Urban areas across the globe are in the midst of a transportation revolution. The rise of Mobility-as-a-Service (MaaS) platforms promises to reshape how people move, offering integrated, on-demand access to a variety of transport modes—from public transit and ride-sharing to bike-sharing and car rentals—all through a single digital interface. As cities grapple with chronic congestion, pollution, and the need for more efficient use of limited infrastructure, understanding the true impact of MaaS on urban traffic has become a top priority for planners, policymakers, and transportation researchers. Without rigorous analysis, the introduction of MaaS could lead to unintended consequences, such as increased vehicle miles traveled or inequitable access. This is where computational modeling steps in: by simulating how travelers respond to MaaS offerings under different conditions, cities can anticipate shifts in traffic patterns and design evidence-based strategies to maximize the benefits of this emerging mobility paradigm.

Understanding Mobility-as-a-Service (MaaS) Platforms

MaaS is more than just a trip-planning app; it is a paradigm shift in transportation. At its core, a MaaS platform aggregates multiple transport services—public buses and trains, ride-hailing, car-sharing, bike-sharing, scooter rentals, and even taxi services—into a single, unified experience. Users can plan a journey, compare modes by cost and time, book, and pay for the entire trip through one account and app. For example, a commuter might take a shared bike to a train station, ride the train to the city center, and then take a ride-share for the last mile, all coordinated through a single interface.

The benefits touted by MaaS promoters include reduced reliance on private car ownership, lower transportation costs for users, and more efficient use of existing infrastructure. Cities like Helsinki (with the Whim app), Vienna (with WienMobil), and Singapore are early adopters. However, the actual impact on traffic congestion, emissions, and overall mobility depends heavily on adoption rates, behavioral changes, and how the system is governed. To forecast these outcomes accurately, planners turn to sophisticated traffic models.

Why Modeling the Traffic Impact of MaaS Matters

Introducing a MaaS platform into a city’s transport ecosystem is not a simple addition; it triggers complex, often nonlinear interactions. A shift away from private cars might free up road capacity, but the convenience of MaaS could also induce new trips—people might choose to make more journeys if travel becomes easier and cheaper. If not managed, this could counteract congestion reductions. Moreover, the effects vary by time of day, location, and user demographics. Traditional static traffic models are inadequate for capturing these dynamics. Hence, researchers and transport authorities develop and use advanced simulation models—often agent-based or system dynamics models—that can represent individual traveler decisions, real-time network conditions, and policy interventions.

These models allow cities to answer critical questions: How will different pricing structures affect mode choice? What will happen if MaaS adoption reaches 30% or 60%? Which areas will see the most congestion relief, and where might problems worsen? Without modeling, cities risk making multimillion-dollar infrastructure and policy decisions based on guesswork.

Key Modeling Approaches and Parameters

Modeling the impact of MaaS on urban traffic typically involves a combination of approaches. The most common are agent-based models (ABMs), which simulate the behavior of individual “agents” (travelers) who have preferences, constraints, and learning abilities. Another approach is system dynamics, which models feedback loops between variables like travel demand, congestion, and mode availability. Regardless of the method, several key parameters are essential to building realistic simulations.

User Adoption Rates

How quickly and extensively do residents adopt MaaS? This parameter is not fixed—it depends on factors such as awareness, app usability, pricing, and available modes. Models often incorporate diffusion curves (e.g., S-curves) to simulate adoption over time. Sensitivity analyses test scenarios with slow, moderate, and rapid uptake. For instance, a study of MaaS in Sweden found that adoption rates are heavily influenced by the availability of seamless payment and journey planning, as well as the perceived reliability of shared modes.

Mode Shift Patterns

MaaS encourages a shift from private cars to shared and public transport. But not all shifts are equal; some users may switch from bus to bike-share without reducing road traffic, others from car to ride-hail, which—if not pooled—can still contribute to congestion. Models must capture the mode-specific transitions and their net effect on vehicle miles traveled (VMT). Empirical data from early MaaS implementations and stated preference surveys help calibrate these parameters.

