Regime-Calibrated Demand Priors for Ride-Hailing Fleet Dispatch and Repositioning

1. 1Segment historical trip data into demand regimes
2. 2Match current operating period to most similar historical analogues using a similarity ensemble
3. 3Combine Kolmogorov-Smirnov distance, Wasserstein-1 distance, and feature distance to measure similarity
4. 4Use regime-calibrated demand priors to inform fleet dispatch and repositioning decisions

Data scientists and AI engineers on a ride-hailing company's team can benefit from this approach to optimize fleet management and improve customer experience

💡 Anticipating demand patterns with regime-calibrated demand priors can optimize fleet dispatch and repositioning

🚗💡 Improve ride-hailing fleet management with regime-calibrated demand priors