Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems
Abstract
1. Introduction
1.1. Why MaaS Is the Future of Transportation
1.2. What Issue Is MaaS Trying to Solve?
1.3. Advantages of MaaS Implementation in Smart Cities
1.3.1. MaaS in Automobiles
1.3.2. The Potential for Recovering Investment Expenses
1.3.3. The Allocation of Funds Towards Developing and Maintaining Public Transit Networks
1.3.4. Minimize the Expenses Incurred by Individual Customers
1.3.5. MaaS for Businesses
1.3.6. Minimizing the Risk to Fleet Operations
1.3.7. Optimize the Utilization of the Fleet’s Resources
1.3.8. Methods for Reaching the Destination
1.4. Potential Risks of MaaS
1.4.1. Service Disruptions
1.4.2. Monopolization
1.4.3. Exclusion of Non-Digital Users
1.5. Key Contributions
- The study provides a year-wise analysis of MaaS application deployment from 2004 to 2025, offering empirical insights into the growing share of MaaS solutions relative to total mobility applications worldwide.
- Evaluate the sustainability index: a novel evaluative mathematical framework is introduced to assess the sustainability index and implemented using Python (Python 3 and Python 2.7) code.
- This study identifies the challenges in the realm of MaaS and highlights the opportunities.
2. MaaS Framework
2.1. Mathematical Model
- Users (U): they can make travel decisions based on cost, convenience, and time.
- Modes (M): transportation mode can be public transit, private car, bike, ride-share, etc.
- Service Platforms (MaaS): integrate services into a single digital interface.
- Infrastructure (roads, lanes, etc.).
- Environmental Factors (emissions, congestion).
- Cost Function (C).
- U(n): number of users choosing the mode of transportation.
- M = {1,2,3…..,n}: mobility options such as bus, car, bike, etc.
- C(Un): the cost function depends on service quality and road congestion.
- Pn: proportion of travelers choosing the mode n.
- Sn: sustainability index
- En: emissions per user for mode n.
- Tn: average travel time for mode n
- Fn: user preference factor (price, comfort, availability)
2.2. Data Collection
2.3. Smart Mobility
2.4. Sharing Mobility
2.5. Payment Solution
3. Related Works
3.1. An Increase Occurring in Quick and Consecutive Intervals
3.2. There Is a Heightened Focus on the Utilization of Open Data
3.3. Increased the Number of Autonomous Vehicles
3.4. Some Examples of Smart Cities Where MaaS Is Implemented
3.4.1. Helsinki, Finland
3.4.2. Vienna, Austria
3.4.3. Hanover, Germany
3.5. Smart Mobility Services
3.5.1. Mobility on Demand
3.5.2. The Concept of MaaS Refers to the Provision of Transportation
3.5.3. Smart Transportation
3.5.4. Motivating the Adoption of Electric Vehicles (EVs)
4. Analysis and Evaluation of MaaS Services
4.1. Data Analysis
4.2. Levels of Integration
4.3. Regional Variability Refers to the Differences or Variations That Exist in Different Regions or Areas
4.4. Enhanced Business Capabilities
4.5. Enhancements and Physical Structures for Services
4.6. Python Code for Evaluating Sustainability
5. Challenges and Opportunities
5.1. Challenges in the Realm of MaaS
5.1.1. The Incorporation of Payment Systems
5.1.2. The Subscription Model
5.1.3. The Process of Ticket Validation
5.1.4. The Process of Information Exchange Among Transportation Suppliers
5.1.5. Ticket Validation
5.1.6. The Efficacy of the Transit Service
5.2. Opportunities
- MaaS can enhance both the availability and the demand for transportation. The key advantage of MaaS would be the capacity to create a service that integrates all available modes of transportation, facilitating effortless and immediate transit from one location to another. MaaS offers numerous benefits to metropolitan areas. These factors heavily rely on the MaaS service, the market structure, the operational and governance model employed, and the effectiveness and involvement of all parties within the MaaS ecosystem, which includes local, regional, and national governments.
- MaaS might reduce the necessity for individuals to utilize or possess vehicles by enhancing the accessibility of various transportation alternatives and providing more educated assessments of the most suitable mode(s) of transportation to utilize in specific scenarios. Users can select the transportation mode, or a combination of modes, that most effectively fulfills their requirements for each journey. MaaS might consider special requirements for a trip, such as the necessity to transport bulky luggage or a pram, or the need for accessibility, in addition to fundamental travel preferences such as speed, convenience, comfort, and affordability. This is remarkably accurate for individuals who experience acute or chronic impairments in their ability to move.