Trip Length and Frequency

The convenience of MaaS can affect both the length and number of trips. Users might make more trips or combine trips differently (e.g., splitting a single car trip into a train and a bike-share segment). Models need to account for induced demand: the possibility that easier mobility generates additional travel, partly offsetting the benefits of mode shift. Real-world examples from ride-hailing studies show that induced demand can be significant, so ignoring it would lead to overly optimistic projections.

Network Capacity and Infrastructure

Existing transport networks have finite capacity. When MaaS adds new modes (e.g., e-scooters) or increases the use of others (e.g., bike lanes), the network’s ability to handle those changes is crucial. Models incorporate road and transit capacities, as well as the availability of dedicated bike lanes, scooter parking zones, and ride-hailing pick-up/drop-off areas. Without such detail, simulations may overlook bottlenecks that could negate potential benefits.

Simulating Different Scenarios

Once a model is built and calibrated, researchers run multiple scenarios to explore future possibilities. Typical scenarios include:

  • Baseline Scenario: No MaaS platform; current travel patterns persist.
  • Low Adoption Scenario: 10–20% of travelers use MaaS, with limited impacts on mode shift and trip frequency.
  • High Adoption Scenario: 50% or more, often associated with aggressive policy support (e.g., congestion pricing, parking taxes).
  • Policy Intervention Scenarios: Combinations of MaaS with measures like dynamically priced ride-hailing, dedicated bus lanes, or subsidies for bike-share trips.

Some studies also simulate spatial variations—for instance, introducing MaaS only in the city center versus metropolitan-wide. The outputs include changes in traffic volumes on key corridors, average travel times, emissions levels, and overall accessibility scores. These outputs guide decision-makers in setting priorities and identifying unintended consequences.

For example, a simulation of MaaS in Helsinki (as part of the Whim pilot) indicated that if MaaS achieved significant adoption, private car use could drop by up to 20% in the inner city, leading to a 10% reduction in CO2 emissions. However, the same study noted that without complementary policies, ride-hailing vehicles could increase deadheading (empty trips) by up to 15%, partially offsetting the congestion benefits. Such findings underscore the need for integrated planning.

Analyzing Potential Outcomes: Benefits and Risks

Modeling studies consistently point to several potential positive outcomes, but also highlight important risks and trade-offs.

Reduced Private Vehicle Ownership and Use

One of the most cited benefits is a reduction in private car trips. When MaaS makes multi-modal travel as convenient as driving, many users choose not to own a car or use it less. This leads to fewer vehicles on the road during peak hours, lowering congestion. A 2021 study from the University of Sydney found that a fully integrated MaaS system could reduce traffic density in urban cores by 15–25%.

Lower Emissions

With fewer private car trips and more use of electric public transit and shared bikes, urban emissions can decline. However, this benefit is conditional: if ride-hailing vehicles are gasoline-powered and often drive empty between trips, the net effect may be small. Models that account for fleet composition and deadheading provide more accurate projections.

Equity Concerns

MaaS platforms are digital and require smartphones and credit cards, potentially excluding lower-income and elderly populations. Moreover, if ride-hail services predominantly serve affluent neighborhoods, existing inequalities could worsen. Models that include demographic distributions can highlight which areas experience improved mobility and which do not, enabling targeted subsidies or complementary services.

Induced Demand and Rebound Effects

As mentioned, the convenience of MaaS can encourage more frequent trips or longer distances. For example, a person previously commuting by bus may switch to a ride-hail to a train station, adding a vehicle trip. Or a bike-share may replace a walking trip, then induce a new car trip later. Models that ignore these dynamics may overstate benefits. Including a rebound factor (often 10–20% of the initial reduction is lost) yields more realistic outcomes.

Impact on Public Transit

MaaS is often positioned as a complement to public transit, providing first/last-mile connections. However, it could also cannibalize transit ridership if people switch from buses to cheaper shared rides. Modeling can help identify the conditions under which transit ridership stabilizes or declines, guiding fare integration and routing decisions.

