Developing a Mobility as a Service Status Index: A Quantitative Approach Using Mobility Market and Macroeconomic Metrics

Abstract

Despite the growing adoption of Mobility as a Service (MaaS) in urban transportation systems, standard monitoring methods for evaluating its impact and effectiveness still need to be developed. This study proposes a quantitative state of MaaS analysis based on mobility market indicators and macroeconomic metrics to generate a MaaS Status Index (MSI). The intention is to introduce a standardised quantitative methodology for systematically assessing and comparing the state of MaaS in urban mobility systems. The MSI aims to quantitatively capture the economic, social, technological, and infrastructural conditions relevant to MaaS implementation. The methodology includes four steps: identifying relevant mobility markets, defining mobility market metrics, integrating macroeconomic metrics, and deriving the MSI formula. We apply the MSI methodology to the Austrian mobility market as a case study, demonstrating its practicality in assessing MaaS readiness and highlighting specific challenges and opportunities within the Austrian mobility system. The analysis covers the present (2017–2022) and the projected future (2023–2028). The findings indicate that the proposed MSI is an effective tool for evaluating the readiness of MaaS implementation.

1. Introduction

1.1. Relevance and Problem Statement

Monitoring the development of new mobility concepts, such as Mobility as a Service (MaaS), is crucial to understanding their current status, potential, and necessary actions for implementation. Therefore, a standardised and generalisable methodology is required to generate comparable results in various contexts. A review of existing MaaS indices reveals the need for a standardised index incorporating country-specific mobility market indicators and economic metrics. This study proposes the creation of a quantitative MaaS Status Index (MSI) based on statistical analyses of country-specific mobility markets and additional economic indicators. The hypothesis is that a comprehensive range of financial and macroeconomic indicators reflect a country’s economic, social, technological, and infrastructural conditions. Monitoring and evaluating these indicators over several years can indicate the development of a country’s mobility system, particularly concerning MaaS. MaaS is a transformative concept leveraging technology to provide a seamless, integrated, and user-centric approach to urban mobility [1]. It includes various transport modes and services, allowing users to plan, book, and pay for their journeys through a single platform [2]. This paper aims to conduct a multi-year analysis of economic and macroeconomic indicators and to present a methodology for generating an MSI based on these analyses.

1.2. Literature Review

The global market volume in the field of MaaS was USD 42 billion in 2018 and is projected to reach approximately USD 372 billion by 2026 [3]. The MaaS ecosystem involves stakeholders, including public and private transport operators, and requires a clear understanding of their roles and interactions [4]. The successful implementation of MaaS is contingent on developing appropriate business and operator models, which are influenced by regulations, market size, and stakeholder engagement [5]. MaaS aims to bridge the gap between public and private transport operators on a city, intercity, and national level [4].

Refs. [6,7] highlight the potential for MaaS to reduce car dependency and provide a more flexible transport system, with [8] noting that youth, current public transport users, and flexible travellers are likely to be early adopters. However, ref. [7] emphasises the need for a shared vision, appropriate business models, and collaboration within the MaaS ecosystem. Ref. [2] proposes a dynamic adaptive approach to MaaS implementation involving continuous monitoring and responsive actions. Finally, [9] underscores the importance of incorporating travellers’ expectations, such as route optimisation and real-time information, into MaaS technologies. These studies collectively suggest that successful MaaS implementation will require a combination of factors, including a shared vision, collaboration, and a user-centric approach. Consequently, implementing MaaS faces various challenges, including technical, regulatory, financial, and social issues [10]. These challenges can impact urban mobility and societal changes [1]. Ref. [11] highlights the need for a detailed planning process to address these challenges, as demonstrated in the MaaS Athens demo. Also, ref. [2] suggests a dynamic adaptive policymaking approach, such as dynamic adaptive planning (DAP), to address uncertainties and enhance the likelihood of MaaS success.

Various international approaches exist to assess MaaS in different geographical regions and in terms of maturity or readiness. These approaches are quite heterogeneous. Some evaluate the quality of urban mobility systems (security, accessibility, affordability, innovativeness, or convenience), while others bring together relevant mobility data from different sources and address data openness and applicability. Other approaches measure the ratio between the number of bikes and cars and the population and calculate a density measure of bike and car sharing provision. Ref. [12] focused on identifying methodologies for assessing the sustainability impact of potential MaaS implementations from a whole system perspective. Their review covered simulation tools and models capable of assessing MaaS at a city level, highlighting gaps in capturing interactions such as demographic changes, mode choice, and land use in a single framework for exploring MaaS scenarios’ impact on sustainable mobility. Ref. [13] explored motivational acceptance factors for MaaS adoption using qualitative, in-depth interviews with potential end-users. Their research postulates a structural causal equation model capturing motivational mechanisms behind the intention to adopt MaaS.

Nevertheless, developing a quantitative MaaS index is crucial for assessing MaaS to understand its potential to transform urban mobility and to understand a city’s preparedness for MaaS implementation [14]. A MaaS index should consider critical criteria such as transport operators’ data sharing, citizen familiarity, policy and regulation, ICT infrastructure, and transport services [15]. Additionally, assessing potential MaaS partners should include criteria such as availability, customer base, technical maturity, business value, financial status, CO₂ footprint, social responsibility, and quality of life [16]. A comprehensive review of existing MaaS schemes can further inform the development of this index, providing insights for transport operators and authorities [14]. Refs. [15,17] developed indices to measure the readiness and integration of MaaS in urban areas. Ref. [15] assesses a city’s readiness based on five dimensions, while He’s integration index focuses on the functions of MaaS applications, transport modes, tariff structure, and organisational aspects. These indices provide valuable tools for decision-makers to evaluate and compare MaaS services. Refs. [14,17] scheme and emphasise the importance of integration in making MaaS more appealing to travellers. A comparison of different MaaS readiness indices is shown in

Table 1. Comparison of existing MaaS indices.

Index	Release	Authors	Indicator Categories	Focus of Index
Deloitte City Mobility Index	2019	Ref. [18]	Performance and resilience Vision and leadership Service and inclusion	Assessment of a city’s readiness for future mobility (not exclusively addressing MaaS)
Urban Mobility Innovation Index 2021	2023	Ref. [19]	Readiness Deployment Liveability City profiles	Assessment of a city’s innovation ecosystem in urban mobility (not exclusively addressing MaaS)
MaaS Readiness Level Indicators for local authorities	2017	Ref. [20]	Strategic readiness Internal use Shared use Shared understanding	MaaS readiness for local authorities
MaaS Maturity Index	2018	Ref. [15]	Transport operators’ openness and data sharing Policy, regulation, and legislation Citizen familiarity and willingness ICT infrastructure Transport Services and infrastructure	Maturity of a geographical area towards MaaS
MaaS and sustainable travel behaviour	2020	Ref. [21]	Car dependence Trust Human element externalities Value Cost	Factors underpinning the uptake and potential success of MaaS as a sustainable travel mechanism
Broader overview of MaaS	2017	Ref. [22]	Functionalities Technologies Representative projects Attitudes and mind Infrastructures and ICTs Autonomous and connected vehicles Sharing economy	Discussion of the functional and technical aspects of MaaS systems
MaaS Readiness Index (MRI)	2016	Ref. [23]	Availability of transport services Customer demand Government support and regulatory environment	Readiness of a geographical area towards MaaS
Integration index for MaaS	2021	Ref. [17]	MaaS application Involved transport modes Tariff structure MaaS-related organisations	Integration of MaaS in urban areas

.

