期刊
TRANSPORT POLICY
卷 112, 期 -, 页码 173-188出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.tranpol.2021.08.005
关键词
Service quality; Satisfaction; Private transport users; Car users; Ordered logit; Market segments; European cities
资金
- Spanish Ministry of Economy and Competitiveness [TRA2015-66235-R]
This paper investigates private vehicle users' perceptions of public transport quality, identifying key attributes that significantly affect overall satisfaction. Attributes such as frequency, punctuality, intermodality, cost, and cleanliness were found to be core attributes for private vehicle users, while others showed significant impacts in specific cities or market segments.
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, inter modality, 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.
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