Experience alignment improves ride-hailing matching and driver revenue
ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDi
Machine Learning
Summary
Matching drivers and passengers efficiently in ride-hailing services can be tricky because preferences and behaviors vary widely and can be hard to measure. The authors developed ALIGN-HOLD, a system that learns from real ride data to decide when to delay matching to get better driver-passenger pairs. This approach uses a model that weighs different experiences and filters uncertain cases, leading to more completed trips, higher driver income, and fewer cancellations. They tested ALIGN-HOLD on DiDi’s Brazil platform and found it outperformed existing methods over a month.
What this means in practice
- •For ride-hailing platform engineers: Improve driver-passenger matching in live ride-hailing systems by learning hold policies from implicit marketplace preferences rather than handcrafted rewards.
- •For on-demand logistics operators: Optimize real-time agent-task matching in large-scale on-demand delivery platforms by adopting preference-based hold policies inspired by ride-hailing experience alignment.
Authors
Zuhao Zhang, Xu Liu, Kai Wan, Zihao Lu, Li Ma, Shuai Li
Abstract
Real-time hold control is a high-leverage mechanism in large-scale ride-hailing systems: by selectively deferring driver-order pairs, the platform can wait for better matching opportunities and improve end-to-end passenger-driver experience. Existing production systems such as EXHOLD learn bandit-based hold policies from handcrafted combinations of trip completion, cancellations, waiting time, and driver effort. However, designing such rewards becomes increasingly difficult as marketplace preferences are heterogeneous and observed passenger-driver behavior can be sparse, noisy, and affected by dynamic supply-demand conditions. We present ALIGN-HOLD, a production-scale experience alignment framework that learns hold policy from implicit marketplace preferences. ALIGN-HOLD constructs complementary preference pairs from order trajectories, driver trajectories, and contemporaneous local matching graphs, and trains an experience Reward Model (RM) using balanced multi-view sampling and model-adaptive hard preference sampling. During simulator-based policy learning, the frozen RM provides a dense, context-dependent reward and supports label-free filtering of low-identifiability interactions whose behavioral feedback is difficult to attribute to matching quality. We deploy ALIGN-HOLD on DiDi's ride-hailing platform and evaluate it in a 28-day randomized A/B experiment, covering approximately 100,000 passenger requests per day. Compared with the deployed production policy, ALIGN-HOLD achieves statistically significant improvements in trip completion rate and driver income, while significantly reducing passenger cancellations before and after driver acceptance. Complementary ablations, RM diagnostics, and behavioral analyses validate the contributions of the proposed components. ALIGN-HOLD has been fully ramped up and is currently serving DiDi's Brazil marketplace.