Will Car Users Change Their Mobility Patterns With Mobility As A Service (Maas) And Microtransit? – A Latent Class Cluster Analysis

Abstract

highlights the data and methodology used in this research, as well as provides an outlook concerning the expected results and implications.

2. Methodology

2.1. Data collection and sample

Respondents are recruited from the Netherlands Mobility Panel (MPN). The MPN yearly collects mobility information of respondents, including a 3-day travel diary (Hoogendoorn-Lanser et al., 2015). The MPN also includes a household and a personal questionnaires. Thus, a lot of socioeconomic characteristics of the panel respondents are already available prior to the attitudinal questionnaire.

MPN respondents participating in this additional attitudinal survey are eighteen years old or older, and representative of the Dutch population in age distribution and gender. The sample is limited to individuals living in urban areas due to the larger expected importance of new mobility services in these areas. The questionnaire will be distributed in April 2018. A net sample size of 1000 individual is expected.

2.2. Latent class cluster analysis

There are different methods to cluster individuals. One of these is latent class cluster analysis (LCCA). LCCA models, also referred to as finite mixture models, group individuals according to an unobserved (latent) class variable that underlies their responses on a set of observed indicators (Molin et al., 2016). LCCA differs from traditional clustering methods such as K-means in several aspects. We want to highlight three of them: first, it uses a probability-based classification; second, it uses statistical indicators to identify the number of clusters, and last, it can directly classify the nature of the subgroups simultaneously to the clustering (by including covariates) (Magidson & Vermunt, 2002). On these grounds, we use LCCA for our analysis.

A total of 36 5-point Likert-scale attitudinal indicators are included in the questionnaire and analysis. Ten of these are directly related to MaaS, ten others directly related to microtransit and the sixteen remaining to general mobility attitudes (innovation, sharing, transferring and reliability). A factor analysis is performed previous to the LCCA to find latent unobserved factors while reducing the number of study variables. The analysis is performed using the LatentGOLD software tool.

2.3. Model conceptualization

Previous research has used LCCA to cluster mobility patterns of individuals. However, due to the novelty of the services under consideration in this study, behaviour towards MaaS and microtransit cannot be observed in order to cluster respondents. That is why initial attitudes towards these services are measured and analysed. The graphical representation of the initial model that we analyse is represented in Figure 1. MaaS and microtransit are latent variables and represent the indicators to explain the latent classes of the model (measurement model). The boxes below are the covariates of the model. First, the model is analysed with the latent variables only (posterior to the

factor analysis to analyse if any additional factors can be extracted from the attitudinal questions). Subsequently, covariates are added to the model to predict class membership (active covariates) or to further profile the latent classes (passive covariates).

Figure 1: Graphical representation of the initial latent class model to be analysed

Two strategies will be followed to increase the model fit, as suggested by Magidson & Vermunt (2004): 1) variable reduction (deleting one or more variables) and 2) increase dimensionality (increase the number of latent variables).

3. Expected results and outlook

This study tackles three aspects:

1) It clusters respondents according to their attitudes towards MaaS and microtransit.

2) It explains the role of mobility patterns and socioeconomic characteristics in explaining variability in attitudes towards novel mobility services.

3) It analyses the characteristics of the respondents with a positive attitude towards these new mobility services and discusses the consequences of a possible modal shift from those segments.

The model estimation results will be presented and discussed. The results will allow examining if car users have a positive attitude towards these new mobility services or if, on the contrary, it is public transport users that are more likely to adopt these new services. Better understanding the attitudes of different segments can also support customizing MaaS and microtransit to fit the needs and

desires of different segments. It will also help better forecast the modal shifts that these services can induce in urban areas.

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