1. Introduction

The diffusion and market penetration of new technologies are becoming a crucial point for transport system analysts and decision-makers. The main issues are regarding a correct understanding of the phenomena and the simulation of different possible operational scenarios.

Among the several new technologies aiming to let the transportation system be more efficient and sustainable, two main issues continue to be challenging tasks: (a) interpreting and modeling users' behaviour towards these new technologies and (b) assessing the potential environmental impacts.

Both issues are highly correlated as, without an effective interpretation of users' behaviour, no reliable estimation of the market penetration, and/or the corresponding impacts could ever be obtained.

Within the cited context, the consumer choice theory based on the random utility theory (RUT) may be considered the more effective and practical approach to model and forecast user's behaviour, but it is a common opinion that consolidated random utility model (RUM) formulations may lead to neglect the numerous nonquantitative factors that may affect users' perceptions and behaviors. As a matter of fact, psychological factors, such as attitudes, concerns and perceptions, may play a significant role which should be explicitly modeled. On the other hand, collecting psychological factors could be a time- and cost-consuming activity. Furthermore, real-world applications must rely on theoretical paradigms easily implementable in order to allow the estimation of users' choices in different technological scenarios.

HCM includes a latent variable model, it is possible to take into account the effects of users' latent attitudes, perceptions and concerns (i.e. integrated choice and latent

Approaches for Modelling User's Acceptance of Innovative Transportation…

Adopting the standardized notation for path analysis, Figure 1 introduces the general structure of an ICLV and allows to comprehend the different sub-models that define an ICLV: the latent variable model and the discrete choice model. In particular, the ellipses represent the unobservable (latent) variables, the rectangles represent the observable variables, and the circles represent the error variance or

Since the latent variables (attitudes, perceptions and concerns) cannot be directly observed and measured from a revealed choice or a stated preference experiment [18], they have to be modeled and then indirectly identified starting from a set of indicators. The latent variable model allows to identify and measure these unobservable variables as a function of the indicators, in order to include

Mathematically, a latent variable is treated as a random variable; the latent variable is specified through a structural equation formalizing it as a function of several parameters and a random error term. With regard to the relationship between indicators and latent variables, it can be formalized through a measurement equation, in which each observed psychological indicator is a function of a latent variable and a random error term. In general, each latent variable may be part of

with an alternative j is considered as a function of explanatory variables. The latent variables are included in the utility function of the alternatives as explanatory

<sup>j</sup> that an individual i associates

variable (ICLV) model).

DOI: http://dx.doi.org/10.5772/intechopen.87088

disturbance terms.

them in a choice model.

variables.

Figure 1.

19

Scheme of a hybrid choice model (HCM).

more than one measurement equation.

Finally, in accordance with the RUT, the utility U<sup>i</sup>

As clearly stated in the current literature, the propensity to adopt a new technology, and, in particular, an alternative fuel vehicle (AFV), is mainly affected not only by instrumental attributes of the technology of interest (alternative) and of the competing technologies (other alternatives) and by personal attitudes (attitudes) of the consumer (user) not depending on the alternatives but also on the consumer personal feelings and on the socioeconomic context in which he/she lives. Several recent analyses have pointed out the necessity to take into account attributes considering the perceptions and the attitudes of the users. For example, see [1]. The main issues of the literature refer to:


Indeed, even though RUMs models usually adopted in demand modeling are suitable for the representation of the choice process, these are not applicable to represent the perceptions and attitudes [6]. The issue was addressed in the literature through the hybrid choice models (HCM) [7–11] based on attitude investigations trying to infer the role of psychological factors with latent variables within a discrete choice modeling framework.

In particular, several studies aiming to overstep the boundary of RUMs have been conducted by Ortúzar and Hutt [12] and by McFadden [13], which around the 1980s investigated the possibility to include subjective variables in a discrete choice modeling. Starting from the approach proposed by Jöreskog [14] focusing on the investigation between latent variables and the measurement of the perception indicators, several researchers contributed to the assessment of the methodological framework of hybrid choice models (e.g. see [15–17]).

The book chapter is organized as follows: Section 2 focuses on modeling overview, while Section 3 focuses on survey design; the quantitative preliminary analyses and the model specification are, respectively, discussed in Sections 4 and 5. The corresponding results of two case studies are displayed and discussed in Sections 2 to 5.
