Travel behaviour modelling - discrete choice models, and parameters influencing choice of mode of transportation among commuters

Abstract

This habilitation thesis explores travel behaviour modelling with a particular focus on discrete choice models and the factors influencing mode choice among commuters. The research provides a comprehensive review of theoretical and empirical approaches to choice modelling, emphasizing the role of socio-economic, psychological, and environmental variables in shaping travel decisions. A key contribution is the integration of discrete choice models, including multinomial logit, nested logit, logit kernel models, and latent class models, to assess commuter behaviour and mode choice dynamics.

The study presents an extensive analysis of travel behaviour determinants, covering aspects such as speed compliance on highways, the adoption of carsharing services in the Czech Republic, potential behavioural shifts among habitual car users, and the acceptance of Mobility as a Service (MaaS) solutions. Methodologically, the research employs a combination of stated preference experiments, hypothesis testing, principal component analysis (PCA), and structural equation modelling (SEM) to derive insights into commuter decision-making processes.

The findings reveal that psychological factors, social influences, and perceived utility play crucial roles in the adoption of sustainable transportation alternatives. The thesis also demonstrates that choice modelling techniques can serve as valuable tools for policymakers aiming to promote sustainable transport behaviour. The research contributes to the development of decision-support models for urban transport planning and highlights future research opportunities in agent-based simulations, machine learning applications, and virtual reality-enhanced choice experiments.