DART: Navigating Last-Mile Heterogeneity in Instant Delivery via Distribution-Adaptive Splines

Abstract

On-demand delivery platforms rely on Travel Time Estimation (TTE) to balance courier earnings and overdue risks. In collaboration with one of China's largest platforms, we address a critical "Fairness Gap" in TTE: current systems fail to capture complex delivery patterns in GNSS-denied environments, subjecting couriers handling high concurrent order volumes to disproportionate pressure due to overdue deliveries. Analyzing 1.27 million real-world trajectories, we attribute this bias to unique challenges in GNSS-denied scenarios: distributional heterogeneity, structural heterogeneity, and contextual uncertainty. To bridge this gap, we propose DART (Distribution-Adaptive Robust Timing). DART incorporates a Learnable Adaptive Spline (LAS) encoder with a gradient-driven knot migration mechanism to enhance non-linear expressiveness for outliers, significantly improving long-tail accuracy. Furthermore, a Spatio-Temporal Transition Graph (STTG) reconstructs the latent topology by integrating sequence semantics, such as Wi-Fi-sensed arrival merchant timestamps. At the same time, a Distribution Gating Mechanism characterizes delivery time distributions under distinct contexts. Through extensive experiments and large-scale online A/B testing, DART not only reduces MAE by 14.0% in complex environments but also decreases the Order Overdue Rate by 1.7% (saving $24,000 daily), demonstrating how AI effectively reconciles operational efficiency with labor fairness.