Policy Implications and Recommendations

The insights from traffic modeling translate directly into actionable policies. Some recommendations that commonly emerge include:

  • Congestion Pricing: Charging for road use, especially in dense areas, can make private car trips less attractive and nudge users toward MaaS options. Models can test optimal pricing levels and zones.
  • Dedicated Lanes and Infrastructure: Providing priority lanes for buses, bikes, and high-occupancy vehicles ensures that shared modes remain fast and reliable, encouraging mode shift.
  • Integration with Public Transit: MaaS subsidies for transit trips or integrated monthly passes can strengthen the complementarity rather than competition.
  • Data Sharing Mandates: To keep models accurate, cities need access to MaaS platform data (privacy-protected). Regulatory requirements for data sharing enable ongoing monitoring and model refinement.
  • Progressive Pricing for Ride-Hailing: Dynamic fees based on time and location can reduce deadheading and discourage non-pooled rides during peak hours.

For instance, a model-based study in Singapore tested the combination of MaaS with a distance-based congestion charge. The simulation showed that this policy mix could reduce traffic volumes by 18% while increasing average speeds by 12%. Such evidence helps build public and political support for potentially controversial measures.

Challenges and Limitations of Current Modeling Efforts

Despite their power, traffic models for MaaS face significant challenges. First, data quality and availability: MaaS is relatively new, and long-term behavioral data are scarce. Models often rely on surveys, pilot studies, or assumptions derived from similar services (like Uber or Lyft), which may not transfer directly to the integrated MaaS context.

Second, human behavior is adaptive and often irrational. Models based on economic rationality may not capture habits, reluctance to switch modes, or trust issues. Advanced ABMs incorporate psychological factors, but they are data-intensive and computationally expensive.

Third, the transportation landscape is constantly evolving—new vehicle technologies, changing land use, and unforeseen events (e.g., pandemics) rapidly alter travel patterns. Models must be regularly updated and validated against real observations to remain useful.

Fourth, model transparency and reproducibility are concerns. Many simulations are proprietary or too complex to be fully understood by all stakeholders. Open-source modeling frameworks and standardized benchmarks can help address this.

Finally, modeling alone cannot replace real-world experimentation. Many cities are conducting pilot programs alongside their simulation work. The combination of modeling and field tests offers the most robust foundation for decision-making.

Case Study: The Vienna MaaS Simulation

Vienna is one of several European cities that has extensively modeled the potential impact of MaaS. Its mobility agency, Wiener Linien, partnered with researchers to develop an agent-based model of the metropolitan area, populated with synthesized traveler profiles based on census and travel survey data. They simulated the introduction of a new MaaS platform (based on the existing WienMobil app) with different pricing tiers, parking policies, and public transit investments.

The study found that a MaaS platform offering a “mobility subscription” (a monthly fee covering unlimited public transit plus a certain number of ride-hails and bike-shares) could reduce private car trips by 25% in the inner city. However, the model also revealed a potential 30% increase in bike-share usage that would require expanding cycling infrastructure. The city used these results to justify a new cycle superhighway and to calibrate the pricing of the subscription plan. The case illustrates how modeling can provide actionable, spatially specific guidance.

Conclusion

Modeling the impact of MaaS on urban traffic is not merely an academic exercise—it is a critical tool for navigating the transition to more sustainable and equitable transportation. By simulating user adoption, mode shifts, trip patterns, and network constraints, cities can anticipate both the opportunities and the pitfalls of integrated mobility platforms. Strong modeling practices, grounded in real data and transparent methodologies, empower planners to design policies that maximize congestion relief, emission reductions, and accessibility gains while mitigating unintended consequences like induced demand and equity gaps.

As MaaS platforms continue to mature and spread, so too must the models that underpin their governance. Cities that invest in robust simulation capabilities—supported by data management systems like Directus that can integrate diverse data sources—will be better positioned to make informed decisions. The journey toward seamless, shared mobility is complex, but with careful modeling and adaptive policymaking, urban traffic need not be a casualty of that progress.