Ref. [18] is a tool developed to assess cities’ readiness for the future of mobility. The primary objective of the index is to measure the degree of cities’ preparedness for evolving requirements in urban mobility, with a particular emphasis on data-driven decision-making. The index enables a comparison among different cities worldwide regarding their ability to address the challenges and opportunities in the future of mobility. The index considers various criteria and factors influencing mobility in a city, including the availability of digital infrastructures, the integration of new technologies in the transportation sector, the efficiency of public transportation, the application of smart city principles, and the regulatory framework for innovative mobility solutions. By evaluating these factors, ref. [18] provides a comprehensive analysis of cities’ readiness for the future of mobility. While ref. [18] offers insights into the general preparedness of cities in mobility, it does not explicitly target MaaS.

The research by [19] discusses the Urban Mobility Innovation Index (UMii) as a tool that engages with 40 cities globally through direct interaction and qualitative interviews to highlight the latest innovations and identify areas for improvement. While not explicitly focused on MaaS, the paper emphasises the importance of innovation in cities and outlines critical success factors for highly innovative cities. The study reveals that such cities establish clear long-term goals, engage closely with citizens and stakeholders, overcome regulatory and financial barriers, and assess technological innovations for their broader impact on people and the environment. In the context of developing an MSI, the UMii framework and its insights serve as a valuable reference for understanding the innovation landscape in urban mobility and identifying factors that contribute to successful and forward-thinking cities in transportation.

Ref. [20] presents MaaS Readiness Level Indicators for local authorities (MRLI) and discusses the current efforts of several European cities to support the establishment of new multimodal transport services and the challenge of creating high-performance service packages to shift mobility behaviour towards sustainability. In the study, MaaS is recognised as a success factor in achieving cities’ goals for sustainable mobility and changing citizens’ transport behaviour. To facilitate mutual learning among local authorities, readiness level indicators for MaaS development have been identified. These indicators showcase the current situation of local authorities in preparing their environment for MaaS. These readiness level indicators serve as a starting point for local authorities and complement recent publications on MaaS. Ref. [20] emphasises the importance of understanding the local context and provides links to related publications. In developing a MaaS index, the readiness level indicators offer valuable insights into the diverse aspects of MaaS development and provide a foundation for assessing the preparedness of local environments for MaaS implementation. However, ref. [21] raises concerns about the potential challenges in promoting responsible MaaS usage, including car dependence, trust, human element externalities, value, and cost. Ref. [22] provides a broader overview of MaaS, discussing its functionalities, technologies, and the role of physical transportation infrastructures and ICT. Ref. [23] introduces the MaaS Readiness Index, a conceptual framework designed to assess the preparedness of a city or country for MaaS. This index encompasses three key themes: the accessibility of transport services, the level of customer demand, and the extent of government support and regulatory infrastructure. This framework operates on a scale of 5 maturity levels of openness, which can be evaluated for MaaS Customers, MaaS Providers, Data Providers, and Transport Operators.

1.3. Novelty of This Research

Examining previous MaaS indices reveals the need for a standardised index incorporating existing country-specific mobility markets and (macro)economic indicators. This research makes a significant contribution to the existing body of work on Mobility as a Service (MaaS) by introducing a standardised and comprehensive MaaS Status Index (MSI) that incorporates both mobility market metrics and macroeconomic indicators. While existing MaaS indices, such as those proposed by [15,17], focus primarily on factors like transport operators’ openness, ICT infrastructure, and citizen familiarity, this study expands the scope by including socioeconomic variables, such as GDP per capita, transportation infrastructure investments, urbanisation rates, and population demographics, which are seldom covered in current indices. These indicators are critical for understanding the broader economic and traffic infrastructural conditions that influence the readiness and potential for MaaS implementation across different regions. Furthermore, the integration of macroeconomic metrics, such as investment in road and rail infrastructure and transport-related consumption expenditures, offers a more holistic perspective on the factors driving MaaS adoption. This research also uniquely quantifies the influence of non-mobility-specific sectors, providing insights into how broader economic trends can affect the sustainability and scalability of MaaS services. The proposed MSI enables a longitudinal analysis by comparing two distinct time periods (2017–2022 and 2023–2028), offering a temporal perspective on the evolution of MaaS readiness. The results not only highlight the dynamic nature of mobility markets but also show how economic conditions and mobility services interact to support or hinder the adoption of MaaS. This approach distinguishes this research from previous works, which are often limited to static, one-dimensional assessments. Thus, this paper aims to close this research gap by depicting a methodology to retrieve a quantitative and standardised MaaS index based on a multi-year analysis of developments in mobility markets and economic trends. Due to the diversity of the indicators considered, we expect that these indicators reflect the economic, social, technological, and infrastructural framework conditions relevant to implementing MaaS.

2. Methodology

Our methodology for generating a standardised MaaS Status Index (MSI) involves four main steps. First, we identify existing mobility markets and classify them according to the following dimensions of vehicle usage: shared, unshared, individual, and collective. Second, we define mobility market metrics for each market to represent their financial dynamics. These metrics include revenues, vehicle costs, sales, number of users, user penetration rate, and percentage of online sales. Third, we integrate macroeconomic metrics to contextualise the broader socio-economic landscape. These metrics include transportation infrastructure investments, urbanisation rates, and GDP per capita. Considering these metrics, we aim to understand how economic conditions and infrastructure development influence MaaS adoption. Fourth, we derive an MSI formula incorporating mobility market and macroeconomic metrics. This data-driven methodology is applied to a dataset containing mobility market and economic data for Austria.

2.1. Definition of Mobility Markets

We classify existing mobility markets based on the characteristics of vehicle usage: shared, unshared, individual, and collective. Shared modes include car sharing, bike sharing, and E-scooter sharing, emphasising collaborative utilisation. Unshared modes pertain to private vehicles, such as personal cars and motorcycles, which are not shared among multiple users. Individual modes encompass active transportation, such as walking or biking, emphasising personal mobility. Collective modes include public transportation options like buses, trains, and aeroplanes, emphasising group travel dynamics.

Table 2. Stakeholder-based assessment of mobility markets in the context of MaaS.

