date
2021-08-19Referencia bibliográfica
Juan de Oña, Esperanza Estévez, Rocío de Oña, How does private vehicle users perceive the public transport service quality in large metropolitan areas? A European comparison, Transport Policy, Volume 112, 2021, Pages 173-188, ISSN 0967-070X, [https://doi.org/10.1016/j.tranpol.2021.08.005]
Sponsorship
Spanish Ministry of Economy and Competitiveness TRA2015-66235-RAbstract
Most studies on public transport service quality focus on the perspective of the public transport user, overlooking
potential users, that is, private vehicle users. This paper explores the perception of private vehicle users about the
quality of public transport. The objective is to identify the attributes that bear the greatest influence on the
general satisfaction of the private vehicle user with respect to public transport in five major European cities:
Berlin, Lisbon, London, Madrid and Rome. The analysis estimates the effect of 14 quality of service attributes on
general satisfaction using Ordinal Logit Models (OLM), using data from an online survey sent to private vehicle
users, with a similar sample size for each city (N > 500 per city). To analyse the heterogeneity of the perceptions,
20 models were calibrated: 15 models were calibrated controlling for location; and five models (one per city)
were calibrated controlling for sociodemographic and mobility characteristics. Frequency, punctuality, intermodality,
cost and cleanliness were identified as attributes exerting a significant effect on satisfaction in practically
all the models, meaning they could be considered core attributes for private vehicle users. On a second
level, a group of attributes were significant in a substantial number of models (service hours, proximity, speed,
temperature and safety). Finally, the remaining attributes were only significant for specific cities or segments.
The last two groups of attributes allowed to detect differences between cities and market segments.