Complementing or competing with public transit? Evaluating the parameter sensitivity of potential Mobility-as-a-Service (MaaS)urban users in Germany, the Czech Republic,Poland, and the United Kingdom with a mixed choice model

In this study we conduct a large survey (n=6,405) on urban daily commuters in four European countries (Germany, the United Kingdom, Poland


Introduction
Urban transportation is evolving rapidly, particularly as a wide range of new on-demand modes, such as bike sharing, car sharing, and ride sharing are introduced.Even though these mobility options have been available for years, only lately has it become possible for such services to operate in real-time in very large urban settings.These various services create more options and extend the range in which people can travel, but the abundance of choices can be confusing.Mobility-as-a-Service (MaaS), provided to users on single app platforms, is increasingly popular because it reduces complexity which maximizing the advantages of different options, which can be combined.
While not yet fully defined [1], MaaS typically incorporates all aspects of a travel experience, including reservations, payments, and pre-/post-trip information [2] in order to get a user from point A to point B regardless of transport mode.A strong public transportation system would ideally form the core of MaaS in dense urban areas, where congestion, livability, and parking space are high on urban mobility agendas, because it could provide first-/last-mile solutions or complement public transportation (PT) for certain trips where MaaS options are not convenient.When it does integrate PT options, MaaS shows potential for reducing car use ( [3]).
According to prior research, MaaS can increase user travel satisfaction rates while facilitating modal shifts towards lifestyles centered more around public transportation and less on private vehicles.Having received substantial recent attention, hopes are high that MaaS will fuel a mobility revolution similar to the introduction of the private car in the 20th century.However, it is unclear whether the general public will follow the modal shifts ignited by MaaS pilots and whether public transportation-not just on-demand services-will play a greater role in urban MaaS offerings.In other words: will MaaS complement public transportation or simply compete with it, over the long term?
With this study, we enrich our understanding those who choose to use MaaS and investigate the relationship MaaS has with PT in terms of preferences which can impact decisions about which transport mode to choose.To date, there has been very little quantitative research on this topic beyond the evaluation of early MaaS pilot adoption .In our research, we examined whether the different traveler groups we identified preferred using public transportation or, more specifically, other on-demand services, as well as how sensitive these groups were to costs and travel times .Our model analyzed the responses of a large sample of more than 6,000 daily commuters from major cities in Germany, the Czech Republic, Poland, and the United Kingdom.
Within our study of the characteristics of commuters and decision making regarding MaaS, we paid special attention to the relationship with public transport services.The complex multilevel study was performed using the mixed logit model, also known as the "latent class" or "hybrid" model [4,5].First, we derived the segmentation of our overall sample into more homogeneous clusters ("segments") of users, which were than profiled.Finally, individual models were calculated for each segment, providing an in-depth view of the parameters that influence the odds of MaaS or PT use among habitual car users.
The main contributions of this study are the following.First, we identified user clusters with respect to their choice patterns towards proposed travel modes, identifying different profiles of potential MaaS users (and whether they are more likely to use MaaS or public transport).Second, we identified the sociodemographic characteristics that influence the odds of using public transport and MaaS under the conditions presented in a choice experiment we designed.Third, we investigated user sensitivity towards price as well as travel times for various travel modes, which resulted in insight into the decisive factors that can convince potential users to use shared travel modes.Finally, thanks to the multilevel nature of the study, we identified crucial factors with the potential to increase both MaaS and public transport adoption rates among different types of daily car users.
The remainder of this paper is organized as follows: Section 2 provides an overview of the existing literature investigating travel mode choice with a focus on MaaS.Section 3 explains the methodology employed in this study, and Section 4 presents the results.Section 5 discusses the key results, focusing on their implications for future service providers, and Section 6 provides final conclusions.

Previous studies
In this section, we review and identify the existing literature on MaaS users and relevant transport parameters, providing the most prevalent modeling techniques for transportation-related choice modeling.The first subsection describes existing studies and explores possible MaaS user types, while the second focuses on modeling techniques and how models are evaluated.
In this section, we review and identify the existing literature on MaaS users and relevant transport parameters, providing the most prevalent modeling techniques for transportation-related choice modeling.The first subsection describes existing studies and explores possible MaaS user types, while the second focuses on modeling techniques and how models are evaluated.

