*2.2. MaaS Surveys and Model Calibrations*

Transport demand models need to be specified, calibrated, and validated with surveys. Table 1 shows some main elements relative to some papers related to surveying and calibrating transport models in a MaaS context.



\* RP: revealed preferences; SP: stated preferences.

In Table 1, the main characteristics considered relevant for the study reported in this paper are as follows: the MaaS definition column refers to the definition of MaaS; the travel choice column refers to the specification of models relative to the choice of transport mode (i.e., individual vs. MaaS) or the choice of bundling within MaaS (i.e., different prices and services for MaaS); the survey column refers to the survey sample which can be relative to either RP or SP surveys; the calibrated parameters column refers to the calibration of the parameters of the demand models specified; and the study area column refers to the study areas considered in the papers.

Most of the papers reported in Table 1 concerned the probability of choosing a bundle. The type of survey was almost always of the SP type regarding calibration of the parameters of the demand models. In 6 out of 15 papers in Table 1 the parameters were calibrated. Most of the papers analyzed considered the following study areas: the Netherlands, London (UK), and the Strait of Messina.

In [15], the propensity for MaaS in the Netherlands was studied through an SP survey. Additionally, in [19] the same type of survey was conducted in the same study area, but in this case, the choice of travel was the bundle. While ref. [16] used the same type of survey, it was conducted in the city of Padua by interviewing municipal employees. In [18], adaptive user behavior in MaaS contexts was considered through SP and RP surveys. In [20,21], the same survey was used to estimate the models (London Mobility Survey); the area studied in both papers was London. In [22], the researchers analyzed the opportunities and barriers to the use of multimodal transport and how MaaS could support its use [22]. In [17], MaaS systems were not explicitly considered; instead, a preference model for the Messina Strait in southern Italy was considered, where there was discontinuity due to the Messina Strait. In the same area, ref. [23] analyzed the willingness to accept MaaS through an SP survey. In [25], a survey in Australia was conducted on a sample of 3985 people [25]. Other authors have not built preference models for MaaS, such as those of [24,26,27]. In [24], a literature review was included and a MaaS bundle design was proposed [24], and in [26], the figure of the mobility intermediary was studied. In [27], willingness to accept "SocialCar" and "new multi.modal mobility service" was studied through an SP survey conducted in five European cities, as reported in Table 1. In [28], intermodal travel consisting of carpooling and public transport to implement a MaaS system in suburban areas was considered. In [29], a demand model starting from data retrieved from smartphones was proposed.

Most of the MaaS literature has focused on urban areas; in this paper, a MaaS preference model in an extra-urban environment with weak demand is reported in the following sections.

#### **3. Methods**

The pre-test model proposed in this paper is relative to the preference of MaaS over traditional modes of transport. It can support decision makers and decision takers before the planning and designing of a sustainable MaaS system with more detailed models. For the definition of the model, the adopted method involved the following steps: specification (Section 3.1), calibration from a survey (Section 3.2), and validation (Section 3.3).
