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

Table 3: Prediction accuracy of counterfactual OD passenger flow in different cases and time scales. ### Incident's Impact on OD Flows Analysis

The effectiveness of the DC-DCF model is further confirmed through a detailed analysis of the impact of various incident types on short-term OD passenger flow trends. Six primary incident types are considered in this study: power equipment incidents, object intrusion, trackside equipment incidents, signal equipment incidents, train incidents, and door incidents. The proportion of each incident type and its associated average delay duration are computed and displayed in Figure 8. The analysis reveals an inverse relationship between the incident frequency and the average delay duration caused by each type. Train incidents, the most common incident type, accounts for nearly 40% of all incidents but results in an average delay of only 16 min. In contrast, power equipment incidents, which occurs in fewer than 5% of cases, causes an average delay of approximately 70 min. These findings highlight the significant heterogeneity between incident occurrence probabilities and the consequences of each incident type. The trained DC-DCF model is applied to predict the average impact of each incident type on OD flows, both for OD pairs involving the incident station as the origin and destination and for those involving neighboring stations. The results, presented in Tables 6 and 7, confirm the heterogeneous nature of the impact of different incidents on OD flows, as well as the spatial propagation of these effects. The findings show that incidents significantly reduce passenger flow for OD pairs associated with the affected station and its neighboring

Figure 8: Incident type proportion and average delay duration. Figure 8 displays the proportion of each incident type and its associated average delay duration. (**a**) Shows the proportion of six incident types (power equipment incidents, object intrusion, trackside equipment incidents, signal equipment incidents, train incidents, door incidents) in all incidents. (**b**) Presents the average delay duration (in minutes) corresponding to each of these six incident types. The analysis reveals an inverse relationship between the incident frequency and the average delay duration caused by each type. Train incidents, the most common incident type, accounts for nearly 40% of all incidents but results in an average delay of only 16 min. In contrast, power equipment incidents, which occurs in fewer than 5% of cases, causes an average delay of approximately 70 min. stations. For OD pairs where the affected station serves as the origin, the impact is almost instantaneous, as the disruption directly affects the current time period. Conversely, when the affected station serves as the destination, the impact is delayed due to the time required for the negative effects of delays to propagate across the network. The influence of incidents on neighboring stations is also significant but lower in magnitude compared to the affected station, and this effect is subject to a time-lag. The study further establishes a clear positive correlation between the duration of the negative impact on passenger flow and the time required for the incident to be resolved. For example, power equipment incidents, which takes the longest to resolve, results in the most substantial decrease in OD passenger flows, with a reduction of \(-93.729\%\). On the other hand, door incidents, which is easier to repair, leads to a smaller decrease in OD passenger flows, with a loss of only \(-14.120\%\). These results emphasize the critical role of the time required to address each incident in determining the extent of its impact on the overall