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CASE STUDY / AI & Data Science
Airport Taxi Demand Growth & Operations Pilot
Mobility Platforms, Ride Services & Airport TransportApplied mobility analytics, demand forecasting, and AI-enabled operational optimization to improve airport taxi utilization and service performance.
Delivery Details
This pilot focused on matching airport taxi demand and supply. Order, time, location, and trip features were analyzed within a demand forecasting and operational diagnostic framework to identify growth opportunities and service friction, then translated into recommendations for fleet allocation, operating strategy, and performance tracking.
Industry Context
Airport transport demand is shaped by flights, time, passenger behavior, and vehicle availability. Supply-demand mismatch creates waiting, idle capacity, and lost service, so platforms must connect forecasting with operational allocation to improve both experience and utilization.
Business Problem
Airport demand varied sharply by time and location, with supply mismatch affecting both utilization and passenger experience.
What I Built
Combined demand forecasting, trip analysis, and operational diagnostics to identify key periods, locations, and conversion friction.
Business Impact
Produced actionable supply-allocation and growth recommendations for airport mobility operations.
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