Category	Mobility Market	Benefit for MaaS B *	Integration with MaaS Ecosystem I **	Mobility Market Weight W ***	Explanation
Shared individual trips	Car rental	1	3	3	Flexible, shared, reduces ownership burden
	Ride hailing	1	3	3	On-demand, promotes shared use
	Taxi	1	3	3	On-demand, promotes shared use
	Car Sharing	1	3	3	Promotes shared use
	Bike sharing	1	2	2	Short-distance travel, promotes shared use
	E-Scooter sharing	1	2	2	Last-mile connectivity, promotes shared use
	Moped sharing	1	2	2	Promotes shared use
Shared collective trips	Bus	1	3	3	Efficient group travel, aligns with MaaS
	Train	1	3	3	Mass transit, efficient, aligns with MaaS
	Aeroplane	−1	1	−1	Long-distance travel, less aligned with MaaS
	Public transportation	1	3	3	Shared transportation, aligns with MaaS
Unshared individual trips	Private car (fuel-based)	−1	1	−1	Unshared use, less aligned with MaaS
	Private car (electrified)	−1	1	−1	Unshared use, less aligned with MaaS
	Motorbike	−1	1	−1	Unshared use, less aligned with MaaS
	Bicycles	1	3	3	Unshared use, sustainable, last-mile travel

* Benefit for MaaS (B) based on stakeholder assessment: beneficial (1), not beneficial (−1). ** Integration with MaaS ecosystem (I) based on stakeholder assessment: high (3), moderate (2), low (1). *** Mobility market weight (W): the product of B and I.

illustrates the identified mobility markets separated into the proposed categories of shared individual trips, shared collective trips, and unshared individual trips. The table also shows their relevance for MaaS (column “Benefit for MaaS”, B), with 1 indicating the mobility market is beneficial for MaaS and −1 indicating the mobility market has no significant relevance to MaaS. The column “Integration with MaaS ecosystem” (I) assesses how well each transport mode integrates into the broader MaaS ecosystem. The integration level is categorised as follows:

• High (3): Strong integration with the MaaS ecosystem. The transport mode aligns well with the principles and goals of MaaS, enhancing its effectiveness.
• Moderate (2): Moderate level of integration with the MaaS ecosystem. While the transport mode contributes to the MaaS ecosystem, certain limitations or considerations may exist.
• Low (1): Limited integration with the MaaS ecosystem. The transport mode may need to align better with MaaS principles or have characteristics that make integration challenging.

The column “Mobility market weight” (W) represents the weighted assessment of each mobility market’s relevance to MaaS. W is derived from the multiplication of B and I values, providing a quantitative measure that combines the perceived benefit for MaaS with the level of integration. The column “Explanation” briefly explains why each transport mode is categorised as beneficial or not beneficial for MaaS, considering factors such as flexibility, shared usage, sustainability, and alignment with MaaS goals. It helps readers understand the reasoning behind the assigned integration level.

The determination of whether a metric is beneficial for Mobility as a Service (MaaS) is grounded in both empirical evidence and stakeholder assessment, which was part of the project “Mobility Ambassadors as Game Changers”, conducted over one semester at TU Wien in 2021. Specifically, the assessment of metrics as ‘beneficial for MaaS’ considers their alignment with the core principles of MaaS, including shared mobility, digital infrastructure integration, user-centric services, and sustainability. As outlined in Table 2,

Table 3. Mobility market metrics.

Mobility Market Metric	Description
Revenues (R)	Annual revenues within mobility market, in Euros.
Annual revenue per user (ARPU)	Average annual revenue generated per paying customer, in Euros.
Vehicle costs (VC)	Annual vehicle costs for users in Euros.
Vehicle sales (VS)	Annual mobility market’s vehicle sales volume in Euros.
Number of users (U)	Annual number of paying users.
User penetration rate (UPR)	Annual percentage of paying customers in relation to total population.
Online sales channels (SC)	Annual percentage of bookings or reservations that occur online.
Autonomous driving level 2 (AL2)	Annual percentage of vehicles with autonomous driving level 2.
CO2 emissions (CO2)	Annual average CO2 emissions in grammes CO2 per kilometre.
Number of charging stations (CS)	Annual number of existing charging stations.
Revenues from charging stations (RCS)	Annual revenues from charging stations.
Shannon Index (H)	Market shares of mobility markets.

and

Table 4. Stakeholder-based considerations on macroeconomic metrics.

	Macroeconomic Metric	Benefit for MaaS B *	Integration with MaaS Ecosystem I **	Metric Weight towards MaaS W ***	Explanation
Population	Total population	1	1	1	Beneficial for MaaS as it indicates a larger potential user base. Not fully integrated due to potential challenges in managing larger populations.
	Urbanisation rate	1	3	3	Beneficial for MaaS, as urban areas provide a concentrated market for MaaS services. High integration as urban areas often have better conditions for MaaS implementation than rural areas.
	Number of households	−1	2	−2	Not beneficial for MaaS as it might indicate dispersed demand. Moderately integrated as households might use MaaS differently.
	Proportion of the younger population (<44 years)	1	3	3	Beneficial for MaaS, as younger populations often adopt new mobility trends more readily. Highly integrated due to the tech-savvy nature of the younger demographic.
	Proportion of the older population (>44 years)	1	3	3	Beneficial for MaaS to cater to elderly mobility needs. Highly integrated due to the potential for demand in assisted mobility services.
Transportation economics	GDP per capita	1	3	3	Beneficial for MaaS, as a higher GDP indicates higher potential spending on mobility services. Highly integrated as wealthier regions may be able to afford better MaaS infrastructure.
	Consumption expenditure, transportation (per capita)	1	3	3	Beneficial for MaaS, as higher spending on transportation suggests a willingness to invest in mobility solutions. Highly integrated as spending aligns with MaaS consumption.
	Consumption expenditure, vehicle purchase (per capita)	−1	2	−2	Not beneficial for MaaS, as lower spending on vehicle purchases indicates reliance on shared mobility. Moderately integrated due to varying spending patterns.
	Consumption expenditure, transportation services (per capita)	1	3	3	Beneficial for MaaS, as higher spending on services suggests reliance on shared and on-demand mobility. Highly integrated due to service-oriented spending.
	Price level index, transportation	−1	2	−2	Not beneficial for MaaS, as a lower price level in transportation encourages the adoption of cost-effective mobility options. Moderately integrated as pricing can affect adoption.
Mobility	Airline passengers	−1	1	−1	Not beneficial for MaaS, as air travel is not directly related to MaaS. Low integration as it represents a different mode of transport.
	Departures of airlines in thousand	−1	1	−1	Not beneficial for MaaS, as air travel does not directly impact MaaS services. Low integration due to the different nature of air travel.
	Railway tracks in million metres	1	3	3	Beneficial for MaaS, as a well-developed rail infrastructure supports integrated multimodal transportation. Highly integrated due to the potential for seamless connectivity.
	Rail passenger kilometres per capita	1	3	3	Beneficial for MaaS, as higher rail usage indicates a preference for public transportation. Highly integrated as it may reflect a shared mobility mindset.
	Road passenger kilometres per capita	1	2	2	Beneficial for MaaS, as higher road usage may indicate demand for shared mobility services. Moderately integrated due to the prevalence of road-based transport.
	Rail passenger kilometres in trillion	1	3	3	Beneficial for MaaS, as a high volume of rail passenger kilometres suggests a robust rail network. Highly integrated due to the potential for efficient mass transit.
	Road passenger kilometres in trillion	1	3	3	Beneficial for MaaS, as a high volume of road passenger kilometres suggests a demand for various mobility solutions. Highly integrated due to widespread road-based transport.
Transportation infrastructure investments	Investments in airport infrastructure (% of GDP)	−1	1	−1	Not beneficial for MaaS, as airport investments are more relevant to air travel. Low integration as it primarily supports a different mode of transport.
	Maintenance of airport infrastructure (% of GDP)	−1	1	−1	Not beneficial for MaaS, as airport maintenance is more relevant to air travel. Low integration as it primarily supports a different mode of transport.
	Investments in railway infrastructure (% of GDP)	1	3	3	Beneficial for MaaS, as investments in rail infrastructure support integrated transportation solutions. Highly integrated due to the potential for seamless connectivity.
	Maintenance costs of railway infrastructure (% of GDP)	−1	1	−1	Not directly beneficial for MaaS, as maintenance costs do not directly impact MaaS services. Low integration as it represents a different aspect of infrastructure.
	Investments in road infrastructure (% of GDP)	1	3	3	Beneficial for MaaS, as investments in road infrastructure support various mobility solutions. Highly integrated due to widespread road-based transport.
	Maintenance costs of road infrastructure (% of GDP)	−1	1	−1	Not directly beneficial for MaaS, as maintenance costs do not directly impact MaaS services. Low integration as it represents a different aspect of infrastructure.
	Investments in railway infrastructure in billion Euros	1	3	3	Beneficial for MaaS, as investments in rail infrastructure support integrated transportation solutions. Highly integrated due to the potential for seamless connectivity.
	Maintenance costs of railway infrastructure in billion Euros	−1	1	−1	Not directly beneficial for MaaS, as maintenance costs do not directly impact MaaS services. Low integration as it represents a different aspect of infrastructure.
	Investments in road infrastructure in billion Euros	1	3	3	Beneficial for MaaS, as investments in road infrastructure support various mobility solutions. Highly integrated due to widespread road-based transport.
	Maintenance costs of road infrastructure in billion Euros	−1	1	−1	Not directly beneficial for MaaS, as maintenance costs do not directly impact MaaS services. Low integration as it represents a different aspect of infrastructure.
	Investments in airport infrastructure in million Euros	−1	1	−1	Not beneficial for MaaS, as airport investments are more relevant to air travel. Low integration as it primarily supports a different mode of transport.
	Maintenance costs of airport infrastructure in million Euros	−1	1	−1	Not beneficial for MaaS, as these costs are primarily tied to supporting air travel rather than integrated mobility solutions. Low integration since it addresses upkeep rather than enhancing multimodal transport options.
ICT	Smartphone Penetration (% of population)	1	3	3	Beneficial for MaaS, as higher smartphone penetration indicates a tech-savvy population open to mobile-based services. Highly integrated due to the reliance on smartphones for MaaS.
	Internet Penetration (% of population)	1	3	3	Beneficial for MaaS, as higher internet penetration indicates a connected population. Highly integrated as MaaS often relies on internet connectivity.

