  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.
