Sustainable Mobility Readiness Index: Evaluating Global Metropolitan Areas’ Passenger Mobility and Logistics Performance

Define the Concept

The first step involves defining the concept that the SMRI aims to measure. Sustainable mobility is a multidimensional and evolving construct that reflects a metropolitan area’s capacity to transition toward environmentally, socially, and economically responsible transport systems. It encompasses not only travel behavior and infrastructure, but also governance, technology, and resource management. Based on established research and best practices, six core aspects are considered central to this concept:

Design of the Framework

The second step in the construction of the SMRI involves the design of a theoretical framework that defines the key dimensions of sustainable mobility and organizes them into logical and analytically coherent groups. This framework serves as a conceptual blueprint, ensuring that the indicators selected in the following step are both meaningful and aligned with the multidimensional nature of urban sustainable mobility.

Drawing on composite indicator construction methodologies ( 32 ), relevant policy frameworks ( 30 ), and academic literature ( 33 ), six core dimensions were identified:

Each of these dimensions is further disaggregated into thematic groups to facilitate a structured approach to indicator selection. For instance, Transport Supply includes the groups “Modes Availability, Coverage & Usage” and “Infrastructure,”Innovation is composed of “Data,”“Technologies,” and “Connectivity,” and Finance consists of “Subsidization” and “Investment.” These groupings serve as intermediate layers between the broad dimensions and the specific indicators introduced in the next step.

Selection of Indicators

The third step involves selecting measurable indicators to operationalize the theoretical framework. These indicators are assigned to thematic groups within the six dimensions of the SMRI, enabling a multidimensional evaluation of sustainable urban mobility. The aim is to capture both qualitative and quantitative characteristics of mobility systems across metropolitan areas in a structured and comparable manner.

The indicator selection process followed a structured and iterative approach, drawing from a range of sources to ensure both scientific robustness and practical relevance. This included an extensive review of academic literature and applied research on sustainable mobility metrics ( 33 , 34 ), as well as international guidelines and composite indicator frameworks ( 30 , 31 ). Furthermore, the process was informed by consultations with experts from academia, transport authorities, and mobility consulting teams, including urban mobility researchers, transport planners, and data practitioners. Their expertise in sustainable mobility planning, transport innovation, and mobility data systems helped ensure that the selected indicators reflect both theoretical robustness and real-world applicability. Experts from industry, including mobility consulting teams from PricewaterhouseCoopers (PwC), also contributed to the consultation process by providing practical insights from applied mobility projects and metropolitan transport analyses. In addition, PwC supported the development of the research through project collaboration and funding contributions. Table 1 summarizes the professional background and areas of expertise of the consulted experts.

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Table 1.

Profile of Experts Consulted for Indicator Selection

Type of institution	Role/Position	Expertise area	Geographic scope
Academic institutions	Professor / Researcher	Sustainable urban mobility, MaaS, transport systems	Europe
Transport authorities	Mobility planner/policy advisor	Public transport planning, SUMP implementation	Europe, North America
Industry/consulting (PwC mobility teams)	Urban mobility consultant	Mobility innovation, data analytics, smart mobility platforms	Global
Data and mobility platform practitioners	Data specialists	Transport data systems, digital mobility services	Global

Note: MaaS = Mobility as a Service; SUMP = sustainable urban mobility plan; PwC = PricewaterhouseCoopers.

A set of criteria was applied to guide the inclusion of indicators. First, relevance was critical—each indicator had to meaningfully reflect a distinct aspect of the dimension it represents. Second, data availability with priority given to indicators with consistent metropolitan-level data across multiple geographic contexts. Importantly, indicators were also chosen with practical implementation in mind—the aim was to develop a framework that could be calculated with reasonable effort and without requiring extensive new data collection or high-resource inputs. Third, comparability was considered essential, with indicators selected based on the use of standardized definitions and methodologies to ensure consistency across metropolitan areas.

In total, 64 indicators were selected and distributed across the six dimensions and their respective thematic groups. A complete overview of these indicators, along with their corresponding thematic groups, descriptions and units of measurement are presented in Table 2.

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Table 2.