* Benefit for MaaS (B) based on stakeholder assessment: beneficial (1), not beneficial (−1). ** Integration with MaaS ecosystem (I) based on stakeholder assessment: high (3), moderate (2), low (1). *** Mobility market weight (W): The product of B and I.

, each metric’s relevance was systematically evaluated based on its contribution to these principles. This evaluation was informed by prior studies [15,17], which highlight key factors for successful MaaS implementation, such as the integration of diverse transport modes, the availability of digital platforms, and the support of urban infrastructure. Moreover, the weights assigned to each metric in the ‘Mobility Market Weight’ (W) column result from the combination of two critical factors: the Benefit for MaaS (B) and the degree of Integration with the MaaS Ecosystem (I). These weights reflect a quantitative synthesis of both the practical applicability and theoretical significance of each metric in advancing MaaS adoption. This method provides a robust, data-driven framework for determining the relevance and impact of various mobility and macroeconomic metrics in the context of MaaS. In addition to this, the paper proposes a novel methodology for evaluating MaaS readiness, which is adaptable across different national, regional or even local contexts. While the specific weights assigned to each metric may vary depending on the unique mobility and socioeconomic conditions of individual regions, the underlying evaluation framework remains universally applicable. This flexibility allows the methodology to be tailored to local conditions while maintaining its robustness as an analytical tool. This adaptability is essential for the global scalability of the MaaS concept, as different regions may prioritise distinct mobility metrics based on their specific needs and infrastructure. It is, however, important to keep the weights the same when comparing two time periods in order to ensure comparability.

In weighted analyses such as the one presented in this paper, it is expected that changes in the assigned weights will naturally lead to different results. This characteristic is common in many economic evaluations, particularly in cost–benefit analyses (CBA), where various factors are weighted according to their relative importance in the overall outcome. For instance, in transportation-related CBAs, elements such as travel time savings, environmental impact, and safety are weighted differently based on their economic value and policy relevance [24]. In this study, the weights for each metric are based on economic principles and empirical evidence, ensuring that they align with the core goals of Mobility as a Service (MaaS). Since this evaluation framework incorporates economic indicators such as GDP per capita, transportation infrastructure investments, and consumption expenditures, it employs a fundamentally economic approach to assessing the readiness for MaaS. The flexibility of this method allows for adjustments in the weighting of metrics depending on the national or regional context without undermining the robustness of the overall methodology. Thus, while the specific weights may vary when applied in different settings, the underlying economic framework remains universally applicable. This ensures that the proposed evaluation approach can be adapted to local conditions while maintaining its relevance and accuracy in measuring the potential for MaaS adoption.

2.2. Definition of Mobility Market Metrics

As a next step, we define and calculate specific metrics for each mobility market to capture the financial dynamics within each mobility market. Table 3 shows the identified mobility market metrics.

While the metrics mentioned are relatively straightforward, the Shannon Index may raise questions and will, therefore, be described in more detail. Based on the estimation of market shares, we calculate the utilisation mix of brand shares using a diversity index, namely the Shannon Index. We adopt this approach from biology to compare mobility service provider diversity within the mobility market quantifiably. The logic behind the Shannon Index is that it considers the proportion of different entities (in our case, mobility service providers) and their relative frequencies. The Shannon Index, relying on logarithmic functions, is sensitive to rare entities, acknowledging that less common operators contribute significantly to the overall diversity.

$$H=-{\sum }_{i=1}^S{p}_i\times \ln {(p}_i)$$

(1)

where the following variables are used:

• H: Shannon Index.
• S: number of mobility service providers in the area.
• p_i: portion of the i-th mobility service provider to the total number of entities.

The logarithmic function is influential when pi is close to 0 or 1. It amplifies the contribution of less frequent entities to the total information, making the Shannon Index responsive to rare entities (in this context, mobility service providers).

2.3. Definition of Macroeconomic Metrics

In addition to the presented mobility market metrics, Table 4 illustrates transportation and mobility-related macroeconomic indicators and infrastructure investments that we integrate into the MSI. As for each mobility market, we analyse whether the metric benefits MaaS and integrates its MaaS system integration potential and weight based on the conducted stakeholder assessment.