On sensitive transport parameters of MaaS users
The definition of Mobility-as-a-Service (MaaS) is not yet fully defined and accepted despite a decade of discussion.As pointed out by [1], the future of MaaS adoption is still uncertain and full of challenges, particularly due to the recent lack of focus on MaaS core values.[6] discussed the "Ws" (when and where, who and how, why) of MaaS, noting that up until 2020, MaaS concepts and definitions were still evolving, but the general concept typically includes keywords such as service integration, user-oriented, mobility packages, and a high level of service digitization.Commenting about a lack of data-focused analysis (especially user analysis), [7] identified initial potential users of MaaS as young, progressive, and well-educated who do not want to pay for/who wish to stop using and owning private motorized vehicles.
An overview of recent findings regarding factors which influence the adoption of such modes or services can be found in [8].Focused more on potential users, [8] conducted a systematic review (10 studies related to MaaS with sample sizes of 250-1,500 participants from the EU, China, and Australia published between 2018-2022) to understand the factors influencing the adoption of new mobility technologies and services, including MaaS, concluding that personal traits, attitudes, and factors related to technology acceptance have a generally positive relationship towards the likelihood of MaaS adoption, noting users with an interest in technology, who were multi-modal, and who had positive attitudes towards technology and to technological service-related attributes were more prone to adopting MaaS, while people with positive attitudes towards safety, with unimode preferences, who own a car, and who have children in their household were less prone to adopt MaaS.Age, gender, and positive attitudes towards PT findings were had mixed.
Furthermore, [9] conducted an extensive literature review of MaaS-related topics, concluding that potential MaaS users are still uncertain but that early adopters are generally likely to be young to middle aged people living in urban areas.They noted that the set of state-of-the-art works focusing on identifying user groups is limited, but these studies indicate that values such as a low sense of ownership as well as age, place of residence, and other socioeconomic, sociodemographic, and cultural characteristics likely play a role in MaaS adoption rates.
A South Korean case study (a survey conducted in February 2020 with 781 participants) [10] identified factors influencing the MaaS use and included constructs for measuring intention to use MaaS and variables related to influential factors with a ordered probit model and estimating marginal effects.The study identified seven key variables from 11 tested, with user characteristics such as being male, from a lower income household, and existing public transit use showing greater intent to use MaaS.Similarly, a study in the Netherlands (1,010 participants and 8,080 observations) identified that drivers are the least likely group interested in shifting to MaaS solutions [11].The study showed that those who would shift to MaaS would likely benefit from using integrated public transport followed by bike sharing service possibilities.
A behavioral model based on TAM and focused on willingness to adopt MaaS was conducted in [12] using data from Madrid (online survey with 1,000 valid responses) to validate general hypotheses related to openness to experimenting with new technologies, technology affinity, and privacy concerns.Within their cluster analysis, based on user related latent variables, they identified four clusters, which they named "mobility profiles": "technological car-followers" (26.1% of participants); "unimodal travelers" (34.9%); "MaaS-lovers" (14.6%); and "active public transport supporters" (24.4%)."Technological car-followers" and "MaaS-lovers" were observed to be more willing to adopt MaaS followed by active public transport supporters."Unimodal travellers" showed more unwillingness to use MaaS.They found that high levels of openness, extraversion, and agreeableness were significantly related to a positive attitude towards personal innovativeness in information systems such as MaaS applications.
On a similar note, [3] identified user clusters according to their attitudes towards MaaS, empirically pointing out which population segments were most likely to use MaaS through a survey conducted in (sub)urban areas in the Netherlands (May 2018) and including several attitudinal Likert scales for MaaS with focus on pooled on-demand services.The study included Exploratory factor analysis (EFA) and latent class cluster analysis (LCCA) for a dataset containing 1,006 valid responses.Five clusters, using EFA, were identified: Cluster 1 (32% of the sample): "MaaS-FLEXI-ready individuals," Cluster 2 (25%): "Mobility neutrals," Cluster 3 (22%): "Technological car-lovers," Cluster 4 (15%): "Multimodal public transport supporters," and Cluster 5 (6%): "Anti new-mobility individuals".[13] asked 568 residents of the Paleiskwartier district in the city of 's-Hertogenbosch, Netherlands, to identify user groups regarding their intention to use MaaS using a two-step cluster analysis.This analysis resulted in four clusters: "MaaS curious" (18% of the sample), "Frequent car drivers" (24%), "Multimodal travelers" (30%), and "Car lovers" (28%).They found that public transport users and people with strong beliefs about green and healthy commuting were more likely to use MaaS as a long term solution, but car users and people with low consideration for the environment were much less likely to adopt MaaS.They also indicated that demographics, at least the ones analyzed in the study, did not have a strong influence on the intention to use MaaS, contrary to what the other studies noted above reported.
Hoping to gain a broader perspective, [14] identified several studies about potential MaaS users and their characteristics.Within their state-of-the-art review, they indicated a gap in the body of knowledge when it comes to heterogeneity of preferences in such mobility services as well as a lack of studies that identify user groups.Furthermore, within the city of Manchester (United Kingdom), they collected data about 475 individuals (June/July 2018) using both revealed preferences (RP) and stated preferences (SP) experiments in order to fill the research gap.Using a Latent class choice model (LCCM) for the selection of MaaS packages and using several model fit statistics (Akaike Information Criterion (AIC), Bayesian information criterion (BIC) , and rhobar squared) they showed that a three-class model achieved the best balance between goodness-of-fit, parsimony, and the interpretability of results.The identified classes were "MaaS package avoiders" (52% of participants), "MaaS package explorers" (23%), and "MaaS package enthusiasts" (25%).Results buttressed previous results showing that multimodal users were more likely to opt for MaaS, but, unlike other studies [3,13], they did not find car ownership and usage to be significant factors in the choice model.
In order to account for behavioral change towards MaaS, including after MaaS app implementation, a preliminary work proposed a longitudinal study [15] to collect data in the United Kingdom's Solent area (Portsmouth and Southampton) through a stated and revealed preference (SP and RP) survey.In the future, they plan to analyze data through a travel behavior model that would aggregate mobility users into classes based on travel behaviors and attitudinal latent variables and to identify a utility cost function through an SP experiment.Although they conducted a preliminary study, they confirmed the relevance of our own study by identifying the same state-of-the-art gap that we did.
Finally, [16] explored how to understand the potential demand for MaaS depending on different business models such as monthly subscription, pay-asyou-go, or creating one's own package using SP data collected face-to-face in Sydney, Australia, and Tyneside, United Kingdom.One major finding was that car lovers appears to value the convenience of the private transport mode, and even though they might see benefits in using MaaS, they likely would not be willing to give up their cars, with 50% of the sample showing a belief that the car will "always be king."Furthermore, they found that pay-as-yougo might increase uptake of MaaS but it could lead to less sustainable mode choices, whilst potential monthly subscribers stated a preference for PT and soft transport modes.

On discrete choice modeling
Discrete choice modeling is commonly used to describe the impact of decision makers' characteristics and the attributes of alternatives and choices [17].Discrete choice models have been widely used in numerous fields of applications for the last three decades [18].Early transport applications of discrete choice models were to examine the binary choice of travel modes, with further advances in transport applications enhanced by improved discrete modeling methods [19].
The main statistical models used to support travel behavior research are discrete choice models, particularly those from the logit family, such as the multinomial logit model (MNL), the nested logit model (NLM), the kernel logit model (KLM)-particularly useful when panel data are present, and the mixed logit model (MLM) [20].For decades, commuting behavior has been intensively studied using a variety of methodological approaches [21].The logit model proposed by [22] is commonly used and perhaps the most prevalent when considering the choice of mode of travel [23].
MLM generalizes a basic MNL by allowing the associated parameters of the observed variable to vary according to a known population distribution across individuals [5].
Different discrete choice models considering transport mode and passenger behavior selections are employed in the literature.[24] empirically examined factors affecting mode of transportation choices in Germany and the United States by applying a multinomial logit model.The study demonstrated considerable differences in travel behavior among similar individuals in both countries using data from two comparable national travel surveys.By estimating weighted conditional logit and mixed logit models of airport and airline choice, [25] examined how travelers selected airport-airline bundles for trips from San Francisco to Los Angeles, focusing on the joint relevance of airline and airport characteristics.[26] showed that night had a significant effect on the choice of transport mode for commuting.The study, which uses multinomial logit models, suggested that the quality and importance of the results would be further increased by extending it to other models such as a nested logit model (NLM), Kernel-Logit model (KLM), or the mixed logit model (MLM).
Modeling travel demand with discrete choice models often has raises problems in terms of methodology and data.The main concerns are how to handle interdependencies between related choices and the practical and theoretical problems that arise from choosing from a large number of alternatives.[19].This is further complicated when considering panel surveys, where respondents are asked to repeatedly answer on a presented stated choice experiment where only certain parameters-not choices-vary.Historically, a number of theoretical and practical studies have shown that the most suitable tool for such within-sample dependencies from repeated answering is the Kernel-Logit model [19,27,28].

Methods
This study involved large data collection activities throughout Europe (described below), with fairly complex surveys applied.As a result, the nature of the data collected and the study as a whole demanded a robust and carefully chosen data analysis and modeling technique.Based on the analysis of the literature presented in Section 2, the study was divided into six phases, each containing individual research activities.The overall workflow for this as well as consecutive phases are shown in the figure 1.

Logit-Kernel model for Segment1
Logit-Kernel model for Segment2 Logit-Kernel model for Segmenti The results of the first stage of our project have already been presented and elaborated in detail in Section 3.1.These were achieved with an extensive and thorough literature analysis and identification of state-of-the-art regarding both the understanding of commuter behaviors and the most promising and novelty methods for discrete choice modeling, namely in the transportation field.This section is divided into two subsections.First, we deal with the data curation process and methods (the creation of the survey, the choice experiment, the definition of the respondent target group, and the description of the collected data).The second subsection introduces and describes the data analysis methodology, including the complex approach to the multilayer hybrid model.

