The effect of service attributes’ hierarchy on passengers’ segmentation. A light rail transit service case study
[EN] Market segmentation can help transit operators to identify groups of passengers that share particular characteristics and specific needs and requirements about the service. Traditionally, socioeconomic variables have been used to perform a simple segmentation, although satisfaction rates about...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/90386 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/90386 |
| Access Level: | acceso abierto |
| Palabra clave: | Latent Class Clustering Heterogeneity, Service Quality Public Transportation |
| Sumario: | [EN] Market segmentation can help transit operators to identify groups of passengers that share particular characteristics and specific needs and requirements about the service. Traditionally, socioeconomic variables have been used to perform a simple segmentation, although satisfaction rates about service attributes were not similar among individuals belonging to a group. Cluster analysis emerges as a novel analytical technique for extracting passengers’ profiles. This paper investigates passengers’ profiles at the metropolitan Light Rail Transit service of Seville (Spain). Latent Class Clustering algorithm is applied and satisfaction rates about different service quality attributes are considered for the segmentation. Particularly, two different cluster analyses are accomplished: first level, with only socioeconomic attributes; and second level, with eight service quality factors and socioeconomic attributes. The service quality factors are obtained through a principal component analysis, at which, the large number of attributes describing the service is reduced into constructs underlying them. Equivalent satisfaction rates are calculated for these service factors. Then, homogeneous groups of passengers are obtained. Additionally, the main differences among cluster are identified. |
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