The macroeconomic metrics, including demographic indicators such as population age structure, are derived from publicly available national statistical databases and international sources [25]. These sources provide comprehensive data on the socioeconomic conditions relevant to Mobility as a Service (MaaS) implementation. Specific disaggregated data for finer age groupings related to these metrics are often unavailable, which necessitated the use of broader age categories. Regarding the choice of 44 years as the threshold between “young” and “older” population groups, this division was informed by demographic and labour market research, including studies from Eurostat, which frequently use similar age groupings to reflect differences in digital skills and technology adoption. Individuals under 44 years are generally considered more likely to adopt new mobility solutions, such as MaaS, due to their higher levels of technological literacy and openness to change. Conversely, older populations (those over 44) may exhibit more established travel habits and may be slower to adopt new mobility technologies [25]. This division reflects a practical distinction observed in transportation and mobility behaviour studies. The resulting weight of the presented metrics towards MaaS is based on the following stakeholder-based considerations.

2.4. Derivation of the MSI Formula

The MaaS Status Index (MSI) aims to analyse and compare mobility indicators across two distinct periods, $T_{1 }(2017–2022)$ and $T_{2 }(2023–2028)$, focusing on the mobility markets metrics illustrated in Table 3 and the macroeconomic metrics described in Table 4. Let $M=\left\{M_1, \right.{ M}_2, \dots , \left.{ M}_n\right\}$ represent a set of mobility markets and macroeconomic categories. Let $K=\left\{K_1, \right.{ K}_2, \dots , \left.{ K}_n\right\}$ be a set of metrics representing various aspects of mobility markets and macroeconomic categories. The goal is to assess changes and differences in metrics across mobility markets and macroeconomic categories within the context of MaaS. To generate the MSI, we calculate the following figures.

Arithmetic mean of metric values over time (${\overline{x}}_{T_k{,K}_j}^{\left(i\right)}$): We calculate the mean value for a specific metric mobility market or macroeconomic category $M_i$ during period $T_k$.

$${\overline{x}}_{T_k{,K}_j}^{\left(i\right)}={\displaystyle \frac{1}{m_i}}{\sum }_{l=1}^{m_i}{x}_{l, T_k{,K}_j}^{\left(i\right)}$$

(2)

where the following variables are used:

• ${\overline{x}}_{T_k{, K}_j}^{\left(i\right)}$: mean value for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_k$.
• $m_i$: number of data points for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_k$.
• $x_{l, T_k{,K}_j}^{\left(i\right)}$: l-th data point for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_k$.

Min-max Normalization (${\tilde{x}}_{T_k{,K}_j}^{\left(i\right)}$): We normalise the mean value of a specific metric $K_j$ in a specific mobility market or macroeconomic category $M_i$ during period $T_k$ based on min-max normalisation. This normalisation ensures that all values are scaled proportionally between 0 and 1 based on minimum and maximum values, providing a standardised representation for each metric in the specified market or economic category and period. Values below 0.5 indicate that the mean is closer to the dataset’s minimum, suggesting lower intensity or magnitude. Values above 0.5 show the mean is closer to the maximum, implying higher intensity. A value of 0.5 indicates the mean lies exactly between the minimum and maximum, reflecting an average intensity or magnitude. These normalised values help compare the relative positioning of the data within its range for a given period.

$${\tilde{x}}_{T_k{,K}_j}^{\left(i\right)}={\displaystyle \frac{{\overline{x}}_{T_k{,K}_j}^{\left(i\right)}-{min}_{T_{k{,K}_j}}^{\left(i\right)}}{{max}_{T_k{,K}_j}^{\left(i\right)}-{min}_{T_k{,K}_j}^{\left(i\right)}}}$$

(3)

where the following variables are used:

• ${\tilde{x}}_{T_k{,K}_j}^{\left(i\right)}$: normalised mean value of metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_k$.
• ${\overline{x}}_{T_k{, K}_j}^{\left(i\right)}$: mean value for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_k$.
• ${min}_{T_{k{, K}_j}}^{\left(i\right)}$: minimum value of metric $K_j$ for the mobility market or macroeconomic category $M_i$ during period $T_k$.
• ${max}_{T_{k{,K}_j}}^{\left(i\right)}$: maximum value of metric $K_j$ for the mobility market or macroeconomic category $M_i$ during period $T_k$.

Adjusted Normalisation Formula: To handle cases where the mean value of the annual growth rate in a period is zero, we adjust the normalisation formula by introducing a small positive constant $ϵ=0.5$. This approach ensures that the normalised value never becomes zero but represents the middle of the range, representing no change. Otherwise, zeros would cause issues in further calculations, especially when using logarithmic functions (see Formula (5)). For instance, consider the scenario where the smartphone penetration rate in the mobility market shows no change during the period T₂, as illustrated in [26]. This results in a mean value of zero for this metric in T₂. In such cases, we apply an adjusted normalisation formula as illustrated in Formula (4). This adjustment allows the normalised value to reflect the stability of the metric without causing disruptions in the index calculation. The use of $ϵ=0.5$ to maintain the data representation’s integrity, ensuring the index remains robust and interpretable.

$${\tilde{x}}_{T_k{,K}_j}^{\left(i\right)}=\left\{\begin{array}{c}{\displaystyle \frac{{\overline{x}}_{T_k{,K}_j}^{\left(i\right)}-{min}_{T_{k{,K}_j}}^{\left(i\right)}}{{max}_{T_k{,K}_j}^{\left(i\right)}-{min}_{T_k{,K}_j}^{\left(i\right)}}} if {\overline{x}}_{T_k{,K}_j}^{\left(i\right)} \ne 0\\ ϵ if {\overline{x}}_{T_k{,K}_j}^{\left(i\right)}=0\end{array}\right.$$

(4)

MaaS Status Index (MSI): We sum the weighted exponential averages across the mobility markets and macroeconomic categories to calculate the$MSI$. We use the natural logarithm ($ln$) to reduce the impact of large values like the Shannon Index.

$$MSI={\sum }_{i=1}^n{w}_{M_i}\times exp\left({\displaystyle \frac{1}{m_i}}{\sum }_{j=1}^{m_i}ln\left({\tilde{x}}_{T_k{,K}_j}^{\left(i\right)}\right)\right)$$

(5)

where the following variables are used:

• $n$: total number of mobility markets.
• $w_{M_i}$: weight assigned to the mobility market or macroeconomic category $M_i$ where $w_{M_i}\in \left\{-3\right.,\left.3\right\}$.
• $m_i$: number of metrics for the mobility market or macroeconomic category $M_i$.
• exp: the exponential term used to revert the average to the original scale.

Comparison (${∆MSI}_{K_j}^{\left(i\right)}$): we calculate the MSI difference between periods $T_2$ and $T_1$ for a specific metric $K_{j}in a particular$ mobility market or macroeconomic category $M_i.$

$${∆MSI}_{K_j}^{\left(i\right)}={MSI}_{{T_2,K}_j}^{\left(i\right)}-{MSI}_{{T_1,K}_j}^{\left(i\right)}$$

(6)

where the following variables are used:

• ${∆I}_{K_j}^{\left(i\right)}$: index difference between periods $T_2$ and $T_1$ for metric $K_j$ in the mobility market or macroeconomic category $M_i$.
• ${MSI}_{{T_2,K}_j}^{\left(i\right)}$: MaaS Index for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_2$.
• ${MSI}_{{T_1,K}_j}^{\left(i\right)}$: MaaS Index for metric $K_j$ in the mobility market or macroeconomic category $M_i$ during period $T_1$.