Survey description
This online survey was conducted in four European countries: Germany, the United Kingdom, Poland, and the Czech Republic.The survey was spread using a panel data provider which distributes the survey through online channels to registered individuals and allows to set representative quota for age, sex, and residence.For each of the four countries, the questionnaire was written in each respective national language and programmed separately.The survey was open to respondents from October 16, 2020, to October 26, 2020 for a total field phase of ten days.Respondents were able to answer the questionnaire online at any time during this ten-day period.It should be emphasized that the COVID-19 pandemic was very much on the media agenda and COVIDrelated restrictions were also in force.Consequently, a corresponding influence of the pandemic on decision-making behaviors cannot be ruled out.
The questionnaire was divided into five topics (A, B, C, D, and E) enclosed by an introduction and a conclusion.Topic block A focused on latent personality variables divided into three blocks and placed at different points in the questionnaire for the sake of variety.Information on functional components and requirements of a MaaS app were probed within area B. In topic area C, respondents were asked to rate the attractiveness of different mobility alternatives for a previously explained commuting scenario using a stated preference design (choice experiment).Topic D consisted of questions regarding the perceived benefits of MaaS, differentiated into the above-mentioned dimensions, and questions on attitudes for use and intended use.To create a uniform understanding among the respondents, a definition of MaaS was inserted in front of block D in the survey instrument.Block E included specific mobility package questions about the preferred means of transport implemented and willingness to pay.

Creation of the choice experiment
As noted for topic block C, test participants were invited to partake in a hypothetical thought experiment in which they had to choose between six transport options to cover a specific distance:.Intermodal solution options included public transport, car sharing, bike sharing, micromobility, mobilityon-demand, and the combination of public transport and micromobility.This part of the survey was designed according to [29] as seen applied in recent works [16,30].
Before being confronted with the choice experiment, participants were introduced to the scenario to be considered in the decision-making process.This stimulus served to ensure that all respondents started from the same initial situation and that there would be a common understanding of the framework conditions.The scenario described an ordinary trip through the city center in a car with a given distance and duration.The mobility options presented within the choice experiment represented alternatives to the car, from which respondents could choose the most attractive option.Test subjects were shown a total of 16 different cards, which changed the attributes of individual mobility alternatives on each map.An example of the corresponding alternatives and attribute levels, for the case of Germany, is presented in Table 1.
The level characteristics were kept identical across the four countries.Only the travel costs were adjusted to the respective price structures of the countries in order to ensure our probe represented as close as possible real conditions and product offers.After defining all the attributes for each country, orthogonal design was followed to obtain a representative selection of all possible combinations.Orthogonal design led to a total number of 64 cards.Since this high number would have been likely to place an undue burden on test participants or to lead to an excessive size for the decision experiment, four blocks of 16 cards each were formed.The test subjects were then randomly assigned to a block, so that in total approximately 400 participants answered the set of cards for each block.

Target group
The intended population included adult commuters who lived in urban regions of the countries mentioned, because this is the potential target group for MaaS use as well.Thus, only people at least 18 years of age with regular mobility behaviors and living in a city with at least 80,000 inhabitants were allowed to participate in the survey.These individuals were identified as "regular commuters" if they regularly travelled by means of transport at least three days a week, therefore having a mobility need suitable for MaaS use.Since MaaS is primarily a mobility solution for urban areas, it was possible to exclude people living in rural areas by selecting the number of inhabitants of the place of residence.After the survey was completed, the data from the four separate online surveys were combined into one overall data set.After cleaning the data, with exclusion of participants with very fast completion times and patterns in response behaviors, the sample size was a total of n = 6, 405 participants For the final model estimation, only the most promising variables were incorporated into our model.Based on the extensive review of the literature about existing models that previously investigated the willingness to use MaaS, we selected the 13 variables presented here, briefly explained in Table 2.

Application of multilayered hybrid model
Following data collection, the concept of Multilayer Hybrid Model (MHM) was introduced to analyze dependencies in the collected sample.As explained by Qin et al. [27], MHM formulates latent attitudinal variables from dichotomous None of the alternatives survey items.It was first introduced as a modeling framework by [28], and in its formulation, although similar to the mixed logit model, it allows for more flexibility, since it does not require any parameter distribution.Nevertheless, this framework did not include details regarding parameter estimation techniques; rather, it only formulated an overall problem definition.MHM assumes that the choice preference and parameters which influence this choice can differ in society and can be further divided into smaller, more homogeneous, and hence easier to model classes (also called segments).Assume that in reality there exist segments of commuters (according to their behaviors), and that the segment each traveler (represented by respondent) belongs to is not known.The probability that the traveler r is in segment i can be expressed in the following way [27]: where β i is the parameter that will be estimated within the segment i and S rN is the variable for the traveler r choosing the mode N in segment i.
Similarly, the probability of the traveler r belonging to segment i can also be expressed as follows: The survey analyzed here was conducted in four countries.This information about "country of origin" for each completed survey was included as a survey language parameter.For the purposes of further analysis, this parameter was transformed into four separate dummy variables.Each of the countries had its own dummy variable.

Gender
Variable representing the gender each respondents identifed with.Our survey enabled the choice of a "do not specify" option.Such respondents were excluded from this study due to their very small prevalence (less than 0.1%) 0 -man, 1 -woman Territory This variable represents the agglomeration size from which the individual survey was collected.The bins of the agglomeration size where <10,000 <80,000 <100,000 <300,000 <1, 000,000 and above coded as discrete values 1 through 6

Household size
The number of inhabitants in a household of the respondent.
1-2-3-4-5 (and more) Age Age of the respondent. 1 through 99, continuous Income Income understood as per household of the respondent.Each country had different thresholds according to local conditions. 1-2-3-4-5-6 Education Similar to income level, available responses were adequately adjusted in different countries this had to be done due to the fundamental differences in the organization of the educational systems (not every level had its equivalent across the Europe).Therefore, the scale in each country was adopted as closely as possible to the five scale pattern presented here, with preservation of hierarchical character of the variable.
where γ i is the estimated parameter of the segmentation function and Z i is the observed variable.Hence, the resulting probability that mode N is chosen by traveler r in segment i is formulated as follows: and the likelihood function follows: Now that the theoretical framework for the hybrid model has been introduced, the respondent (traveler) segmentation procedure and within-segment choice modeling is described in more detail in the following subsections.

