Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems. As one of the world's largest ride-hailing platforms, DiDi places great emphasis on the accuracy of congestion prediction to enhance the effectiveness and reliability of their real-time services, such as travel time estimation and route planning. Despite numerous efforts have been made on congestion prediction, most of them fall short in handling heterogeneous and dynamic spatio-temporal dependencies (e.g., periodic and non-periodic congestions), particularly in the presence of noisy and incomplete traffic data. In this paper, we introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the above challenges. We first propose a sparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-aware inductive biases to improve the model capacity for efficiently capturing complex spatio-temporal dependencies in varying traffic scenarios. Then, we devise two specialized experts to help identify stable trends and periodic patterns within the traffic data, respectively. By cascading these experts with MAGLs, CP-MoE delivers congestion predictions in a more robust and interpretable manner. Furthermore, an ordinal regression strategy is adopted to facilitate effective collaboration among diverse experts. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method compared with state-of-the-art spatio-temporal prediction models. More importantly, CP-MoE has been deployed in DiDi to improve the accuracy and reliability of the travel time estimation system.

翻译：快速城市化显著加剧了交通拥堵，凸显了对先进拥堵预测服务的需求，以增强智能交通系统。作为全球最大的网约车平台之一，滴滴高度重视拥堵预测的准确性，以提升其实时服务（如行程时间估计和路线规划）的效能与可靠性。尽管在拥堵预测方面已有诸多努力，但大多数方法在处理异构且动态的时空依赖性（例如周期性和非周期性拥堵）方面存在不足，尤其是在面对噪声和不完整交通数据时。本文提出一种拥堵预测专家混合模型，CP-MoE，以应对上述挑战。我们首先提出一种稀疏门控的自适应图学习器混合模型，其具有拥堵感知的归纳偏置，以提高模型在不同交通场景中高效捕捉复杂时空依赖性的能力。随后，我们设计了两个专用专家，分别帮助识别交通数据中的稳定趋势和周期性模式。通过将这些专家与自适应图学习器混合模型级联，CP-MoE能够以更鲁棒且可解释的方式提供拥堵预测。此外，采用序数回归策略以促进不同专家之间的有效协作。在真实世界数据集上的大量实验表明，与最先进的时空预测模型相比，我们提出的方法具有优越性。更重要的是，CP-MoE已在滴滴部署，用于提升行程时间估计系统的准确性与可靠性。