Demand Modeling & Market Estimation
Market sizing and forecasting for the Mobility on Demand market across all segments (by type, vehicle type, propulsion, booking type, commute type, and geography) were executed using a dual-methodology framework combining top-down and bottom-up approaches, reinforced by multi-level data triangulation.
Bottom-Up Market Estimation:
The bottom-up model was constructed by aggregating demand from the smallest definable market units upward to regional and global totals. The following specific metrics and variables were used as core inputs:
- Number of Registered MoD Vehicles per City Tier (two-wheelers, passenger cars, buses, and rail units actively enrolled in sharing or renting programs, segmented by ICE vs. electric propulsion)
- Average Trip Frequency per Vehicle per Day (differentiated by intracity vs. intercity commute type and booking channel — online vs. offline)
- Average Revenue per Trip (ARPT) (calculated by vehicle category, market, and service model — sharing vs. renting — adjusted for local purchasing power parity)
- Platform Commission Rate & Fleet Utilization Rate (percentage of gross booking value retained by MaaS platforms and the ratio of active vehicle hours to total available hours, used to compute net revenue contribution per market segment)
Top-Down Market Estimation:
The top-down approach began with the global transportation and mobility services total addressable market (TAM), sourced from OECD and UITP macroeconomic datasets. Market share was then allocated to the MoD sub-segment using penetration rate benchmarks derived from primary interviews and government transport utilization reports. Regional allocations were calibrated against GDP per capita, urbanization rates, smartphone and internet penetration, and existing public transit infrastructure density.
Multi-Level Data Triangulation:
All market estimates were triangulated across three independent data streams — (1) supply-side data from fleet operators and platform providers, (2) demand-side data from commuter surveys and booking platform analytics, and (3) macroeconomic and regulatory data from government and intergovernmental bodies. Discrepancies exceeding ±8% between streams triggered a secondary validation round with additional primary sources before final numbers were locked.
Regional forecasts for North America, South America, Europe, Middle East & Africa, and Asia Pacific were further stress-tested against scenario models (base, optimistic, and conservative) reflecting variables such as EV subsidy continuation, regulatory tightening on shared mobility, and post-pandemic urban mobility recovery trajectories.
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