Segmentation of Respondents -Finite mixture model
The response patterns among respondents in marketing and various opinioninvestigating surveys cannot be assumed to be uniform.People differ in terms of their opinions and attitudes; therefore, it is normal for response behaviors to be heterogeneous.The Multinominal Logit model assumes that each respondent's (r) replies are independent and the model cannot account for the variability in respondents' behavior.We applied finite mixture modeling [31] to take data dependency due to unobserved heterogeneity in the respondents' sample into account data.The general model in our study was expanded to account for two different types of heterogeneity.First, respondents may differ in terms of their answer patterns, resulting in variations in the likelihoods with which they typically check various rating response categories.By applying whole data set segmentation, we could account for these differences in response styles.Second, the heterogeneity aspect was related to the panel character of the collected data (that is, the interdependence of multiple responses collected from the same respondent) and was addressed in the dependency modeling in Section 3.2.2.
Clustering was used for 113 variables, most of which were represented on a 5-point Likert scale, and some were binary (e.g., gender).The data set had 6,406 records, each representing answers to the questionnaire from individual respondents.The method used for clustering was based on modeling the data with a mixture.The components of this mixture described individual clusters in the data set.Furthermore, these clusters (also called segments) were intended to reflect groups of people with a specific attitudes toward MaaS-specific answering patterns in our survey.
Due to the large size of the task, two measures were used: 1.The components were described by a binomial distribution.It had only one parameter, and the flexibility of its probability function was convenient for the description of clusters.2. The variables were assumed to be mutually independent.This is acceptable when we want to determine the number of clusters and not their shapes.
Under these assumptions, we accepted the theory of independent mixtures.The data were treated like realizations of the discrete variable denoted by For each variable x i , we also introduced a so-called pointer variable c i .This was a discrete random process whose values pointed to the active component within variable x i .These pointers were described by a categorical distribution Then, what we needed for estimation was the joint distribution of the unknown objects for each variable where x (t) = {x 1 , x 2 , • • • x t } and the lower index denoted the discrete time.
To this distribution, we applied the Bayes' rule and then used factorization ) where we used independence induced by the introduced models and treated each variable x i separately.This relation presented the Bayes' rule for a mixture but only for known values of the pointer.As they were not known, we needed to estimate them.This estimate was given by the distribution was the number of components) which were probabilities that the i-th entry of the current data record belonged to an individual component within the i-th variable.
Using this formula in the Bayes' rule above, we could estimate the components in a standard way, with the only difference being that the data coming to the statistics update were multiplied by their respective weights.However, the above procedure works only for the predetermined and fixed number of components.To discover the real number of components in our dataset, we used the AIC (Akaike information criterion) coefficient, see Figure 2. We ran the process of estimation for various numbers of components and selected that for which the AIC coefficient had the smallest value.The definition of AIC was where k was the number of parameters in one component and L was the estimate of the likelihood where n d was length of the data and with w (i) j being the weighting vector connected with x i and f j (x i; t | p i ) is the j-th component within the i-th variable.
The search was started with two, four, six, and up to twelve components and then refined in the area of minimum.The results are depicted in Figure 2. From this 2, we can see that the optimal number of components is five, which was confirmed with the so-called Elbow method [32,33].This was further confirmed as a point of the biggest change of the gradient in the AIC figure between the two following segment numbers.
The structure of the resulting segments and their statistical analysis are presented in Section 4.

Logit-Kernel -The discrete choice model
The second layer of the proposed model was a parameter estimation to discover the relationship between endogenous and exogenous variables and the explained variable of the model.
The general discrete choice model for a given individual n, n− 1, . . ., N was formulated simply as follows: where N was the sample size and an alternative i, i = 1, . . ., J n was the number of alternatives in the choice set C n of an individual n.In this case, y in indicated the observed choice and U in was the utility of alternative i as perceived by this user.This model had two natural extensions.The assumption that the disturbances are i.i.d.Gumbel led to the tractable yet restrictive logit model.The assumption that the disturbances were multivariate normal distributed led to a flexible but computationally demanding probit model.
Since, in the data collection phase, we applied a choice experiment with a survey panel where we collected multiple answers (choices p = 1, . . ., P n ) per respondent and the choices were not independent, the probability of a sequence of choices was not equal to the product of the individual probabilities.A common method for addressing the panel form of the data is introducing random variations in preference in the modeling framework.The logit-kernel (LK) model, also known as the Mixed Multinomial Logit (MMNL) model, directly treats the repeated choice nature of the panel data by introducing a variation in sensitivities across respondents, with intrarespondent homogeneity.Typically, to adjust our choice model, the resulting Logit-Kernel for panel data took the following form [4]: is a (J n P n × M ) matrix of factor loadings, including fixed and/or unknown parameters, T is a (M × M ) lower triangular matrix of unknown parameters ζ n is a (M × 1) vector of i.i.d.random variables with zero mean and unit variance, v n is a (J n P n × 1) vector of i.i.d.Gumbel random variables with zero location parameter and scale equal to µ > 0. The variance is g/µ 2 , where g is the variance of a standard Gumbel (π 2 /6).
In the model 7, the unknown parameters were µ, β, and those of F n and T .X were observed, ζ n and v n were unobserved.The key in terms of identification was that the covariance matrix examined for identification was now of dimension J n P n ×J n P n .As noted by Ben-Akiva, this enables potentially many more disturbance parameters to be estimated, which in turn is suggested by the Order Condition alone, stating that the maximum number of (alternativespecific) disturbance parameters may be as high as JP (JP − 1)/2 − 1.For our study, the Logit Kernel was used with inclusion of an additional factor representing latent characteristics for an individual such as safety consciousness, environmental concerns, or perceived social importance.This approach has already been applied with great success by [34].Such latent characteristics, which are directly unobserved to an analyst, enter all utilities for a person (just as e.g.income would).
With reference to Equation 3, we now derive: • Q r (i) as a probability resulting from segmentation of respondents achieved with the Finite Mixture Model according to Section 3.2.1, and • R r (N | i) as a probability resulting from the Kernel-Logit model according to section 3.2.2.
From this we derive the final probability of the predicted response with our model P r (N ).The results of this modeling are presented in the following section.

Results
Before providing an in-depth elaboration of the complex modeling results, we consider it interesting and purposeful to briefly identify each of the five segments resulting from finite mixture modeling.Our model was conceived as a multilevel approach, with a separate approach to individual clusters of respondents.Therefore, the following section consists of two subsections.First, we describe in more detail results from finite mixture modeling, and the second subsection is fully dedicated to modeling results.

Identification and description of segments
As mentioned in Section 3.2.1,five segments (also called "profiles" here) were identified in the responses to our study.In this section, based on the descriptive statistics analysis of each segment, we provide (if possible) the most prevailing and distinctive characteristics that separate each segment from the rest of the respondents.One of these characteristics is age, but it is not the only one; see Figure 3.
The histogram showing the number of respondents in each identified commuter profile is presented in Figure 4a.The number of respondents varies from 264 in segment 5 to 2,673 in segment 4. The right side of figure 4 represents the number of respondents in each segment who chose public transport, MaaS, or "none of the alternatives" at least once in choice experiment.The number of respondents in each sector was then inspected again, with respect to the number of individual transport mode choices in each segment.It is important to note here that the numerous options available in the choice experiment led to significant dispersion of respondent responses to different mobility options.Here, we focused on "Public transit," "intermodal mobility" (which we described as a MaaS), and the last option in which the respondent did not feel any option fit them.Since respondent could not choose a personal vehicle as one of the modes of transport, this option was treated as "I do not want to change from current mode (predominantly personal vehicle)."

Segment 1 -Carsharers
The first identified profile, with almost 500 respondents, can be described as supporters of the shared economy.These commuters noted they often tend to travel using carsharing or carpooling and they were the youngest respondents in our survey, with the largest families (usually four or more people in household).According to our survey results, they (in general) used carsharing to travel to/from work and believed that it is important to share a ride with colleagues.Moreover, they often expressed a belief that social acceptance and image are somewhat important when choosing a mode of travel.Therefore, they might commonly be referred to as "carsharers".Notable is that most respondents in this category, in our survey, were men.