Sustainable Mobility Readiness Index Dimensions, Groups, and Key Indicators with Noted Importance

Group	Indicators	Importance
Transport supply
Modes availability, coverage and usage	• Private car usage • Public transport coverage • Public transport operating hours • Punctuality of public transport • Coverage of bike-sharing services • Car-sharing availability and density • On-demand services • EV for deliveries • Electric bikes for deliveries	The transport supply dimension—focusing on mode availability, coverage, and usage—is central to both MaaS integration and sustainability. High private car usage indicates lower MaaS maturity and greater emissions, while strong public transport coverage ensures accessibility and supports modal shift. Operating hours also matter, as extended service enhances transit reliability and reduces dependence on private vehicles. Public transport punctuality builds user trust and consistent usage, which are critical for both effective MaaS and sustainable mobility. The inclusion of bike-sharing systems offers a low-emission, flexible travel alternative. On-demand and car-sharing services further reduce private vehicle reliance by offering multimodal options. Lastly, the deployment of electric vehicles and e-bikes for freight and delivery highlights progress in electromobility, helping to reduce emissions and traffic in dense urban areas.
Infrastructure	• Road network density • Public transport stations/stops density • Wi-fi availability on public transport • Cycling network density • Pedestrian streets coverage • EV charging infrastructure density • Multimodal hubs • Consolidation centers • Zero-emission/congestion charging zone coverage	Infrastructure indicators are crucial for assessing the foundations that enable sustainable and integrated mobility systems. Road network density captures the ease of connectivity across the urban area, reducing travel times and improving access. Cycling network density signals investment in micromobility, promoting low-emission, active transport. Network density indicators were calculated using standard spatial density measures derived from GIS datasets. Network density was defined as the total length of the respective infrastructure network divided by the metropolitan area surface (total network length in km/metropolitan area in km2). This approach allows comparison across metropolitan areas of different sizes. The resulting values were subsequently converted to the 0–10 Likert scale and normalized using the min–max normalization procedure described in the “Scoring and Normalization” section. A dense network of public transport stations supports transit accessibility and is essential for effective multimodal journeys within MaaS. Wi-fi on public transport enhances digital connectivity, allowing users to access real-time information and multimodal apps. Pedestrian and zero-emission zones demonstrate a city’s commitment to reducing car dependence and improving air quality. EV charging infrastructure facilitates the transition to cleaner vehicles, especially for shared and freight services. Finally, multimodal hubs and logistics consolidation centers support system integration, making transfers between modes more seamless and efficient key to both MaaS readiness and sustainability.
Transport demand	• Sustainable travel behavior–modal split • Road safety–traffic accidents • Car ownership rate • Tech-familiarity (smartphone penetration) • Gas cost per liter • Average taxi fare • Walkability index	Transport demand indicators are essential for understanding how people interact with urban mobility systems. They capture behavioral, economic, and technological factors that influence modal choices and readiness for sustainable transport. Modal split reflects actual travel habits and the prevalence of sustainable modes, while car ownership and fuel prices indicate reliance on private vehicles. Traffic accident rates and walkability affect safety and accessibility—key drivers of mode choice. The walkability indicator was derived from amenity-based urban accessibility metrics, primarily based on Walk Score data, which estimate walkability according to walking distance to everyday amenities such as retail services, restaurants, parks, schools, and other key destinations. The methodology applies a distance-decay approach and also accounts for elements of pedestrian connectivity such as street intersection density and block length ( 35 , 36 , 37 ). The reported values were subsequently converted to the 0–10 Likert scale and normalized using the min–max normalization procedure described in the “Scoring and Normalization” section. Smartphone penetration and the cost of on-demand services show the population’s digital and financial access to emerging mobility options. Including these indicators helps assess not just the current demand, but also the potential for shifting behaviors toward more sustainable and integrated transport solutions.
Innovation