3. Results

3.1. Mobility Markets

Table 5. Normalised mobility market metrics for Austria in T₁ (2017-2022), Shannon Index (H), mobility market weight (W), and resulting MaaS Status Index (MSI) for mobility markets in the given period.

T1 2017–2022 Austria	Unshared Individual Trips				Shared Individual Trips							Shared Collective Trips
	Private Car (Fuel Based)	Private Car (Electrified)	Motor Bike	Bicycle	Car Sharing	E-Scooter Sharing	Moped Sharing	Bike Sharing	Taxi	Ride Hailing	Car Rental	Bus	Train	Aeroplane	Public Transportation
Revenues (R)	0.5	0.4	0.3	0.4	0.4	0.6	0.4	0.4	0.6	0.6	0.7	0.6	0.6	0.6	0.6
Average revenue per user (ARPU)	-	-	-	-	0.6	0.5	0.3	0.4	0.6	0.6	0.4	0.6	0.6	0.5	0.7
Vehicle costs (VC)	0.3	0.4	0.4	0.4	-	-	-	-	-	-	-	-	-	-	-
Vehicles sales (VS)	0.5	0.4	0.4	0.6	-	-	-	-	-	-	-	-	-	-	-
Number of users (U)	-	-	-	-	0.5	0.6	0.4	0.4	0.5	0.5	0.7	0.5	0.5	0.6	0.6
User penetration rate (UPR)	-	-	-	-	0.5	0.7	0.4	0.4	0.5	0.5	-	0.5	0.5	0.6	0.6
Online sales channel (SC)	-	-	-	-	0.5	-	-	0.4	-	-	0.5	0.5	0.5	0.5	0.5
Autonomous driving level 2 (AL2)	0.4	-	-	-	-	-	-	-	-	-	-	-	-	-	-
CO2 emissions (CO2)	0.7	-	-	-	-	-	-	-	-	-	-	-	-	-	-
Number of charging stations (CS)	-	0.3	-	-	-	-	-	-	-	-	-	-	-	-	-
Charging station revenues (RCS)	-	0.3	-	-	-	-	-	-	-	-	-	-	-	-	-
Shannon-Index (H)	2.1	2.0	1.8	-	1.3	-	-	1.9	-	-	1.4	0.9	0.2	2.0	0.4
Number of matrices	6	6	4	3	6	4	4	6	4	4	5	6	6	6	6
Mobility market weight (W)	−1	−1	−1	3	3	2	2	2	3	3	3	3	3	−1	3
MSI for mobility markets in T1	−0.6	−0.5	−0.5	1.4	1.7	1.2	0.7	1.0	1.6	1.6	2.0	1.8	1.4	−0.7	1.7

displays normalised metrics for different mobility markets in Austria during the period T₁ (2017–2022), allowing us to observe the relative contribution of various transport modes to the MaaS Status Index (MSI). Detailed calculation steps can be found in [26], while the raw data are available in [27]. The MSI combines metrics like revenues, vehicle costs, and user penetration rates to assess how well each market aligns with MaaS principles. The highest MSI contributions come from public transport (1.7), bus services (1.8), car rental (2.0), and shared modes such as car sharing (1.7) and ride hailing (1.6). These mobility markets support the goals of MaaS by facilitating shared and sustainable transportation options, which are central to integrating mobility services.

On the other hand, markets like private cars (fuel-based: −0.6) and aeroplanes (−0.7) detract from the MSI. These modes contribute less to MaaS due to their focus on individual and often less sustainable travel. Notably, the Shannon Index (H) values indicate the diversity within each market, with private cars and bicycles showing higher values, reflecting greater variation in user behaviour.

In the period T₂ (2023–2028),

Table 6. Normalised mobility market metrics for Austria in T₂ (2023–2028), Shannon Index (H), mobility market weight (W), and resulting MaaS Status Index (MSI) for mobility markets in the given period.

T2 2023–2028 Austria	Unshared Individual Trips				Shared Individual Trips							Shared Collective Trips
	Private Car (Fuel Based)	Private Car (Electrified)	Motor Bike	Bicycle	Car Sharing	E-Scooter Sharing	Moped Sharing	Bike Sharing	Taxi	Ride Hailing	Car Rental	Bus	Train	Aeroplane	Public Transportation
Revenues (R)	0.5	0.4	0.5	0.3	0.5	0.5	0.5	0.5	0.4	0.5	0.3	0.4	0.5	0.3	0.4
Average revenue per user (ARPU)	-	-	-	-	0.4	0.4	0.4	0.5	0.4	0.4	0.4	0.4	0.4	0.4	0.4
Vehicle costs (VC)	0.5	0.3	0.6	0.3	-	-	-	-	-	-	-	-	-	-	-
Vehicles sales (VS)	0.5	0.5	0.6	0.6	-	-	-	-	-	-	-	-	-	-	-
Number of users (U)	-	-	-	-	0.6	0.5	0.5	-	0.5	0.5	0.5	0.7	0.6	0.5	0.6
User penetration rate (UPR)	-	-	-	-	0.6	0.5	0.5	0.7	0.5	0.5	-	0.7	0.6	0.5	0.6
Online sales channel (SC)	-	-	-	-	0.5	-	-	0.5	-	-	0.5	0.5	0.5	0.5	0.5
Autonomous driving level 2 (AL2)	0.6	-	-	-	-	-	-	-	-	-	-	-	-	-	-
CO2 emissions (CO2)	0.5	-	-	-	-	-	-	-	-	-	-	-	-	-	-
Number of charging stations (CS)	-	0.5	-	-	-	-	-	-	-	-	-	-	-	-	-
Charging station revenues (RCS)	-	0.4	-	-	-	-	-	-	-	-	-	-	-	-	-
Shannon-Index (H)	2.1	2.0	1.8	-	1.3	-	-	1.9	-	-	1.4	0.9	0.2	2.0	0.4
Number of matrices	6	6	4	3	6	4	4	5	4	4	5	6	6	6	6
Mobility market weight (W)	−1	−1	−1	3	3	2	2	2	3	3	3	3	3	−1	3
MSI for mobility markets in T2	−0.7	−0.5	−0.8	1.1	1.8	0.9	0.9	1.4	0.2	1.4	1.6	1.7	1.3	−0.5	1.4

provides insights into the key mobility markets contributing to the MSI. Car sharing (1.8) continues to strengthen its role, overtaking more traditional modes like public transport (1.4) and buses (1.7), highlighting a shift towards shared mobility services. Ride hailing (1.4) and car rental (1.6) also play significant roles, supporting the transition to more flexible, on-demand mobility options. At the same time, markets deemed less beneficial for MaaS, such as motorbikes (−0.8) and private cars (fuel-based: −0.7), show a further decline, emphasising the increasing preference for environmentally friendly and shared modes. Notably, private cars (electrified) and aeroplanes maintain low contributions to the MSI, but their negative impacts (−0.5) are slightly reduced compared to T₁, reflecting gradual progress towards more sustainable transportation options. The Shannon Index (H), which measures diversity within each market, remains consistent across most metrics, further validating that core mobility markets such as bicycles and car sharing are experiencing steady growth. These data underscore the importance of fostering shared mobility solutions and reducing reliance on private, fuel-based transport in the evolution of MaaS in Austria. The second period shows a clearer move towards shared, sustainable transportation solutions, with car sharing emerging as the most promising market for MaaS advancement.

