Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis

Abstract

The quality of the transport system offered at city level constitutes an important and challenging goal for society, for local authorities, and transport operators. Therefore, appropriate evaluation of travellers' satisfaction is required to support service performance monitoring, benchmarking, and market analysis. This aspect implies the collection of satisfaction levels for different passengers' groups, as it could provide interesting suggestions for identifying priority areas of action. To this end, an original study aimed at understanding the main aspects affecting the common view of satisfaction among different kinds of travellers at European level is presented in this paper. A specific survey investigating how travellers perceive the quality of their journey is proposed to people living in cities characterised by different sizes. Data are then analysed through a multi-view co-clustering algorithm, an innovative machine learning technique that highlights clusters of respondents grouped according to various categories of features. Such results could be used by local authorities and transport providers to understand the specific actions to be operated to improve the quality of transport service offered in a market segmentation dimension.

Stated interest, actual use or indifference towards car sharing: profiling students and staff of a university campus in Turin (Italy)

“…Some works are found in literature applying this technique to different domains of transport engineering such as tours classification (Pirra and Diana, 2016) or transit quality evaluation (de Oña et al, 2014;de Oña, de Oña and López, 2016). However, an innovative parameter-less method named "multi-view co-clustering", is adopted here (Ienco et al, 2013;Pirra and Pensa, 2019). Classical co-clustering computes a partition of objects and a partition of features simultaneously, thus providing meaningful clusters of objects with a useful interpretation given by the grouping on features (Dhillon, Mallela and Modha, 2003): this approach is different from clustering features and objects separately, since it adopts an objective function whose optimization takes into account both the object partition and the feature partition.…”

The Usage of Public Transport and Impact of Satisfaction in the European Union

“…The European Commission supported a big project A MEasurement Tool to determine the quality of the Passenger EXperience (METPEX), which aims to measure the determinants of the passengers' experience and service quality including public transport [22,23]. This project encompassed eight EU cities and the results were presented in a vast number of research studies [18,[23][24][25][26][27][28][29]. However, to the best of our knowledge, at the country level the UPT satisfaction and usage were scarcely analysed.…”