面向城市交通拥堵预测的可解释级联混合专家模型
Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
AI总结：
针对交通拥堵预测中异构动态时空依赖难捕捉、数据含噪缺失的问题，本文提出带拥堵感知偏置的级联混合专家模型CP-MoE，结合有序回归实现多专家协作，性能优于现有模型且已部署于DiDi提升行程时间估计效果。
摘要：
快速城镇化进程大幅加剧了交通拥堵，凸显了先进拥堵预测服务对支撑智能交通系统的必要性。作为全球最大的网约车平台之一，DiDi高度重视拥堵预测的准确性，以提升行程时间估计、路径规划等实时服务的效能与可靠性。尽管现有研究已在拥堵预测领域开展大量工作，但多数方法难以应对异构且动态的时空依赖（例如周期性与非周期性拥堵），尤其是在交通数据存在噪声和缺失的情况下表现不佳。本文提出一种拥堵预测混合专家模型CP-MoE，以解决上述挑战。我们首先提出一种带拥堵感知归纳偏置的稀疏门控自适应图学习器混合模型（MAGLs），提升模型在多变交通场景中高效捕捉复杂时空依赖的能力。随后，我们设计了两个专用专家，分别用于识别交通数据中的稳定趋势与周期性模式。通过将这些专家与MAGLs级联，CP-MoE以更鲁棒、更具可解释性的方式输出拥堵预测结果。此外，模型采用有序回归策略，促进不同专家之间的有效协作。在真实世界数据集上开展的大量实验表明，与现有最先进的时空预测模型相比，本文所提方法性能更优。更重要的是，CP-MoE已部署于DiDi平台，用于提升行程时间估计系统的准确性与可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