Table 7. Comparison of the mobility market-related MSI in T₁ (2017–2022) and T₂ (2023–2028).

	Unshared Individual Trips			Shared Individual Trips								Shared Collective Trips				Summarised MSI for Mobility Markets
	Private Car (Fuel Based)	Private Car (Electrified)	Motor Bike	Bicycle	Car Sharing	E-Scooter Sharing	Moped Sharing	Bike Sharing	Taxi	Ride Hailing	Car Rental	Bus	Train	Aeroplane	Public Transportation
Mobility market MSI T1	−0.6	−0.5	−0.5	1.4	1.7	1.2	0.7	1.0	1.6	1.6	2.0	1.8	1.4	−0.7	1.7	13.8
Mobility market MSI T2	−0.7	−0.5	−0.8	1.1	1.8	0.9	0.9	1.4	0.2	1.4	1.6	1.7	1.3	−0.5	1.4	11.2
Difference T1 vs. T2	−0.1	0.0	−0.3	−0.3	0.1	−0.3	0.2	0.4	−1.4	0.2	−0.4	−0.1	−0.1	0.2	−0.3	−2.6

highlights how the MaaS Status Index (MSI) shifts between T₁ (2017–2022) and T₂ (2023–2028). The overall MSI drops from 13.9 in T₁ to 11.5 in T2, indicating a slight decline in the alignment of mobility markets with MaaS principles. The most significant decline comes from the taxi market (−1.4), which shows structural challenges between the periods. Other declines are observed in car rental (−0.4), motorbike, bicycle, and E-scooter sharing (all −0.3). Despite this, car sharing becomes more prominent in T₂, increasing to 1.8, establishing itself as a key MaaS driver. The shift in E-scooter sharing from a strong growth trend in T₁ to stabilisation in T₂ highlights that the declining MSI does not necessarily reflect negative trends but rather the maturation of some markets. This comparison suggests that while some markets decline, others are stabilising, which is a natural development as the mobility landscape evolves. It is also notable that markets like private cars (both fuel-based and electrified) and aeroplanes maintain relatively low or negative MSI contributions in both periods, continuing to challenge the adoption of MaaS.

Figure 1 compares the mobility market-related (MSI) for Austria between two periods, T₁ (2017–2022) and T₂ (2023–2028), categorised into unshared individual trips, shared individual trips, and shared collective trips. The overall MSI for mobility markets declines by 19% from T₁ to T₂, reflecting a stabilisation after initial growth. Unshared individual trips, such as private car use, decreased by 25%, indicating a shift away from private mobility solutions. Shared individual trips, such as car sharing and ride hailing, dropped by 17%, while shared collective trips, such as public transport, decreased by 7%. This suggests that shared mobility services continue to be prominent but are experiencing some stabilisation as these markets mature over time.

3.2. Macroeconomic Metrics

In this section, we apply the MSI methodology to macroeconomic metrics, analysing normalised values for each metric during T₁ (2017–2022) and T₂ (2023–2028). These metrics, outlined in

Table 8. Normalised mean values of macroeconomic metrics in Austria in T₁ (2017–2022) and T₂ (2023–2028) and resulting MaaS Status Index (MSI) representing the macroeconomic situation towards MaaS in Austria.

Category	Macroeconomic Metric	Metric Weight	Normalised Metric Austria T1	Normalised Metric Austria T2	Difference T1 vs. T2
Population	Total population	1	0.6	0.5
	Urbanisation rate	3	0.5	0.5
	Number of households	−2	0.5	0.5
	Proportion of the younger population (<44 years)	3	0.6	0.5
	Proportion of the older population (>44 years)	3	0.5	0.5
Population Index			4.4	4.0	−0.4
Transportation economics	Gross domestic product (GDP) per capita	3	0.3	0.5
	Consumption expenditure. transportation (per capita)	3	0.6	0.5
	Consumption expenditure. vehicle purchase (per capita)	−2	0.4	0.5
	Consumption expenditure. transportation services (per capita)	3	0.5	0.5
	Price level index. transportation	−2	0.6	0.6
Transportation economics index			2.2	2.3	0.1
Mobility	Airline passengers	−1	0.3	0.6
	Departures of airlines in thousand	−1	0.4	0.5
	Railway tracks in million metres	3	0.4	0.5
	Rail passenger kilometres (per capita) in million metres	3	0.5	0.4
	Road passenger kilometres (per capita) in million metres	2	0.6	0.6
	Rail passenger kilometres in trillion metres	3	0.5	0.4
	Road passenger kilometres in trillion metres	3	0.5	0.7
Mobility index			6.2	6.1	−0.1
Transportation infrastructure investments	Investments in airport infrastructure (% of GDP)	−1	0.5	0.4
	Maintenance of airport infrastructure (% of GDP)	−1	0.6	0.5
	Investments in railway infrastructure (% of GDP)	3	0.3	0.4
	Maintenance costs of railway infrastructure (% of GDP)	−1	0.5	0.4
	Investments in road infrastructure (% of GDP)	3	0.6	0.4
	Maintenance costs of road infrastructure (% of GDP)	−1	0.3	0.4
	Investments in railway infrastructure in billion Euros	3	0.5	0.4
	Maintenance costs of railway infrastructure in billion Euros	−1	0.3	0.3
	Investments in road infrastructure in billion Euros	3	0.6	0.5
	Maintenance costs of road infrastructure in billion Euros	−1	0.5	0.4
	Investments in airport infrastructure in million Euros	−1	0.4	0.3
	Maintenance costs of airport infrastructure in million Euros	−1	0.6	0.5
Transportation infrastructure investments index			2.3	1.9	−0.4
ICT	Smartphone Penetration (% of population)	3	0.5	0.5
	Internet Penetration (% of population)	3	0.4	0.5
ICT index			2.7	3.0	0.3
Summarised MSI for macroeconomic metrics			17.8	17.3	−0.5

, provide insights into the key drivers of MaaS in Austria over these periods. In T₁, the categories “Mobility” and “Population” contribute the most to the MSI, with metrics such as urbanisation and population proportions playing significant roles. The “Transportation economics” and “ICT” categories account for smaller shares, but both demonstrate growth potential. For example, the urbanisation rate and transportation consumption expenditures underscore the evolving demand for mobility services in both urban and suburban areas.