- Straightforwardly presenting various modes of transport can additionally enhance the transparency regarding the actual expenses associated with mobility. Customers utilizing the effective integration of several transportation methods may save costs compared to the expenditure associated with car ownership. Suppose that the advantages of driving and possessing a vehicle have diminished, while the availability of public transport has improved. Under such conditions, customers may opt to utilize many modes of transportation to reach their desired destinations. One option is using public transport and subsequently engaging in walking or cycling to the closest station. These attributes would benefit both inhabitants and visitors, particularly those who are unfamiliar with and have difficulty understanding the intricacies of the local transport system.
- MaaS can enhance regional intermodal connections while simultaneously maximizing the use of existing transportation resources and services. During peak hours in urban regions, traditional public transport services such as buses, trams, and cabs are heavily utilized and often overcrowded. However, this is not the situation in suburbs and rural areas or for services provided during early mornings or late nights. Standardizing and sharing data sources among firms in the MaaS ecosystem can enhance network efficiency by enabling more precise and well-informed intermodal decisions, leading to shorter journey times. For instance, a MaaS operator may suggest fewer commonly used routes to clients, particularly those that experience significant demand during peak hours. This can also assist decision-makers in making more effective public investments that yield long-term benefits for all.
- A future in which mobility is less dependent on the ownership of expensive transport assets can lead to enhanced social inclusion, less isolation, and improved access to services, education, employment, and social interaction. MaaS’s customized approach to providing transport from one doorstep to another can establish a standard for creating environmentally friendly transport options for all individuals, especially those who face challenges in utilizing conventional public transport, such as the elderly and disabled. Engaging in physical and mental activities is widely acknowledged to have positive effects on health. As a result, the ability to move freely is considered a basic entitlement that promotes social and economic objectives. Due to the exorbitant expenses associated with conventional transport services, whether public or private, the quality of impaired transit exhibits significant disparities among different regions. Usually, government bodies or individuals create and endorse customized solutions.
- i.
- Improve the convenience and quality of travel by increasing personalization, efficiency, responsiveness, and predictability.
- ii.
- Transport them using the most optimal means of conveyance currently accessible.
- iii.
- Reduce the duration of travel in general.
- iv.
- Provide support in enhancing service management, strategizing, and monitoring.
- v.
- Minimized traffic congestion and its adverse impact on the environment.
- vi.
- The reduction of transportation expenses, increased availability of suppliers, and expanded accessibility of the service would be achieved.
6. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
Abbreviations | Full form |
MaaS | Mobility-as-a-Service |
IoT | Internet of Things |
AI | Artificial Intelligence |
ICT | Information and Communication Technology |
API | Application programming interface |
P2P | Peer-to-Peer |
CO2 | Carbon Dioxide |
V2I | Vehicle-to-Infrastructure |
MOD | Mobility on Demand |
EVs | Electric Vehicles |
HMM | Hidden Markov Model |
M2M | Machine-to-Machine |
NFC | Near-Field Communication |
GDPR | General Data Protection Regulation |
GTFS | General Transit Feed Specification |
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Ref. | Contribution | Limitation | Year |
---|---|---|---|
[28] | The authors present the MaaS and describe the framework of MaaS. | The authors only considered MaaS without observing the challenges in its implementation. | 2016 |
[29] | The authors present the challenges of implementation and the policies required for MaaS. | The authors only considered the challenges in MaaS implementation without giving the framework. | 2017 |
[30] | The authors present the MaaS in rural regions. | The authors only considered the MaaS implementation in rural areas; however, it is more needed in urban areas. | 2018 |
[31] | The authors present the potential of MaaS bundles as a mobility management tool. | The authors only considered the implementation of MaaS without providing the framework. | 2019 |
[32] | The authors present an understanding of MaaS and its past, present, and future. | The authors only provided an overview of MaaS without giving the framework. | 2020 |
