MaaS Demand Modeling & Service Coverage Analyzer
Generate data-driven MaaS demand forecasts and optimize multi-modal service coverage
What You Can Do
You can rapidly prototype MaaS network scenarios, forecast demand across multiple transportation modes, and optimize service coverage areas without building models from scratch. The skill guides you through structured analysis to balance operator economics (fleet sizing, vehicle utilization) with user accessibility (first-last mile coverage, wait times), typically compressing 2-3 weeks of transportation consultant work into 2-3 days.
Features
aggregate demand signals across ride-hailing, car-sharing, bike-sharing, and transit to build unified MaaS forecasts
test coverage expansion, pricing changes, fleet size adjustments, and service zone boundaries with rapid iteration
quantify cannibalization effects and predict how user behavior shifts between transportation modes
calculate vehicle, bike, and scooter requirements based on profitability targets and utilization constraints
identify optimal pickup/dropoff density requirements and first-last mile coverage gaps
generate demand-based revenue projections and stress-test economics under budget constraints
structure analysis that weighs operator profitability against user accessibility and market penetration
Example Output
Example 1: Demand Forecast for New MaaS Launch
- Peak-hour demand projection: 2,400 trips/day across 5 km² service zone
- Modal split: 35% car-sharing, 28% ride-hailing, 22% bike-sharing, 15% transit integration
- Fleet requirement: 180 shared vehicles + 450 bikes to achieve 12-min avg wait times
- Revenue model: $2.1M annual subscription + $1.8M pay-per-use across modes
Example 2: Service Coverage Optimization
- Current gaps: 18% of service zone has >15-min walk to nearest station
- Recommendation: Deploy 120 additional scooters in underserved corridors
- Impact: Reduces coverage gap to 4%, increases off-peak utilization by 22%
Example 3: Modal Cannibalization Analysis
- Ride-hailing reduction: 12% when car-sharing integrated into platform
- Transit ridership effect: +8% from first-mile/last-mile bike-sharing connections
- Net new mobility demand: +31% vs. standalone services
What's Included
- SKILL.md instruction file with multi-modal demand modeling methodology:
- MaaS demand scenario template with mode-specific parameters and elasticity assumptions:
- Fleet sizing calculator framework for vehicles, bikes, and scooters:
- Service coverage gap analysis checklist for identifying underserved zones:
- Modal split and cannibalization modeling worksheet:
- Business case validation framework linking demand forecasts to revenue/unit economics:
Who It's For
- MaaS program managers and mobility solutions planners launching integrated transportation platforms
- Transportation consultants developing market-entry strategies for shared-mobility operators
- City planners and transit agencies evaluating MaaS integration feasibility
- Venture-backed mobility startups building demand business cases for investor pitches
- Regional mobility operators sizing fleet and service zones for profitability
Best For
- Designing new MaaS service launches in untested markets
- Forecasting demand across multiple transportation modes (ride-hailing, car-sharing, bike-sharing, transit)
- Sizing fleet requirements based on profitability targets and utilization constraints
- Analyzing modal cannibalization effects and user behavior shifts
- Defining optimal service zones and pickup/dropoff density requirements
- Building investment-grade business cases with demand-based revenue projections
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