## 3 Methods
### Dual-Channel Deep Counterfactual Prediction Model for OD Matrix Prediction
#### 3.3.3 Loss Function and Prediction Accuracy Evaluation Metrics
By deploying similar predictive systems, a metro operator could automatically anticipate the magnitude of passenger displacement caused by an incident and initiate timely interventions. Another advantage of our counterfactual approach is its transparency: because the model separately estimates what would have happened in the absence of the incident, staff can directly compare the predicted \"normal\" versus \"incident-affected\" OD matrices. Such comparisons make the impact of the disruption immediately interpretable to planners and controllers, and they can see which origin-destination flows are most affected and by how much, rather than just receiving a single opaque forecast. This interpretability builds trust in the model's suggestions and aligns with the goal of improving situational awareness during crises [21]. The predictive capabilities of our model can be instrumental in real-time operational decision-making. For example, if the model forecasts a significant increase in passenger flow at a particular station due to an incident, transit operators can proactively add train runs or deploy shuttle buses to alleviate congestion. Similarly, if a station is predicted to become a bottleneck, temporary closure or controlled access measures can be implemented to ensure passenger safety and maintain service reliability. In practical terms, the enhanced interpretability and modular design (each channel serving a distinct role) mean the tool could function as a reliable \"advisor\" to human decision-makers, who remain accountable for service adjustments. Real-world use cases could include scenario analysis (e.g., \"If a certain station must close for 30 min, what are the expected OD flow changes and where should we allocate extra buses?\") and post-incident analysis (\"How much did this disruption divert trips or suppress demand compared to normal?\"). By providing data-driven answers to these questions, the model can assist transit agencies in not only reacting to incidents but also learning from them to improve resilience. ## 6 Conclusions

### Conclusions

This research introduces a novel approach to predicting passenger origin-destination (OD) matrices during operational disruptions in urban rail systems. A dual-channel deep counterfactual inference model is developed, integrating factual predictions with counterfactual estimations to predict passenger flows under both normal and incident conditions. This model explicitly enhances predictive robustness in the face of emergencies. The design of key components, such as tensor-based KL divergence for propensity matching between factual and counterfactual conditions, a shared layer for synergistic learning of knowledge across both channels, and the integration of incident type embedding features are critical innovations that improve model performance and interpretability. Our study makes three primary contributions. From a theoretical perspective, the dual-channel counterfactual inference model offers a sophisticated way to account for the causal effects of disruptions. From an interpretability standpoint, it provides valuable insights into spatiotemporal differences between normal and disrupted scenarios. Practically, it equips transit operators with actionable tools to assess real-time disruption impacts and implement effective emergency responses. Among the most significant insights of this study is the demonstration that KL divergence-based propensity matching can reliably utilize normal and disrupted passenger flow patterns, yielding a nuanced understanding of temporal disruption trends. Equally important is the incorporation of incident-type embeddings, which captures the heterogeneous delay effects most notably, the disproportionately large impact of power equipment failures compared to more frequent but less severe incidents. The analysis reveals consistent time-lag propagation, where disruptions immediately depress origin station flows, while destination and neighboring stations experience delayed and attenuated effects.