Uncertainty-Aware Probabilistic Travel Time Prediction for On-Demand Ride-Hailing at DiDi

Travel Time Estimation (TTE) aims to accurately forecast the expected trip duration from an origin to a destination. As one of the world's largest ride-hailing platforms, DiDi answers billions of TTE queries per day. The quality of TTE directly decides the customer's experience and the effectiveness of passenger-to-driver matching. However, existing studies mainly regard TTE as a deterministic regression problem and focus on improving the prediction accuracy of a single label, which overlooks the travel time uncertainty induced by various dynamic contextual factors. To this end, in this paper, we propose a probabilistic framework, ProbTTE, for uncertainty-aware travel time prediction. Specifically, the framework first transforms the single-label regression task to a multi-class classification problem to estimate the implicit travel time distribution. Moreover, we propose an adaptive local label-smoothing scheme to capture the ordinal inter-class relationship among soft travel time labels. Furthermore, we construct a route-wise log-normal distribution regularizer to absorb prior knowledge from large-scale historical trip data. By explicitly considering the travel uncertainty, the proposed approach not only improves the TTE accuracy but also provides additional travel time information to benefit downstream tasks in ride-hailing. Extensive experiments on real-world datasets demonstrate the superiority of the proposed framework compared with state-of-the-art travel time prediction algorithms. In addition, ProbTTE has been deployed in production at DiDi in late 2022 to empower various order dispatching services, and improves passenger and driver experiences significantly.