SurvCancel: A Longitudinal Dataset and Benchmark for Dynamic Order Cancellation Prediction in On-Demand Ride-Sharing Systems

Passenger-order cancellation is a critical source of uncertainty in on-demand ride-sharing systems, where requests, assignments, and vehicle routes evolve continuously in real time. Due to shared capacity and coupled routes, a single cancellation may propagate through downstream matching and routing decisions, degrading system efficiency and service reliability. However, cancellation prediction in on-demand ride-sharing remains difficult to study systematically due to the lack of large-scale longitudinal datasets, standardized evaluation protocols, and fine-grained real-time benchmarks. In this work, we introduce SurvCancel, a real-world longitudinal dataset and benchmark for dynamic passenger-order cancellation prediction in on-demand ride-sharing systems. Constructed from operational logs collected across four service regions in the real world, SurvCancel contains 173K released orders and 7.9M time-dependent snapshots. Each order is represented as a longitudinal sequence of system states, capturing both order-level attributes and the evolving supply-demand environment throughout its lifecycle. We formulate cancellation prediction as a stage-conditioned dynamic survival task: at each landmark time, models estimate short-term cancellation risk from the historical state sequence observed so far. We evaluate representative survival and dynamic-risk models under both in-distribution and distribution-shift protocols. Our results show stage-dependent model behavior in the in-distribution setting and reveal distinct robustness patterns under regional and temporal distribution shifts.