发表机构
Didichuxing Co. Ltd（滴滴出行科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模网约车匹配中等待控制问题，提出EXHOLD两阶段框架，第一阶段学习决策模型分配司机-订单对层级，第二阶段求解等待时间调度，经实验验证能提升市场效率和体验，已在巴西生产系统部署。
AI 中文摘要
在大规模网约车中,等待控制是改善乘客-司机体验的关键机制。现有行业等待策略常依赖多个预测模型的启发式阈值,在非平稳交通下脆弱且难针对多目标体验信号优化。我们提出EXHOLD,一个可部署的两阶段框架,将经验感知配对评估与等待时间执行解耦。第一阶段,通过优化统一目标学习决策模型,将每个司机-订单对分配到离散、可解释的经验层级。第二阶段,通过对经验分位数的约束优化求解单调等待时间调度。在巴西滴滴生产系统中通过随机A/B实验评估,结果显示在市场效率和体验上持续提升,消融和行为分析证实两阶段都重要,该策略在时空异质性下做出校准决策,目前已在巴西部署服务生产流量。
英文摘要
In large-scale ride-hailing, hold control is a critical mechanism for improving passenger-driver experience. By selectively delaying certain driver-order pairs, the system waits for better opportunities, reduces cancellations, and mitigates wasted driver effort. However, existing industrial hold strategies often rely on heuristic thresholding over multiple predictive models, which can be brittle under non-stationary traffic and hard to optimize for multi-objective experience signals. We propose EXHOLD, a deployable two-stage framework decoupling experience-aware pair assessment from hold-time execution. In Stage I, we learn a decision model assigning each driver-order pair to discrete, interpretable experience tiers by optimizing a unified objective that aggregates satisfaction signals across the matching funnel. In Stage II, we solve for a monotone hold-time schedule via constrained optimization over empirical quantiles. This explicitly enforces service guardrails bounding the unnecessary holding of promising matches while maximizing overall experience improvement. We evaluate EXHOLD through randomized A/B experiments in DiDi's production system in Brazil. Results show consistent gains in marketplace efficiency and experience: EXHOLD increases trip completion and driver income, significantly reduces passenger cancellations, and improves funnel efficiency. Ablations and behavioral analyses confirm both stages are essential and that the policy makes calibrated decisions under spatiotemporal heterogeneity. EXHOLD is currently deployed, serving production traffic in Brazil.