Segment 2 -Car users by choice and beliefs
Segment 2 was described as commuters who are convinced they must regularly use personal cars for daily travel.Here, these were predominantly young people with families (average household size: 3.2) who expressed a belief that cars are important for safety, security, and a high level of comfort.They noted they are also not very keen to consider a different travel mode in the future based on their stated preference responses.The most valuable advantage of traveling by car for these people stated was short travel time and the social image related to the mode of transport used.

Segment 3 -Unspecified users
Although we were able to specify a user profile for four segments of five, this was not possible for Segment 3.These users had average answers to all the attitudinal and preference questions, consisted of a mixture of both genders, and were an equally distributed cross-section in terms of of household sizes, incomes, and so on.The only "unique" characteristic of this segment was the dominant age being 40-50 years old and that the majority of these respondents were from the Czech Republic.However, this is not enough to consider a broader characterization of these respondents.We believe that undecided, uncertain respondents who are skeptical of shared mobility options but who might change could be a way to generalize this profile.

Segment 4 -Habitual car users by necessity
Segment 4 profiling revealed that these are mostly the commuters in our survey who reported not living close to their daily travel destinations.This was visible from a number of parameters examined as well as from the responses to the survey.For example, those respondents tended to live in large cities and reported that they usually commute to work every day.They said they regard the car as a valuable travel mode and are less likely to use alternatives such as micromobility, cycling, and walking.However, due to their commuting patterns, public transportation was not very common in their stated choices.Therefore, unlike the respondents in Segment 2, they said they do not mind traveling with means other than a private car, but noted transportation systems do not provide them with viable alternatives to car travel.

Segment 5 -Frequent public transport users
Segment 5, in general, represented the oldest group of respondents in our study (see Fig 3).These travelers, although they noted they use personal vehicles, also report traveling very often by bus or tram.Almost 85% of them said they use public transportation at least once a week.This "pro PT" attitude was also visible in the part of the survey where they answered attitudinal questions regarding their opinions about public transport and how much they agree with the statements like "Public transport can be problematic with regard to hygiene" or "Safety is concern when traveling with public transport."For all of these questions, members of Segment 5 tended to assess these statements with very small significance (average of 4.7 on 5-point Likert scale, where significance had a descending order).This very basic characterization of each segment as the basis for the latter interpretation of user sensitivity modeling regarding a possible travel mode choice "away from the habitual car use."These results will be presented in the following subsection, and discussion regarding their meaning will continue in Section 5 -Discussion.

Description and elaboration of choice models across segments
In the following subsections, an in-depth elaboration of choice modeling is presented for each particular segment.Our focus was on determining how sensitive participants were with respect to the various parameters of transport modes when planning everyday trips.Despite the initial framework presented in Figure 1, the actual sample in this study did not allow the mode choice model to be used with respect to public transport and intermodal mobility for Segment 1.As presented in Table 1, each respondent was presented with the choice of six travel modes and a seventh choice, "no alternatives."Only two travel modes (public transport and intermodal/MaaS) were analyzed here, and the reference category chosen was "none of the alternatives."Therefore, from Table 1, we only used Answers 1, 6, and 7.For Segment 1, we encountered a situation where there were not enough choices of categories of our interest.We assigned this fact to the identified respondent profile of Segment 1 respondents.As stated in Section 4.1, Segment 1 representing travelers who said they are very friendly towards shared-economy-related transport modes, such as carsharing, carpooling, shared scooters, and so on.We believe that the attitudes and characteristics of respondents in Segment 1 are why only 16 respondents in this segment (out of the total of 493 -see Figure 10) chose public transport or the intermodal mode of transport.Due to practical reasons and the nature of regression modeling, the model for these respondents could not be calculated with statistically significant results.Therefore, although a total of five response segments were initially identified, only Segments 2 through 5 will be analyzed below.To better understand the impact of the segmentation of our response base, we present here the regression results for each segment with reference and comparison to the results achieved for the whole sample (see Figures 12 through 20 and Tables 3, and 4. The analysis of commuter sensitivity to the price and time values for the alternatives offered is analyzed in the very beginning of each model section, and therefore, although presented in summary table and segment graphs, this is not discussed for each segment separately.This is because these are the parameters of a transport mode itself and, as will be presented below, these tended to be uniform across all sections (they were not influenced by sociodemographic nature of different user segments -their profiles).Note that in the figures depicting the radar plots of the regression results, odds ratios were used to present the influence of personal characteristics or route parameters on a preferred mode of travel.Therefore, this should be read as the ratio of influence that a given parameter had on the resulting transport choice (public transport or MaaS).For example, referring to the results for Segment 2 in Table 3, an odds ratio value of 0.733 means that the respondent, being male, decreased his chance of choosing public transport for his daily commute by a factor of 0.733 (women were generally more likely to consider change in our study).
The results elaborated only consider the estimated coefficients that are statistically at the level of α < 0.01.Therefore, only the magnitude of the effect of the variable on the dependent variable is presented and not its statistical significance.

Willingness to use public transport
According to the proposed workflow (see Figure 1, individual regression models were calculated for each dependent variable modeled Y n (in our case Y 1choice of public transport and Y 2 -choice of intermodal transport service/-MaaS).Thus, the results in this section will be divided into two parts, the first describing the regression results for each segment (and their differences) for the public transport mode, and the second constructed in a similar manner for the MaaS mode.In order to magnify and highlight the effect of different variables for a modeled choice, we analyzed the results for each segment with reference to the overall sample (the unsegmented respondent pool in our survey).Separate from the segmentation process and the modeling for each segment, an additional analysis of the sensitivity of participants was performed for the travel mode price and time parameters.To achieve this, price and travel time were variables that changed in each scenario presented (see Table 1.Since, due to the international nature of the study, the exact values of travel cost and time varied between the four countries studied, it was not possible to directly incorporate them into this study.Thus, these values were converted into discrete ordinal values (1/2/3) and, as such, were used to represent the increase in time or cost value in the scenarios presented.As expected, both price and time were seen as being important to the respondents when choosing their mode of travel.In the case of PT, this effect was relatively constant for each segment analyzed, and for price, the chances of using PT doubled when this mode was cheaper, while the effect of shorter travel times was two times less .This is an important finding, since it provides data supporting the assumption that possible shorter travel times are valued less than cheaper cost in the eyes of the respondents to this survey (see Figure 11).