[33] | The authors present the MaaS and private car use based on the Sydney MaaS trial project. | The authors only considered the MaaS and private car use without providing a framework to cover all transportation. | 2021 |
[34] | The authors present the models for supporting MaaS. | The authors only considered the MaaS models without providing the framework and challenges. | 2022 |
[35] | The authors present a model of the effect of MaaS on mode choice decisions. | The authors only considered the impact of MaaS implementation on mode choice decisions without providing a framework for demand. | 2022 |
[36] | The authors present the new frontiers for sustainable mobility: MaaS. | The authors only considered MaaS implementation for sustainable mobility without providing a framework. | 2023 |
[37] | The authors present the shared mobility services toward MaaS. | The authors only considered the shared mobility services toward MaaS without providing the framework and implementation challenges. | 2023 |
[38] | The authors present the MaaS and the role of multimodality in the sustainability of urban mobility in both developing and developed countries. | The authors have not presented a specific roadmap for implementing MaaS in developing countries. | 2024 |
[39] | The authors have focused on smart mobility adoption in sustainable smart cities to establish a growing ecosystem. | The authors have not presented the MaaS implementations. | 2024 |
[40] | The authors have presented a review of transportation 5.0, focusing on advancing sustainable mobility through intelligent technology and renewable energy. | The authors have not presented MaaS implementations. | 2025 |
Operators | Year | Implementation | Level | Modes of Transportation |
---|---|---|---|---|
GVH | 2004 | Hanover, Germany | Level 3 | Sharing vehicles, bicycles, park-and-ride lots, and taxis are all viable options. |
Gaiyo | 2008 | Utrecht (The Netherland) | Level 3 | Parking lots filled with taxis, carshares, scooters, and bikeshares |
Gojek | 2010 | Indonesia | Level 2 | Delivery drivers, bicycle cabs, and bluebird taxis |
imbric | 2011 | Spain and Europe | Level 2 | Vehicle storage, motorcycle rental, and cab and bus |
Citymapper | 2011 | 31 Countries | Level 2 | Transportation networks including car-sharing, car-rental, bike-sharing, taxis, urban public transportation, and regional public transportation. |
Divia Mobilites | 2012 | France | Level 2 | Park-and-ride services, bicycles, buses, trams, carpooling, taxis, bike-sharing, urban public transportation, and rural public transportation. |
moovit | 2012 | 12 countries | Level 1 | Ridesharing, taxis, bicycle rentals, and urban physical therapy |
ISTmobil | 2013 | 160+ municipalities | Level 2 | Public transport system |
Fluidtime | 2013 | Sweden | Level 3 | Ridesharing, taxis, bicycle rentals, and urban physical therapy |
ULU | 2014 | Slovenia | Level 2 | Connecting cars to the internet. |
MyCicero | 2015 | Italy | Level 2 | Permits for parking in urban congestion pricing zones, as well as permits for parking in other urban and regional areas |
ReachNow | 2015 | Hamburg, Germany | Level 2 | Carpooling (by bus, tram, shared bicycles, or electric scooters) |
wegfinder | 2015 | Austria | Level 2 | Carpooling (by bus, tram, shared bicycles, or electric scooters) |
Free2Move | 2016 | Europe and USA | Level 1 | Charging stations at valet parking lots, car-sharing locations, and rental vehicle agencies |
moovizy | 2016 | St. Etienne, France | Level 2 | Guidance on many modes of transportation including but not limited to railways |
Modalizy | 2017 | Europe | Level 3 | Transportation options include walking, biking, carpooling, driving, and taking the bus or subway. |
S’hail | 2017 | Dubai, UAE | Level 1 | Metro, tram, taxi and bus |
Whim | 2017 | Globally | Level 3 | Ridesharing, taxis, bicycle rentals, and urban physical therapy |
Compte mobilite | 2017 | Mulhouse metropolitan Area, France | Level 3 | Public transport (bus and tram), bicycles, carsharing, parking, e-bikes |
MobiFlow | 2017 | Flanders, Wallonia and Brussels | Level 2 | Charging stations at valet parking lots, car-sharing locations, and rental vehicle agencies |
Pick | 2018 | Portugal | Level 2 | Transportation services such as carpooling, car rentals, taxis, bikeshare programs, and public transportation in urban and rural areas |
Quicko | 2018 | São Paulo and 5 other cities in Brazil | Level 1 | Electric bikes, buses, and trams, as well as car-sharing and parking options. |
WienMobil | 2018 | Vienna, Austria | Level 2 | Bicycle, cab, train, bus, tram, power outlets |
Beep | 2019 | US | Level 3 | Shared mobility, reducing carbon emissions and traffic congestion |
EMOT | 2019 | Japan | Level 2 | Transportation options include bus, train, metro, ferry, transportation, parking, and bike sharing |
Jelbi | 2019 | Berlin (Germany) | Level 3 | You can take the bus, the train, the underground, the tram or the car you parked in a lot. |
Mobiliteits | 2019 | Luxembourg | Level 2 | Parking, public transportation, ridesharing, taxis, and bike share |
