Introduction: Public Transport users have diverse mobility needs and preferences on how to meet those needs. While this is consistent with typical standards for both academic scholars and practitioners, less is known about the structure of these preferences, how different they are, especially with regard to innovations in Public Transport. Previous research works have been successful in bringing together and developing a comprehensive set of state-of-the-art innovations that could be potentially valuable for Public Transport Authorities and Operators in covering mobility needs. Purpose: Going a step further, this study collected empirical evidence on the preferences’ pattern of European citizens when considering these innovations. The present study’s objectives were (a) to measure European citizens’ preferences regarding Public Transport innovations, (b) to examine potential differences at individual level between innovations and demographic and behavioral measures, and (c) to profile respondents based on a multidimensional set of parameters including individual preference scores. Methods: Correspondingly, the study employed complementary methodological designs like the Maximum Difference Scaling method, which is an effective tool for encompassing large numbers of attributes, Analysis of Variance, and Latent Class Analysis. Results: Findings prioritized significant differences in user preferences along the tested innovations and innovations were linked to specific motivational schemes (viz. “information provision”, “efficient design concerns”, “provision of effectiveness”, “pricing concerns”, and “assistance provision”). Motivational schemes and their properties encompassing users’ diverse patterns of ranked preferences regarding Public Transport innovations were then employed as the basis for profiling. Conclusion: Further to methodological contributions reflecting the design of the present study, implications for practitioners regarding the use of differentiated mix of motives are also provided.
- Analysis of variance
- Latent class analysis
- Maximum Difference Scaling method
- Public transport