Segment 2 -Car users by choice and beliefs
In Segment 2, habitual car users, the most important user characteristics that influenced the odds of using public transport were gender and properties such as the price and travel time.It is important to note that this segment, in general, reported not being interested in changing their mode of travel.Although respondents from Germany and Poland were about half as likely to choose public transport, in (constructed) Segment 2, country of origin did not have such a notable effect on their choice of travel mode.However, women were generally less likely to choose public transport, by approximately 25% less than the overall sample.The other factor that slightly negatively influenced the chances of choosing public transport was income level.The impact of education level of a respondent was similar in size but opposite in direction the effect on respondents in Segment 2. All the above-mentioned effects were statistically significant.The situation for Segment 3 was very different.Here, we observed a very strong impact of country of origin for the Czech Republic and the United Kingdom.Being from the Czech Republic increased the odds of choosing public transport as a travel mode by a factor of more than two, and being from the United Kingdom, almost 2.8.In case of Germany and Poland these effects were significantly smaller or absent.Income, again, played an even greater role as a factor in terms of choosing public transport, affecting the choice of PT in a positive way.The same can be said for women.Family size appeared to have led to slightly less public transport use.We could only hypothesize that there is a source in various family-related constraints and the complex routes that respondents take every day e.g., for transporting children to schools or kindergartens, though this was not examined in this study.Similar to unspecified users, we observed for Segment 4 a significant impact of country of origin on the stated choice of transport mode.This time, the highest impact on public transport use was observed for Germany and Poland, although the these odds were negatively affected, unlike in the cases of the Czech Republic and the United Kingdom.For Germany and Poland, being in this category reduced the chances of choosing PT by a factor of three, similar to levels observed for the overall sample.Although many other variables, such as household size, age, income, and education level, were significant, their importance in terms of public transport choice?was very small.Section 5, the group of participants who are the most in favor of PT in this study, were again significantly different mainly in terms of country of origin parameters.The biggest impact here was seen for German and Czech respondents.For both countries, being casual PT users further increased the odds of choosing this transport mode in the future.Again, Czech respondents (similar to Segment 3) showed increased (by a factor of 2) odds of traveling by PT, and German respondents showed a 4.315 factor increase.Higher levels of education also increased the chances of using PT by more than 20% in this study.This effect was almost three times greater than for the general sample (21.5% vs 8.6%).

Willingness to use intermodal transport services
The second choice modeled was the stated preference for MaaS (the intermodal mode) as transportation for hypothetical situations.For the purpose of modeling, again, the same choice experiment explained in Section 4.2.1 was used.Table 4 presents an overview of the modeling results for the overall sample and each segment, accordingly.
A brief comparison of Tables 3 and 4 shows a clear tendency for the opposite effect of particular variables on results.This is to be expected, since the two transport modes (PT versus MaaS) were presented to participants as direct alternatives to each other.Generally speaking, Czechs, Germans, and citizens of the United Kingdom were significantly less likely to choose the MaaS mode than PT.The same effect is clearly present for high travel times and costs, where travel cost is the more dominant of the two variables.A fascinating result was observed for the territory of residence parameter, where we can see that, overall, the larger the urban area a respondent came from, the lower the odds that they would use MaaS.This goes against the common assumption that multimodal travel and micromobility could be more developed, easier to use, and therefore beneficial in bigger cities.Another interesting result was the positive effect of household size on the odds of using MaaS .Education also had a modest (9.5%) but positive effect on the chances of traveling with MaaS.

The overall effect of the time and price factors
We analyzed the sensitivity of respondents to the price and time factors.Unlike for public transportation, the effect of travel time on choices was much less uniform for MaaS.Not only did shorter travel time not have a significant effect on habitual car users, it appeared to actually adversely affect people traveling shorter distances.This is an important finding, since it identifies a weaknesses of the MaaS travel mode in the eyes of the specific potential user groups.As for changes in travel costs, effects were much more unilateral than for travel times, but effects were not constant in magnitude.In general, how much MaaS costs appears to be a very important parameter to consider when planning a new MaaS service, especially for attracting new potential users to a service service [35].Segment 2 represented habitual car users.Unlike in the PT case, we observed very similar results for this segment compared to the overall sample.Features worth noting are even stronger effects of Czech and German origin (2.909 vs 2.252 for Czech and 4.721 vs. 4.199 for German) resulting in choosing MaaS than PT.Another difference worth noting is the insensitivity to the travel-time factor.In the overall sample, we observe an increase in odds of 1.474 while in the case of segment 2, time was not identified as a statistically significant parameter in our model.Again, as in the case of modeling PT preference, travel time and travel cost sensitivity will be analyzed separately at the end of this section.In Section 3, modeling results appeared to contradict our assumptions about characterization of these respondents as simply "undecided".During the segment profile achieved by finite mixture modeling, Segment 3 was identified as "unspecified".This segment, at first glance, might seem as if it would be the most similar of all segments to the overall sample.However, because of the extraction of the most characteristic response segments from the overall sample, the unspecified users differed quite a bit from the overall sample.First, Czechs in this segment were actually less likely to choose MaaS than public transit as a travel mode.The odds were 0.749 (roughly 3 to 4), while in the overall sample, the same odds were almost thrice as high.A similar decline in the odds of using MaaS can be seen for respondents from the United Kingdom.Here again, the odds dropped from 2.094 to 0.623 compared to overall sample.Another significant finding was the influence of age, where older respondents said they were more likely to use MaaS.Importantly, the actual travel mode parameters themselves appeared to have the greatest impact on the respondents in this segment, as suggested by our results.This, together with the identification of these users as "unspecified" (which can also be understood as not having a specific attitude toward any mode of transportation), might be hypothesized as the result of respondents are the most rational thinking commuters or those with the least prejudices among respondents in this study.Commuters without strong habits or preferences might be the most influenced by MaaS parameters.A quick peek into the results for PT modeling for Segment 3 (Section 4.2.1 or Table 3) seems to confirm these conclusions, although the effect for this segment was not as strong as it was in the PT model.Among the respondents who tend to commute nearby on an everyday basis, we observed the greatest dislike for the idea of traveling with MaaS of all segments in this study.This is probably related to their main identifying characteristic, short travel distance, where there is likely very little or no need/benefit to MaaS transport.Furthermore, these respondents stated they occasionally commute to work/school on foot.We observed that not only did most of the parameters have very little effect on MaaS travel decisions, but when they did have an impact, it was mostly negative.Such an effect is surprising, especially for the United Kingdom respondents, which might be result of various factors ranging from insufficient existing MaaS infrastructure, no need to use MaaS, or even such high satisfaction levels with existing PT or other options that MaaS is an attractive alternative, though this study did not examine these factors in greater detail.Our previous work has already suggested there is a negative effect on MaaS attractiveness if users are satisfied with PT services [36].Again, travel distance seems to be the most convincing argument to logically interpret the negative influence of the shorter travel time on MaaS adoption.Additional application and use of multi-modal transpiration might seem excessive and unattractive to these commuters.This is supported by identifying users in this segment who stated they are car travelers by necessity, meaning they are not particularly against public transport but they are unable to use it easily.Segment 5 -Casual public transport users Segment 5, casual public transport users, again provided results which support initial segment profiling.Here, satisfaction with existing PT services could also have played a significant role in the generally negative attitude towards a MaaS travel option.Note that most of the sociodemographic characteristics of participants in this segment had a negative effect on choosing MaaS.This is especially visible with age, where younger people were more eager to use new travel modes, while the older the respondent, they less willing they said they were to abandon occasional PT use in favor of MaaS.For people with a better incomes who might be see MaaS as being more convenient than regular PT, it appeared from our results that they were eager to consider MaaS options without considering price.At the same time, in general, casual public transport users tended to shift toward the MaaS mode if the cost/travel time conditions turned in favor of MaaS.This effect was especially strong for the case of the price variable, where cheaper MaaS service increased the odds of being used by a factor of over 3.5 (higher prices decreased the odds by 0.293).This is by far the biggest effect observed for this segment of respondents.