SNCF Assistant | 2019 | Paris, France | Level 3 | Transportation options include taxis, shuttles, buses and on-demand carpooling/bicycle pooling |
tarc | 2019 | Louisville, USA | Level 2 | You can take the underground, bus, train, bus, cab, shuttle, carpool, taxi, moped, or bike. |
yuwway | 2019 | France | Level 2 | Transportation options (bikes, buses, trains, trams) and power sources transport and parking issues |
BerlinMobil | 2019 | Berlin, Germany | Level 1 | Options for public transport include: |
Arevo | 2019 | Melbourne, Australia | Level 1 | Transportation options include trains, buses, cars, rides, and scooters. |
hvv switch | 2020 | Hamburg, Germany | Level 2 | Transportation services such as carpooling, car rentals, taxis, bikeshare programs, and public transportation in urban and rural areas |
Mobility Inside | 2020 | Germany | Level 3 | Mobility options include car, bus, train, bike, and flexible carpooling |
Move Brussels | 2020 | Brussels (Belgium) | Level 2 | Costs for a variety of transport options |
Umaji | 2020 | Taiwan | Level 3 | Transportation options (bus, train, and commuter rail) auto |
yumuv | 2020 | Switzerland | Level 3 | Transit on demand, carsharing, bike sharing, scooter sharing, ridesharing, taxi pooling, taxis, and rented automobiles |
Citen | 2020 | Paris, France | Level 2 | Ridesharing, electric bikes, scooters, buses, and bikes |
DerbyGo | 2021 | Derby, UK | Level 2 | Transportation sharing, taxis, rental cars, cars, bikes, public transit, and public transport outside of major cities. |
Glimble | 2021 | Netherland | Level 2 | Transportation (including buses and trams), automobiles, parking, bicycles and electric bikes, taxis, bikes, vehicles, and scooters that can be shared |
GoHI | 2021 | Scotland | Level 2 | Buses, trains, taxis, bike rentals, car clubs, flights, ferries, and DRT are only a few available transportation modes. |
Jak Lingko | 2021 | Indonesia | Level 2 | Common-carrier ridesharing, BRT, trains, and railways. |
Gireve | 2022 | France and UK | Level 3 | Reserve, remunerate, and authenticate rail tickets, linking an electric automobile to a charging station. |
Umob | 2023 | US | Level 3 | Discover, book, and pay for any kind of transportation. |
MaaS Saudi | 2024 | Saudi Arabia (Riyadh, Jeddah) | Level 3 | Metro, taxis, ride-hailing, park-and-ride, and e-scooters |
DelhiMobility | 2024 | India (Delhi NCR) | Level 2 | Metro, buses, e-rickshaws, parking, and shared e-bikes |
RideLink MX | 2024 | Mexico | Level 2 | Bike-sharing, minibuses, e-taxis, and metro |
FlexMove UK | 2024 | UK (Manchester) | Level 3 | Buses, suburban rail, bikes, carshare, and parking |
MaaS Bharat | 2025 | India | Level 2 | Buses, trains, autorickshaws, e-cycles, and intercity cabs |
CairoGo | 2025 | Egypt (Cairo) | Level 2 | Metro, buses, tuk-tuks, ferries, and car-hailing |
SmartRideSG | 2025 | Singapore | Level 3 | Buses, shared bikes/scooters, AI planning, and payment integration |
eMove Lagos | 2025 | Nigeria (Lagos) | Level 2 | Minibuses (danfo), ferries, BRT, and bikes |
MetroMaaS USA | 2025 | USA (Boston or Seattle) | Level 3 | Subway, commuter rail, bus, shared mobility, and smart ticketing. |
import numpy as np import pandas as pd modes = [‘Bus’, ‘Car’, ‘Bike’, ‘Metro’] N = 1000 # total users P = np.array([0.05, 0.005, 0.002, 0.5]) # Eco-friendly proportions K = np.array([50, 5, 2, 500]) # capacity of each mode E = np.array([0.05, 0.2, 0.01, 0.03]) # emissions per user t = 30 # base travel time in minutes alpha = 0.5 # congestion sensitivity E_max = 0.3 # worst-case emissions T_max = 60 # worst-case travel time w1, w2 = 0.5, 0.5 # sustainability weights U = P * N # number of users per mode T = t * (1 + alpha * (U/K)) # travel time per mode sum_UE = np.sum(U * E) # total emissions sum_UT = np.sum(U * T) # total travel time |
# Sustainability Index S = w1 * (1 − sum_UE/(N * E_max)) + w2 * (1 − sum_UT/(N * T_max)) # Display the results in a DataFrame df = pd.DataFrame({ ‘Mode’: modes, ‘Proportion (Pn)’: P, ‘Users (Un)’: U, ‘Capacity (Kn)’: K, ‘Emissions/User (En)’: E, ‘Travel Time (Tn)’: T, ‘Un × En’: U * E, ‘Un × Tn’: U * T }) print(“\n--- Sustainability Index ---”) print(f”Sustainability Index (Sn): {S:.3f}”) print(“\n--- Mode-wise Breakdown ---”) print(df) |
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Alam, T. Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems. Future Transp. 2025, 5, 94. https://doi.org/10.3390/futuretransp5030094
Alam T. Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems. Future Transportation. 2025; 5(3):94. https://doi.org/10.3390/futuretransp5030094
Chicago/Turabian StyleAlam, Tanweer. 2025. "Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems" Future Transportation 5, no. 3: 94. https://doi.org/10.3390/futuretransp5030094
APA StyleAlam, T. (2025). Breaking the Traffic Code: How MaaS Is Shaping Sustainable Mobility Ecosystems. Future Transportation, 5(3), 94. https://doi.org/10.3390/futuretransp5030094