Data	• Open public transport data • Open traffic data • Open parking data • Open shared-services data	Data indicators are central to MaaS implementation and digital mobility systems. Open data on public transport and traffic enables real-time planning and congestion reduction. Parking availability data supports seamless multimodal trips, while shared mobility data ensures users can access integrated, up-to-date travel options. These indicators reflect the digital foundations needed for smart, sustainable mobility. All data-related indicators are scored using structured multilevel rubrics (e.g., degrees of API openness or coverage), rather than binary values, to increase measurement precision and support meaningful comparability across cities.
Technologies	• Traffic management center/smart traffic lights • Smart ticketing • Smart parking • Smart public transport stops • Multimodal journey planners maturity • Multimodal mobility apps maturity • CAVs adoption • Incentivization engines for EV charging	The technology group reflects the extent to which metropolitan areas adopt digital tools to enhance mobility efficiency, integration, and sustainability. Traffic management systems and smart ticketing indicate a city’s ability to manage flows in real time and streamline user access across modes. Apps providing parking availability and pricing help reduce search traffic and improve overall trip planning. Smart public transport stops with live updates increase system reliability and attract more users. Multimodal journey planners and mature mobility apps support seamless transitions between services. The presence of CAVs signals innovation readiness, while incentives for EV charging encourage cleaner energy use and support sustainability goals.
Connectivity	• 4G and 5G coverage • Existence of smart city platform	The integration of data availability into a smart city platform, along with widespread 4G and 5G coverage, illustrates the level of connectivity and open data access available to the public. These are critical factors for the adoption of mobility platforms, as they enable citizens to stay connected across different areas and make use of real-time data to improve their transport experience.
Policies	• SUMP or strategic transport plan • SULP or logistics plan • Mechanisms to monitor progress • Open data standards policy • Standards for open transport APIs • Data security standards • Third-party ticket sales • Transport authority • Net zero/climate neutrality policies	The policies dimension captures the regulatory and strategic frameworks that support sustainable mobility and the implementation of MaaS. The existence of SUMPs and SULPs reflects how metropolitan areas manage transport systems for both passengers and freight. Monitoring mechanisms ensure progress is tracked and plans are adjusted accordingly. Standards for data sharing and security are crucial for enabling digital integration while protecting user privacy. A dedicated transport authority strengthens coordination across services and modes. Lastly, the inclusion of net zero or climate neutrality policies signals alignment between mobility planning and broader environmental goals. Policy indicators also follow graded scales based on year of adoption, update frequency or maturity level. Only “transport authority” and “third-party ticket sales” are binary (yes/no), as they inherently capture a categorical notion.
Finance
Subsidization	• Subsidized public transport • Subsidized shared modes • Subsidized EV purchases • Subsidized EV infrastructure	The group of subsidization indicators relates to economic and policy support mechanisms that help make public transport, shared mobility services, and electromobility more affordable and attractive. These factors are essential for the successful adoption of MaaS and for achieving urban sustainability.
Investments	• Public–private partnerships • Government investment in shared mobility, EV charging stations, and CAV technologies	The indicators related to investments reflect a city’s financial and strategic commitment to advancing integrated and sustainable mobility systems through collaboration and funding. In this context, public-private partnerships are essential for fostering innovation and enabling seamless service integration. Government investments in shared transport highlights the promotion of flexible, multimodal options for urban travel. In addition, funding for EV charging infrastructure and CAV technologies supports long-term scalability and aligns with broader climate goals — all of which are key components reflected in the index.
Environment and energy	• Air quality • Renewable energy use in transport • Energy efficiency of transport • CO2 emissions • Fuel consumption split • Net zero/climate targets • Climate-related losses • Noise pollution restraint • Light pollution restraint	The environment and energy dimension reflects how metropolitan areas advance clean, shared, and public transport while reducing reliance on fossil fuels. Indicators such as air quality, renewable energy use, and CO2 emissions capture progress toward low-carbon mobility. Fuel consumption split and climate targets show alignment with long-term environmental goals, while climate-related losses highlight the need for resilient infrastructure. Noise and light pollution metrics also reveal how well metropolitan areas integrate active, electric, and energy-efficient modes into urban planning—core to both sustainability and MaaS adoption.

