Accurate Travel Time Estimation (TTE) is critical for ride-hailing platforms, where errors directly impact user experience and operational efficiency. While existing production systems excel at holistic route-level dependency modeling, they struggle to capture city-scale traffic dynamics and long-tail scenarios, leading to unreliable predictions in large urban networks. In this paper, we propose \model, a scalable and adaptive framework that synergistically integrates link-level modeling with industrial route-level TTE systems. Specifically, we propose a spatio-temporal external attention module to capture global traffic dynamic dependencies across million-scale road networks efficiently. Moreover, we construct a stabilized graph mixture-of-experts network to handle heterogeneous traffic patterns while maintaining inference efficiency. Furthermore, an asynchronous incremental learning strategy is tailored to enable real-time and stable adaptation to dynamic traffic distribution shifts. Experiments on real-world datasets validate MixTTE significantly reduces prediction errors compared to seven baselines. MixTTE has been deployed in DiDi, substantially improving the accuracy and stability of the TTE service.

翻译：准确的行程时间估计（TTE）对网约车平台至关重要，其误差直接影响用户体验与运营效率。现有生产系统虽擅长整体路径级依赖建模，却难以捕捉城市尺度的交通动态与长尾场景，导致在大型城市路网中预测不可靠。本文提出MixTTE，一个可扩展的自适应框架，将路段级建模与工业级路径TTE系统协同整合。具体而言，我们设计了时空外部注意力模块，以高效捕捉百万级路网中的全局交通动态依赖；构建了稳定化的图专家混合网络，在保持推理效率的同时处理异构交通模式；并定制了异步增量学习策略，实现对动态交通分布漂移的实时稳定适应。在真实数据集上的实验表明，MixTTE相较七种基线方法显著降低了预测误差。该模型已在滴滴平台部署，大幅提升了TTE服务的准确性与稳定性。