Behavior and factors of choice of urban travelers

A data-driven approach to sustainable mobility

Bibliographic Data

ID19339166
AuthorsSayeh Fooladi Mahani (0000-0001-9445-0720, Universidade do Porto), Beatriz Brito Oliveira (0000-0001-8262-8907, Universidade do Porto, corresponding author), Lia Patrício (0000-0003-2414-1556, Universidade do Porto), Vera Miguéis (0000-0001-7831-9140, Universidade do Porto), Maria Antónia Carravilla (0000-0002-9245-2674, Universidade do Porto), José Fernando Oliveira (0000-0002-4061-1311, Universidade do Porto)
Year2026
Publication date2026-04-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTransportation (JOURNAL)
Journal identifiersISSN: 0049-4488 • E-ISSN: 1572-9435
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11116-026-10765-3
OpenAlexW7159572871
LanguageEN
References cited50

Achieving sustainable urban mobility requires shifting travelers toward public transport. However, policies often assume uniform preferences, leaving a critical gap in understanding how different travelers prioritize mobility factors. To address this, the study examines behavioral heterogeneity among urban travelers using a data-driven clustering approach based on the relative importance assigned to cost, comfort, environmental sustainability, and flexibility. Using data from 698 respondents in the Asprela area of Porto, Portugal, a mixed-use district combining universities, hospitals, and commercial facilities, the study applies principal component analysis (PCA) and K-means clustering to derive distinct traveler profiles. Unlike segmentation based solely on socio-demographics or observed mode choice, this approach groups individuals according to their underlying value structures. Six clusters were identified, ranging from car-dependent, comfort-oriented users to environmentally conscious and low-engagement groups. The findings show that one-size-fits-all policies are unlikely to address behavioral diversity effectively. Building on these insights, the study proposes tailored and cross-cutting policies to enhance the attractiveness of public transport and promote sustainability. By uncovering latent preference structures, the study contributes to more inclusive and value-informed mobility planning

Attractiveness · Cluster analysis · Diversity (politics) · Mode (computer interface) · Mode choice · Preference · Public policy · Public transport · Sustainability · Economic and Environmental Valuation · Transportation Planning and Optimization · Urban Transport and Accessibility

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Open DOIOpen Access