A Practical Framework for Online Driver Repositioning to Balance Supply and Demand in Ride‑Hailing Platforms

This article presents a three‑stage, data‑driven framework for online driver repositioning that generates candidate dispatch tasks, scores them using a marginal gain model, and selects optimal tasks via a minimum‑cost flow planning algorithm, demonstrating significant improvements in driver efficiency and experience through large‑scale A/B experiments.

AB testingdriver repositioningfleet management

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A Practical Framework for Online Driver Repositioning to Balance Supply and Demand in Ride‑Hailing Platforms

Didi Tech

Jul 16, 2020 · Operations

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning

The paper describes Didi’s online driver‑repositioning system that treats idle‑driver dispatch as a recommender problem, generating candidate destinations, scoring tasks with a marginal‑gain model, and selecting optimal assignments via a minimum‑cost‑flow optimizer, which in live A/B tests boosted driver efficiency, earnings, and satisfaction while reducing empty cruising.

AB testingRecommendation Systemsdriver repositioning

0 likes · 11 min read

When Recommender Systems Meet Fleet Management: A Practical Study on Online Driver Repositioning