skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

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

Sustainability, 2019, Vol.11 (24), p.7159 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su11247159

Full text available

Citations Cited by
  • Title:
    Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis
  • Author: Pirra, Miriam ; Pensa, Ruggero G.
  • Subjects: Cluster analysis ; Clustering ; Data mining ; Discriminant analysis ; Learning algorithms ; Machine learning ; Market segmentation ; Passengers ; Public transportation ; Quality of service ; Sociodemographics ; Transportation systems ; Travel ; Travellers
  • Is Part Of: Sustainability, 2019, Vol.11 (24), p.7159
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2071-1050
    EISSN: 2071-1050
    DOI: 10.3390/su11247159
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait