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

With the rise of mobile internet, ride‑hailing has become a common travel option, yet platforms often suffer from supply‑demand imbalance, causing passengers to miss rides and drivers to idle. The paper defines the driver dispatch problem: the platform interrupts a driver’s self‑directed cruising and guides them to a destination with higher order‑receiving probability.

A dispatch task consists of four elements—driver, target location, expiration time, and compensation amount—and follows three possible successful outcomes (status 2 and 3) or a failure (status 4). Failed tasks trigger compensation to maintain driver trust.

The proposed solution consists of three stages. Stage 1: Candidate Generation filters idle drivers, selects candidate target cells (nearby grids, historically frequent grids, city hotspots), picks a POI within each cell, and sets expiration based on ETA. A failure‑probability model discards high‑risk tasks and determines compensation proportional to travel distance.

Stage 2: Task Scoring models the relationship between answer rate and supply‑demand ratio with a piecewise linear function, computes the marginal gain of adding an idle driver to a spatio‑temporal cell, and derives a driver‑gap metric for each cell.

Stage 3: Planning formulates the selection of final dispatch tasks as a constrained optimization problem that maximizes global platform benefit while respecting driver experience constraints; this is transformed into a minimum‑cost flow problem (see Figure 5).

The framework was deployed on the Didi platform and evaluated through multiple online A/B experiments. Results show increased driver efficiency, higher total income, and positive driver feedback (NPS 27.0%, 64.6% willingness to accept future tasks). The paper also discusses future directions such as reinforcement‑learning‑based end‑to‑end solutions and direct route optimization using road‑network data.

Reference: "When Recommender Systems Meet Fleet Management: Practical Study in Online Driver Repositioning System" (https://dl.acm.org/doi/abs/10.1145/3366423.3380287).