Discussion
Our study shows both parallels and differences in segmentation approaches and outcomes for MaaS user types when compared to the previous studies mentioned in Section 2.1.While categorization is mostly done within four (e.g., [13]) to five (e.g., [3]) segments, the characteristics of each segment in this study are different and influenced the distribution of respondents.With regard to the number of segments, the development of a "neutral" category is another difference (performed by [3], for example).Such a category, not used here, would bring with it the advantage of avoiding the dilution of segments, which also influences distribution as well as segment accuracy.Further, the categorization of sociodemographic values used here is more finely granular when compared to previous studies on MaaS adoption (see [3], [12], [13]).Nevertheless, in [12], we found parallels to our study in the characteristics for segment descriptions.This applies, for example, to the variables of socioeconomics and demographics (e.g., gender, age, income, or household structure).Considering segment types and names, we found that [3] characterized a segment as "unspecified users," as we did.[13].Car users were placed in two sub-segments, as we did in our study, but an additional segment of "MaaS curious" was not identified in our study.
It is important to note that our results about the choice of mode in this study (public transport or MaaS) per segment refer to a comparison against the odds of choosing a car as mode of transport.
Overall, taken as a whole, participants illustrated a tendency towards a higher probability of using MaaS than public transport.This effect is interesting, because public transport has been identified as the backbone of MaaS systems ( [37], [38], [39].We conclude that other modes of transport (or combinations of transport options) lead to a higher probability that people will use MaaS instead of public transport.A comparison between countries shows that the discrepancy (extrema) between non-probable public transport use and probable MaaS use is most pronounced in Germany.Here, participants were much more open to using MaaS than public transport.This could be because (more) MaaS systems might already be more established in Germany (e.g., Jelbi is a popular example).[40] showed that people in northern and central Europe have more knowledge about new mobility alternatives (in the case of their study, car sharing) that elsewhere in Europe, which may support our findings for Germany.Overall, however, participants in our study shared the same pattern of sensitivity towards pricing, both for public transport and MaaS.The higher the price for public transport and MaaS, the lower the willingness to use either option.Others have confirmed this finding, such as ( [39], p. 138).Since MaaS can be considered to be a new mobility option, using MaaS as an "innovation adoption decision" ( [39]), the willingness to pay for MaaS options might illustrate that people are conservative/cautious or sensitive about price points than when considering public transport, which is a long-standing transportation option in the countries observed here.This effect was supported in previous studies ( [41], [42]) that found that perceived costs influence mobility use mainly in the first stage of the life cycle (MaaS can be considered being in this phase), while in the maturity phase (where public transport is classified), people are less concerned about prices ( [41]).

Female Car Users by Choice and Beliefs: using PT and MaaS less often
When looking at Car Users by Choice and Beliefs, the results showed that safety plays the most important role when choosing modes of transport.Doubts about public transport safety, especially for the women in this study, might be the reason behind these concerns ( [43]) for decisions about both public transport and MaaS use ( [44]).Further, prior studies have proposed that family responsibilities might be another underlying reason contributing to perceptions about safety when travelling ( [45]).Our study confirmed prior findings that families with younger children are less likely to use MaaS than other options ( [8]).Furthermore, [46] found that mothers always depend on cars because they perceive travelling with children this way to be easier and more convenient.This might apply for the women in our study as well, who may have found cars to be more convenient than mobility options, but investigating this question in more detail was beyond the scope of our study.For Female Car Users by Choice and Beliefs, the preference for private cars was so noticeable that even in lower income situations, women probed in this study would rather use a car than PT.Furthermore, a fundamental aversion to MaaS has been noticed in prior studies showing car use is important aspect influencing peoples' MaaS use decisions ([3], [13]).

Unspecified Users: High price sensitivity
The "Unspecified Users" group in our study was characterized by a high number of people 40-50 years of age.They were more sensitive towards prices-especially in public transport prices-than the whole sample.When prices fell, participants in this group were more likely to use public transport and MaaS options.A reason for this, according to [39], is that public transport seems to be the preferred mode for particular age groups, and this may apply to these Generation X participants.When the most frequent used mode of transport in daily life is negatively affected by price, a higher sensitivity is the consequence.As for income in our study, higher incomes improved the chance that public transport would be used, but not of MaaS.This lower probability of MaaS use for Unspecified Users might be due to a lower technology affinity as well as "attachment to the business-as-usual case" ([47], p.12), but probing this was also beyond the scope of this study.Another possible reason proposed in prior studies is that older people are often undecided about the potential of MaaS use ( [36]).For this group, higher travel times led to lower chances of public transport and MaaS use.We conclude that the basic factors impacting use of a mobility options, such as price or travel time ( [48]) -regardless of the respective mobility form (public transport or MaaS) -are basically the drivers of an increase in usage for the Unspecified Users group.Further research is necessary to test such assumptions and to identify additional characteristics of this group.As an example, people living in Czech-Republic and United Kingdom both reported a higher chance of using PT but a lower probability using MaaS.In general, people in the Czech Republic are known for being satisfied with the current quality of PT [49].In general, previous research has shown that people who are satisfied with PT and do not use it often do not feel the need for MaaS, since they are already satisfied [36], confirmed also by [50] (a UK study) which show similar results, however noting that PT usage increases for interurban travels but that private cars are often chosen for suburban travel.

Habitual Car Users: MaaS brings higher comfort, by necessity
For the Habital Car Users by Necessity segment, it is clear from our study that household size matters when considering public transport and MaaS use.The bigger the household in our study, the more likely the household member was to use MaaS; the smaller the household, the more likely public transport would be chosen.We assume that Habitual Car Users by Necessity participants perceived MaaS to be more practical and functional than public transport, but this was beyond the scope of our investigation.It might be because MaaS systems based on car use (e.g., carsharing, ride pooling and hailing) are perceived as more advantageous than by public transport.This contradicts a study by [51], which found that in MaaS containing both cars and public transport, only the later was perceived to have benefits.In our study, longer travel times for this group led to higher MaaS use and lower public transport use.MaaS was perceived as being more comfortable than public transport; travelling longer with MaaS (and probably Habitual Car Users by Necessity participants primarily were thinking about car use) increased the likelihood of using MaaS.This finding resonates through the literature, where car users show the least likelihood of using MaaS ( [16]) "because they usually believe in the necessity of car ownership as a family with children: ( [3]).Further, they might have an addiction to private vehicle travel ( [8]).These "car" advantages were observed in our study, with this group being very "car sensitive".

Frequent Public Transport Users: similar as Car Users by Choice and Beliefs, however sensible to prices and educational levels
The Frequent Public Transport Users group had the most participants in Germany and the Czech Republic.Our results showed that the higher the income, the more likely participants were to use MaaS, but not public transport.This finding suggests that this group uses public transport out of conviction, regardless of the financial situation, though deeper investigation was beyond scope of this study.We found parallels of this behavior in the results for the Car Users by Choice and Beliefs group (i.e., preference for a car use regardless of income).In comparison to other groups, Frequent Public Transport Users were more sensitive to higher MaaS prices, confirming [52] previous research that revealed a lower willingness to pay for MaaS in the group of frequent public transport users.The main reason is, according to previous research, is that public transport users overlap with groups that are sensitive to travel prices and tend to have a lower incomes ( [8]).Further, our study revealed that Frequent Public Transport Users with lower levels of education and a tendency to be unemployed were more likely to use MaaS.Different conclusions were drawn by [53], who found that full-time employees were more likely than others to use MaaS, while retirees are least likely considering MaaS.[13] found those with higher education were the core of so-called "MaaS-lovers".

