  Metro Origin-Destination (OD) prediction is a crucial yet challenging
spatial-temporal prediction task in urban computing, which aims to accurately
forecast cross-station ridership for optimizing metro scheduling and enhancing
overall transport efficiency. Analyzing fine-grained and comprehensive
relations among stations effectively is imperative for metro OD prediction.
However, existing metro OD models either mix information from multiple OD pairs
from the station's perspective or exclusively focus on a subset of OD pairs.
These approaches may overlook fine-grained relations among OD pairs, leading to
difficulties in predicting potential anomalous conditions. To address these
challenges, we analyze traffic variations from the perspective of all OD pairs
and propose a fine-grained spatial-temporal MLP architecture for metro OD
prediction, namely ODMixer. Specifically, our ODMixer has double-branch
structure and involves the Channel Mixer, the Multi-view Mixer, and the
Bidirectional Trend Learner. The Channel Mixer aims to capture short-term
temporal relations among OD pairs, the Multi-view Mixer concentrates on
capturing relations from both origin and destination perspectives. To model
long-term temporal relations, we introduce the Bidirectional Trend Learner.
Extensive experiments on two large-scale metro OD prediction datasets HZMOD and
SHMO demonstrate the advantages of our ODMixer. Our code is available at
https://github.com/KLatitude/ODMixer.