Note: MaaS = Mobility as a Service; SUMP = sustainable urban mobility plan; EV = electric vehicle; CAV = connected and autonomous vehicles; SULP = sustainable urban logistics plans; API = application program interface.

Data Collection

Following the definition of indicators, the next step involves gathering the corresponding data through a structured and methodologically sound collection process. The SMRI was applied to 26 metropolitan areas (Figure 2). Ensuring the quality, consistency, and comparability of data is essential for the SMRI’s reliability and applicability across diverse urban contexts.

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Figure 2.

Geographic distribution of the 26 assessed metropolitan areas in the Sustainable Mobility Readiness Index.

Data were sourced from recognized, transparent platforms such as international statistical agencies, national data portals, commercial mobility platforms, and open repositories. For European cities, primary data were collected from Eurostat and EU-level services. For international coverage, sources included the World Bank, the Organization for Economic Co-operation and Development (OECD), and the US Energy Information Administration for US cities. Traffic flow, congestion, and infrastructure data were obtained from platforms such as TomTom and OpenStreetMap, while spatial datasets, including GIS shapefiles, were retrieved from open government portals and national geospatial repositories.

In cases where standardized or open-access data were lacking, supplementary sources such as academic literature, news media, industry reports, and company websites were used selectively. For indicators requiring local-level specificity, data were obtained directly from municipal authorities or official city websites. If no open or standardized source was available for a specific indicator, PwC offices in the corresponding world regions contacted local authorities to obtain the required metropolitan-level data. When these data could not be provided, we used a proxy derived from nationally reported statistics that reflected the same conceptual dimension. Proxy use was limited and applied consistently to maintain comparability across cities.

Validation of Sources

Once data collection was completed, all sources underwent a validation process to ensure their credibility and relevance. Priority was given to sources from official government agencies, reputable international organizations, and recognized academic institutions, known for adhering to established statistical standards. The recency of data was also reviewed, with a preference for data within a five-year window. Older data were used only when necessary and documented for transparency, strengthening the index’s reliability and allowing meaningful comparisons across cities and time.

Data Conversion

To ensure internal consistency and enable cross-city comparability, all collected data were converted into standardized units of measurement. This step addresses variations in how different metropolitan areas or countries report data and is essential for maintaining the methodological integrity of the index.

Quantitative values were harmonized according to international conventions and measurement systems. For instance, physical quantities such as distance or area were standardized into metric units, while financial values were converted into a common currency using exchange rates applicable to a consistent reference year. Where necessary, normalization factors were applied to adjust for population size, geographic extent, or other structural differences. This process followed guidance from composite indicator methodologies ( 31 ) and ensured that all indicators are expressed on a comparable scale.

Comparison among Metropolitan Areas

An initial comparative analysis was conducted to examine the distribution and range of values for each indicator across the selected metropolitan areas. This empirical step was essential for identifying the minimum and maximum values used in the scoring process and for revealing key patterns in urban mobility performance. The comparison exposed meaningful variation across metropolitan areas in areas such as public transport coverage, cycling infrastructure, modal split, policy frameworks, and environmental impact.

This step provides an empirical foundation for interpreting differences in city performance, highlights potential outliers, and supports the construction of a robust and equitable index. Understanding the full range of values also allows for the identification of regional patterns or gaps in specific dimensions of sustainable mobility, which are addressed in subsequent steps of the index development process.

Conversion to Likert Scale

To enable cross-indicator comparability and facilitate aggregation, all indicator values were transformed into a unified Likert-type scale ranging from 0 to 10. This scoring system standardizes diverse indicators into a common evaluative format that reflects each city’s relative performance.

The conversion process followed a data-driven min–max linear transformation approach. For each indicator, the minimum and maximum values observed across all metropolitan areas were identified, and the total range was divided into 11 equal intervals, corresponding to Likert scores from 0 (lowest performance) to 10 (highest performance). Each city was assigned a score based on the interval in which its value fell.

For positively oriented indicators—those for which higher values represent better outcomes (e.g., availability of sustainable infrastructure)— metropolitan areas with the highest values received a score of 10, and those with the lowest values received a score of 0. For negatively oriented indicators—those for which lower values represent better performance (e.g., CO₂ emissions, congestion levels)—the scale was inverted so that lower values corresponded to higher scores.

This transformation enables easier comparison of metropolitan areas on a common scale but does not address variations in the distribution of values across indicators. Therefore, normalization will be performed in the subsequent step to ensure full comparability.

Normalization of Values

After converting indicator values to a 0–10 Likert scale, a final normalization step was applied to ensure all indicators are fully comparable and contribute equally to the construction of the index. While the Likert transformation standardizes the score range, it does not account for differing distributions across indicators, making normalization necessary for meaningful aggregation across thematic groups and dimensions.