Conclusion
While sustainable mobility is currently a hotly-debated societal topic, discussion is often reduced to debates about abandoning fossil fuels and using electric vehicle or hydrogen-powered vehicles.While such discussions might lead to more efficient engines or spur renewable energy innovation, we feel not enough attention is given changes in our travel behaviors.A trip not made is far better for the environment than a trip in an electric vehicle.The traditional approach to mode choice needs to change and include new options such as micromobility and MaaS, the latter of which is user-not car-centric.This brings with it the potential for through policy integration, which would really make an impact on improving the sustainability of urban transportation systems.This paper provides a better understanding of how the over 6,ooo urban daily commuters in our multilevel study of four European countries (Germany, the United Kingdom, Poland, and the Czech Republic) made choices regarding their likelihood to use public transport and MaaS.The key underlying objective was to learn more about what circumstances could persuade different kinds of participants to abandon cars and use alternatives.By using finite mixture model, we were able to group respondents, based on their reported travel habits, into five different segments (or user profiles).For each segment, using the Logit-Kernel model, we analyzed what variables could positively or negatively influence participants' willingness to use alternative travel modes instead of cars.One of the most significant findings is related to the differences between participants living in the four countries surveyed.Although we confirmed numerous variables influencing the chances of using MaaS among participants, we found that their effect was not the same across national boundaries.This led use to the conclusion that, although there are increasing numbers of research studies on MaaS and public transport preferences, conclusions drawn from local studies must be confirmed before they are generalized and applied to a different setting.
Looking at the factors that have the potential to increase the attractiveness of public transport and MaaS, this study confirmed prior findings that both time and travel cost are very important factors that influence the choice of transportation.In our study, the effect of travel time and price on public transport was observed rather uniformly across the different segments, with higher cost reducing the likelihood of PT travel.Longer travel times did not impact PT choices as much as cost.For decision making regarding MaaS, we observed more differences across the segments, apparently influenced by the attitudes of the respondents.For MaaS choices, prices played a greater role than any sociodemographic characteristic.Thus, economical value (as perceived by users) likely should play a key role in MaaS system design.
This study also identifies other factors that can make choosing MaaS more attractive, but different user segments in our findings displayed different behaviors.Our results imply that it may be difficult to influence the behavior of those who use cars by choice and beliefs, but there are differences even within this segment that varied across the countries studied.Younger people appear to be more willing to use MaaS than habitual car users (Segment 4 in this study) and casual public transport users (Segment 5), but this was not the case for unspecified users (Segment 3).People with higher incomes who casually use public transport (Segment 5) were more likely to choose MaaS, but other factors such as higher education had an opposite effect.
Our complex approach, combining several mathematical modelling tools, was shown to provide insight into mode choice behavior.We were able to identify distinct participant segments (user groups) and then study the most significant factors influencing their decision making.This is important not only from the research perspective, but also can be useful to decision makers, who can target their mobility awareness campaigns more effectively to certain kinds of users or create new and more direct policies (e.g., low pricing) to support alternative travel modes.
Dr. Michal Matowicki is a deputy head of the applied mathematics department at Czech Technical University in Prague, Faculty of Transportation sciences.His PhD explored the behavioural patterns of drivers in relation to Variable Message Signs on Czech highways.Nowadays he maintains his interest in behavioural analysis and modelling in transportation, with a focus on the analysis of panel data and discrete choice modelling with regard to the choice of mode of transport.
Dr. Pavla Pecherkova is a Senior Lecturer at Czech Technical University in Prague, Faculty of Transportation sciences.Her field of research is statistics including the bayesian approach and modelling of stochastic systems.She is the author of multiple interdisciplinary publications with significant contributions in the analysis of data.
Dr. Marco Amorim is currently a Senior Scientist in the mobility ecosystem team at the Mobility and Innovation Unit of the Fraunhofer Institute for Industrial Engineering IAO, Stuttgart.His current topics address user behavior and its impacts on transport system by modelling transport demand, transports usage and emissions.Previously, he was a Researcher with a focus on human behaviour in road safety at the Research Centre for Territory, Transports and Environment (CITTA), a part of the Engineering School of the University of Porto.His research interests include urban mobility (public transit, MaaS, and emissions topics), optimization, simulation, and user behavior, and preferences modeling.
Mira Kern is a researcher at University of Stuttgart -Institute of Human Factors and Technology Management.Her current research area is dedicated to user and acceptance research in new mobility offers and systems.The main focus of her work lies in quantitative data collection and analysis in order to understand human travel behavior and preferences.
Dr. Ivan Nagy is an Associate Professor at Czech Technical University in Prague -Faculty of Transportation Sciences.The main focus of his research work is the modelling, identification and control of large-scale transport systems under uncertainty.Estimation of the unknown model parameters is performed using a Bayesian identification algorithm.Based on the identified model and the control synthesis performed, either decision support is generated for traffic operators or the results can be used for direct automatic control of local traffic systems.Another area of his interest is research in data-mining methods, mainly focused on the analysis of discrete traffic-related data.Nicolaj Motzer is a researcher at University of Stuttgart -Institute of Human Factors and Technology Management.The focus of his current work is user and acceptance research in the context of mobility ecosystems (MaaS, public transport) by applying empirical methods.Another area of his research interest is prodct as well as business model development and evaluation, both, in national and international projects (industry and public).
Prof. Ondrej Pribyl is a researcher at Czech Technical University in Prague where his work focuses on highway traffic management systems, transport sustainability and urban transportation systems including smart and livable cities.In his earlier research, he focused on activity-based transport modelling which stays his area of interest today.

FinalFig. 1
Fig.1The workflow of processes during research execution and construction of Multilayer Hybrid Model

Fig. 2
Fig. 2 The results of AIC evaluation for Finite Mixture Modelling for data segmentation

Fig. 3 Fig. 4
Fig. 3 Histogram of respondents average age in segments

Fig. 10
Fig. 10 Histogram of usable response numbers in each segment

Fig. 11
Fig. 11 Radar plot with odds ratios resulting from the influence of price (left) and time (right) factors on public transport choices.

Fig. 12
Fig. 12 Radar plot with odds ratios resulting from modeling in Segment 2

Fig. 13
Fig. 13 Radar plot with odds ratios resulting from modeling in Segment 3

Fig. 14
Fig. 14 Radar plot with odds ratios resulting from modeling in Segment 4

Fig. 15
Fig. 15 Radar plot with odds ratios resulting from modeling in Segment 5

Fig. 16
Fig. 16 Radar plot with odds ratios resulting from influence of price (left) and time (right) factors on MaaS choice.

Fig. 18
Fig. 18 Radar plot with odds ratios resulting from modeling in Segment 3

Fig. 19
Fig. 19 Radar plot with odds ratios resulting from modeling in Segment 4

Fig. 20
Fig. 20 Radar plot with odds ratios resulting from modeling in Segment 5

Table 1
Example of attributes and values for the German sample

Table 2
Explanatory (independent)variables in sensitivity analysis of choice

Table 3
Odds ratio of public transport preference for each segment for the different commuter or scenario parameters examined

Table 4
Odds ratio of MaaS preference in each segment, for different examined commuter or scenario parameters