## 3 Methods
### Dual-Channel Deep Counterfactual Prediction Model for OD Matrix Prediction
#### 3.3.3 Loss Function and Prediction Accuracy Evaluation Metrics
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## 4 Results

The analysis describes the study area and dataset and then presents a systematic evaluation of model performance. Prediction accuracy is assessed across multiple temporal granularities and passenger-flow conditions using MAE MAPE and RMSE. Ablation experiments quantify the impact of key methodological components including KL divergence-based propensity matching graph contrastive embeddings and incident-type features. A detailed analysis of incident categories uncovers heterogeneous delay characteristics and spatiotemporal propagation phenomena that underscore the model's operational relevance. ### Study Area and Dataset

This research focuses on the Shanghai URT system, comprising 20 lines and 409 stations. With an average daily ridership of approximately 11.41 million passengers on weekdays, the system faces significant challenges in optimizing operational resources and improving service quality. This study integrates multiple datasets to model passenger flow patterns and the effects of operational disruptions, including the Shanghai URT smartcard dataset, land use data, and multimodal public transport network data. The lines and stations distribution of Shanghai urban rail transit network are presented as Figure 4.

The Shanghai URT smartcard dataset covers weekdays and weekends from 6:00 a.m. to 10:00 p.m., spanning the period from 1 September to 28 September 2023. For the temporal analysis, the dataset is divided into four time intervals per day: 96 intervals for 10 min segments, 48 intervals for 20 min segments, 32 intervals for 30 min segments, and 16 intervals for 60 min segments. The dataset is split into training, validation, and testing sets with a ratio of 70%, 20%, and 10%, respectively, for both factual and counterfactual samples. This study divides sub-networks from the complex Shanghai URT system. These sub-networks are selected based on different passenger flow conditions: high passenger flow, medium passenger flow, and low passenger flow.