6. Conclusions

Depending on the context, several factors may affect users' choices. In this chapter, the main focus refers to modeling users' propensity to choose/adopt a new/ innovative technology. This is a crucial task in order to increase the attractiveness of strategies that may be employed to achieve sustainable transportation. In particular, two related main issues are still open in the literature: (a) interpreting and modeling users' behaviour towards these new technologies and (b) assessing the potential environmental impacts. It is widely recognized that traditional approaches used to interpret and model users' choice behaviour may lead to neglect the numerous nonquantitative factors that may affect users' behaviors. Indeed, users' choices may be influenced by social and psychological factors, symbolic and affective factors, habits and the conflict between collective and individual interests (e.g. car use as a commons dilemma). These imply that changes in transportation modes may be achieved either by influencing individual motivations and perceptions (psychological strategies) or by changing the context in which decisions are made (structural strategies).

The book chapter provides an overview of the methodology to be adopted in order to support psychological-based strategies. In fact, psychological factors, such as attitudes, concerns and perceptions, may play a significant role which should be explicitly modeled.

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Although several approaches may be identified in the literature able to address the above-mentioned issue, the hybrid choice modeling approach based on RUT can be considered a proper solution to explicitly consider the perceptions, attitudes and concerns in the modeling of the choice behaviour. The specification of such models requires a careful survey design, rigorous preliminary descriptive analyses and the model parameter estimation. The present chapter deals with all the cited issues, first introducing the main criticalities in modeling choice behaviour in new technological contexts, then proposing a methodological framework and finally introducing different explanatory examples on real case studies. In particular, Section 3 focuses the attention on the different approaches to collect users' attitudes/perceptions and concludes the need for a mixed approach based on both direct and indirect questioning. Section 4 introduces the methodology to properly evaluate the consistency of the dataset and the latent variables identification. It evidences the need for basic analysis, such as the estimation of the mean and standard deviations, and the importance of the Cronbach's alpha test and the principal components and the rotate component matrices for the identification of the latent variables. Section 5 deals with the model's specification issues, pointing out the most robust approach for the specification and calibration of a hybrid choice model with latent variable. All the introduced sections are supported by real experimental results [24, 25] for explanatory and guideline purposes.