The min–max normalization method was used to rescale each indicator’s values to a fixed range of [0,1], preserving the relative distances between observations while eliminating scale differences. The normalization was applied across all metropolitan areas for each individual indicator using the following formula indicated in Equation 1:

x ′ = ( x − min ( x ) ) ( max ( x ) − min ( x ) )

(1)

where x is the original value of the indicator (in Likert form), min(x) and max(x) represent the minimum and maximum values of the indicator across all metropolitan areas, and x′ is the normalized value, ranging from 0 (lowest observed performance) to 1 (highest observed performance).

This method ensures proportional contribution from each indicator, preventing any from disproportionately affecting the final score because of scale differences. Min–max normalization is commonly used in composite index construction for its simplicity and effectiveness ( 31 ).

Weighting

Weighting is a fundamental step in the construction of the SMRI, as it determines the relative influence of each indicator on the final composite score. Recognizing that not all indicators contribute equally to assessing sustainable mobility readiness, a budget allocation (BAL) method was applied to assign differentiated weights.

To apply the BAL method, we engaged subject-matter experts from each metropolitan area included in the study, typically one to three per city. These experts represented a mix of professional backgrounds, including staff from transport authorities, academics with specialization in sustainable mobility and transport innovation, and practitioners from PwC offices worldwide with operational experience in mobility and urban transition projects. Each expert independently assigned importance values to the indicators on a continuous 0–1 scale, reflecting their assessment of the relevance of each metric to sustainability and MaaS readiness. The individual assessments were then averaged to produce consolidated weights. Indicators considered central to promoting low-carbon, equitable, and integrated mobility systems were assigned higher weights, while those reflecting complementary or context-specific aspects received proportionally lower values.

The use of expert-driven weighting allows the index to reflect not only the structure of the theoretical framework, but also normative priorities embedded in current policy and research. This method ensures that the SMRI is not a simple aggregation of available data, but a conceptually grounded and policy-relevant tool. The BAL method is widely accepted in composite indicator development for its transparency and flexibility ( 32 ) and was well suited to the dual emphasis of the SMRI.

Aggregation

The final step in the construction of the SMRI is the aggregation of the normalized and weighted indicator values. Aggregation was performed in a hierarchical manner to preserve the conceptual structure of the index and ensure that all dimensions are proportionately represented.

First, the weighted and normalized indicators within each group were aggregated to produce dimension-level scores, reflecting performance in six core areas: transport supply, transport demand, innovation, policies, finance, and environment and energy. Subsequently, the dimension scores were aggregated to calculate the overall SMRI score for each city. This two-tiered approach ensures internal consistency and transparency in how individual indicators influence the final score. Additive aggregation was used to combine values, as outlined in the OECD composite indicator methodology ( 31 ). This method ensures that strong performance in one dimension can partially offset weaker performance in another, while still highlighting differences in underlying strengths across metropolitan areas.

Validation and Sensitivity Analysis

Following the construction of the SMRI, a validation and sensitivity analysis was conducted to assess its robustness, reliability, and policy relevance. Figure 3 visually represents the performance of the 26 evaluated cities, mapped through an interactive web-based tool. Validation involved comparing SMRI scores with external datasets, established benchmarks, and observed urban mobility trends to ensure consistency with city-level characteristics and performance trajectories.

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Figure 3.

Global map of Sustainable Mobility Readiness Index scores across 26 metropolitan areas as visualized on the web-based tool.

To assess the index’s responsiveness to real-world developments, a sensitivity analysis was conducted using planned or recent interventions in selected cities. This analysis tested the impact of new policies or infrastructure expansions on the index. For example, the opening of a metro line in Riyadh was used to estimate its effect on public transport indicators, while scenarios involving multimodal public transport investments, bicycle lane expansions, low-emission zones, and the integration of emerging technologies (e.g., CAVs, electric vehicles [EVs], e-bikes, e-scooters) were also analyzed.

This scenario-based approach demonstrated the SMRI’s ability to reflect policy shifts and mobility transitions. Results confirmed that the index is sensitive to significant improvements across relevant dimensions, yet stable under minor fluctuations, reinforcing its robustness and practicality for assessing sustainable mobility readiness in both current and future policy contexts ( 38 ).