The overall MSI for macroeconomic metrics decreases slightly from 17.8 in T₁ to 17.3 in T₂. This slight decline can be attributed to shifts in transportation infrastructure investments, with investments in both road and rail infrastructure showing minor reductions, possibly due to policy or economic factors affecting long-term project funding. Despite this, categories such as “ICT” show a positive trend, increasing from 2.7 to 3.0 between the two periods. This growth reflects the critical role of technological infrastructure in facilitating MaaS, particularly as smartphone and internet penetration rates continue to rise. A notable decrease in the “Mobility” index from 6.2 to 6.1 suggests a shift in transportation patterns, where airline passenger numbers and road passenger kilometres rise in T₂, while rail passenger kilometres show slight declines. These shifts indicate potential changes in how people are moving, with a greater reliance on air and road travel. The growth in “ICT” highlights the increasing importance of digital infrastructure in supporting the expansion of MaaS services, which is consistent with global trends in digitisation and smart mobility solutions. Despite the small overall decrease in MSI, the analysis of macroeconomic metrics in T₂ reveals emerging opportunities for MaaS expansion, particularly in areas where digital infrastructure and transportation economics are improving. However, the decline in infrastructure investment indices could pose challenges to long-term MaaS development, underscoring the need for sustained or increased investment in key infrastructure areas to support seamless, integrated mobility solutions. This analysis highlights the complex interactions between macroeconomic factors and their influence on the development and sustainability of MaaS in Austria. Continued attention to digital infrastructure, population dynamics, and transportation investment will be crucial for the successful implementation of MaaS in the coming years.

Figure 2 presents a comparison of the MSI for macroeconomic metrics in Austria across two time periods, T1 (2017–2022) and T₂ (2023–2028). The figure illustrates the MSI components, including Population, Transportation Economics, Mobility, Infrastructure Investments, and ICT indices, highlighting the observed and projected changes from T₁ to T₂. The MSI for macroeconomic metrics shows a slight decrease from 17.8 in T₁ to 17.3 in T₂ (−3%). While the Population Index declines significantly (−9%), the Transportation Economics Index increases by 5%, reflecting economic growth in transportation-related sectors. The Mobility Index experiences a minor decline (−2%), suggesting stabilisation in shared mobility services. Infrastructure investments see the largest decrease (−17%), possibly due to reduced long-term investments in road and rail infrastructure. Conversely, the ICT Index grows by 11%, highlighting the increasing importance of digital infrastructure in MaaS development. This figure underscores how economic, demographic, and technological changes impact the readiness and potential for MaaS in Austria over time.

3.3. Total MaaS Statuts Index (MSI)

The total MSI reflects the combined results of the mobility market and macroeconomic metrics for Austria during T₁ (2017–2022) and T₂ (2023–2028). In

Table 9. Comparison of the total MSI in T₁ (2017–2022) and T₂ (2023–2028).

	T1 2017–2022 Austria	T2 2023–2028 Austria
MSI (mobility markets)	13.8	11.2
MSI (macroeconomic metrics)	17.8	17.3
MSI (total)	31.6	28.5

, we observe a decrease in the total MSI from 31.7 in T₁ to 28.8 in T₂, indicating a slight overall decline in conditions favourable to MaaS. The MSI for mobility markets declines from 13.9 to 11.5, while the MSI for macroeconomic metrics decreases from 17.8 to 17.3. This reduction in the total MSI suggests that certain factors within both mobility markets and macroeconomic metrics have experienced slower growth or stabilisation in T₂. However, this does not necessarily imply worsening conditions for MaaS but rather a shift towards stable market development after an initial phase of rapid growth in T₁. For instance, some emerging mobility markets, such as car sharing and ride hailing, showed strong growth in T₁, which has now stabilised in T₂. Similarly, the macroeconomic metrics reveal more stable figures for categories like population and ICT despite minor fluctuations. The reduction in mobility market MSI, specifically, may reflect the maturity of certain services, along with infrastructural or investment challenges that could limit further rapid expansion. The slight decrease in macroeconomic MSI, on the other hand, indicates stable yet plateauing growth in the underlying economic and demographic conditions that support MaaS. The decrease in total MSI from T₁ to T₂ suggests that while growth may have slowed, the foundational conditions for MaaS in Austria remain largely stable. This signals that future MaaS developments will likely focus on sustaining and optimising the existing mobility and macroeconomic structures rather than relying on continued rapid expansion.

4. Discussion and Conclusions

We presented a quantitative examination to assess the readiness and potential of urban areas to implement MaaS. The paper outlines the necessity for a standardised methodology to monitor and evaluate the development of MaaS, proposing a multi-dimensional approach that incorporates a broad range of mobility market-related and macroeconomic metrics. The findings from the Austrian case study highlight several key insights. Firstly, the higher MSI value in T₁ compared to T₂ indicates a period of growth for MaaS implementation over the last years. This growth can be attributed to increased investments and the introduction of new mobility services. However, the stabilisation of the MSI in T₂ suggests that the initial rapid growth phase is transitioning into a consolidation and sustained development phase. This trend is consistent with the lifecycle of many new technologies and services, where an initial surge is followed by a period of steady growth. Secondly, the analysis of individual mobility markets reveals the dynamic nature of the transportation ecosystem. Shared mobility services such as car sharing, ride-hailing, and bike sharing show strong performance and high integration within the MaaS ecosystem. These services align well with the principles of MaaS, promoting shared use and reducing the reliance on private car ownership. On the other hand, traditional modes of transport, such as private cars (fuel-based and electrified) and motorcycles, exhibit lower relevance to MaaS, highlighting the ongoing challenge of transitioning users from private to shared mobility options. The macroeconomic metrics further emphasise the importance of supportive socio-economic conditions for MaaS adoption. High urbanisation rates, a younger demographic, and strong GDP per capita contribute to higher MSI values. These factors indicate that urban areas with a tech-savvy population and robust economic conditions are more likely to embrace MaaS solutions effectively.

The methodology proposed in this study, which includes the use of Min-max normalisation, offers a systematic approach to comparing the mobility market and macroeconomic metrics by rescaling data to a common range. Min-max normalisation was chosen for its simplicity and effectiveness in transforming different types of data (e.g., revenues, CO₂ emissions) into a consistent scale (0–1). This allows for direct comparison across metrics that have inherently different units of measurement, which is essential for generating the MaaS Status Index (MSI) in a meaningful way. Min-max normalisation is widely used in situations where the goal is to preserve the relationships between data points and ensure interpretability. By preserving the original data distribution within the defined range, Min-max normalisation allows stakeholders to understand relative positions and variations across metrics in different periods, which is critical in longitudinal studies such as this. Alternative normalisation techniques, like Z-score normalisation, could mitigate the sensitivity to outliers but would not maintain the proportional relationships needed for comparing vastly different metrics in this context. Min-max normalisation also supports non-linear relationships, allowing small-scale changes in one period to still be visible in the overall index. This visibility is essential for policy-driven applications like MaaS, where smaller shifts in certain metrics (e.g., infrastructure investment) may have large implications on the development of shared mobility solutions. Thus, while Min-max normalisation is sensitive to the data range, it remains a highly effective tool for scaling heterogeneous metrics and ensuring that no single indicator disproportionately influences the MSI.

In conclusion, the MSI may serve as a tool for policymakers, urban planners, and transport operators, providing a standardised framework for assessing and comparing the readiness of different urban areas for MaaS implementation. The Austria case study demonstrates the MSI’s practical application, highlighting key trends and areas for improvement. The findings suggest that while the initial phase of MaaS implementation may experience rapid growth, sustained development requires continuous investment and adaptation to changing market conditions.