Revolutionary approach to predicting AAM market adoption through individual-level behavioral simulation. Simulate millions of agents across 180+ European regions with unprecedented accuracy.
Simple steps to powerful insights
Select from a list of 180+ European regions, with 40+ million agents, carefully curated with real datapoints, covering 50+ data categories.
Seamlessly adjust dozens of parameters that reflect your business assumptions and create the perfect macro conditions to test the agents.
Execute simulations up to 60 months in length, processing millions of individual agent decisions in as little as 65 minutes.
Generate an incredibly detailed dashboard with 85+ metrics sourced from real and synthetic data for unprecedented decision confidence.
Product is currently tailored for VTOL, cTOL and Passenger aircraft and drones. For custom use cases involving other transport methods, contact us.
Agent-Based Modeling (ABM) Simulation
Unlike traditional top-down forecasting, our ABM engine simulates millions of unique individuals across regions. Each agent has precise demographic characteristics derived from real and official data, including age, income quintile, employment sector, education level, household composition, among many others.
Every agent makes autonomous monthly decisions using utility-based choice models. The engine evaluates transport alternatives based on individual preferences for time, cost, comfort, and safety—calibrated to real-world travel patterns. Seamlessly adjust 50+ variables to tailor forecasting.
Agents interact uniquely with evolving social networks that grow based on shared experiences. Time-based market maturity, social proof from network connections, and personal experience create non-linear adoption patterns unique to each agent profile, making this a unique forecasting tool.
Transport module that introduces competition between cars, buses, trains, ferries and planes, based on a mix of real and synthetic data. Agents choose between transport modes based on personal traits, commuting times, costs, suitability for their specific journey needs and preferences, and many other factors.
Captures complex market dynamics that emerge from millions of individual decisions, revealing adoption patterns invisible to aggregate models
Every agent is unique with distinct preferences, constraints, and social connections based on real demographic distributions
Models how adoption spreads through social networks, with influential nodes accelerating or decelerating market penetration
Agents learn from their eVTOL experiences and adjust future behavior, creating realistic adoption acceleration or resistance
Test subsidies, infrastructure investments, and regulatory changes at the individual decision level for precise impact assessment
Multiple simulation runs with behavioral randomization provide statistical confidence bounds on adoption forecasts
Hot/warm/cold agent storage optimizes 4M agent processing to just 6GB RAM through intelligent caching
Batch processing across 32+ CPU cores enables 60-month simulations in 65 minutes
Separate modules for agent memory, social networks, S-curve logic, and data capture ensure maintainability
Comprehensive monthly state capture enables deep post-simulation analysis and visualization
Agent characteristics preserve exact NUTS2 regional statistics for age, income, employment distributions
Simultaneous evaluation of eVTOL/cTOL and other passenger drones adoption, rail, car, ferry, buses, planes and no-travel options based on actual infrastructure data
Transform uncertainty into strategic advantage*
Join industry leaders who trust ClutchRoute for market entry strategy
* Platform is currently in early access, seats are limited.
Powerful new features launching soon
Weather impact on route operations and agent behaviour
Vertiport construction locations scouting (eVTOL) and leveraged existing infrastructure (cTOLS)
North American markets add-on with real data - United States of America, Canada And Mexico
GCC markets add-on - Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates
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