## 3 Methods
### Dual-Channel Deep Counterfactual Prediction Model for OD Matrix Prediction
#### 3.3.3 Loss Function and Prediction Accuracy Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Incident Type** & \begin{tabular}{c} **Post-Incident** \\ **Period** \\ \end{tabular} & \
\begin{tabular}{c} **Incident-Station** \\ **Origin OD Pairs** \\ \end{tabular} &
\begin{tabular}{c} **Incident-Station** \\ **Destination OD Pairs** \\ \end{tabular} \\ \hline Power & \(30\) min & \(-67.392\%\) & \(-42.685\%\) \\ equipment incidents & \(60\) min & \(-75.579\%\) & \(-59.530\%\) \\ \hline Object intrusion & \(30\) min & \(-62.040\%\) & \(-34.822\%\) \\  & \(60\) min & \(-60.483\%\) & \(-51.499\%\) \\ \hline Trackside & \(30\) min & \(-48.330\%\) & \(-29.020\%\) \\ equipment incidents & \(60\) min & \(-48.804\%\) & \(-39.874\%\) \\ \hline Signal & \(30\) min & \(-32.218\%\) & \(-19.929\%\) \\ equipment incidents & \(60\) min & \(-34.935\%\) & \(-29.420\%\) \\ \hline Train incidents & \(30\) min & \(-17.654\%\) & \(-9.399\%\) \\  & \(60\) min & \(-17.154\%\) & \(-13.676\%\) \\ \hline Door incidents & \(30\) min & \(-10.340\%\) & \(-6.016\%\) \\  & \(60\) min & \(-11.638\%\) & \(-8.461\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Short-term passenger flow impacts of different incident types on OD passenger flow of incident stations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Incident Type** & \begin{tabular}{c} **Post-Incident** \\ **Period** \\ \end{tabular} & \
\begin{tabular}{c} **OD Pairs of Adjacent** \\ **Stations as** \\ **the Origin** \\ \end{tabular} &
\begin{tabular}{c} **OD Pairs of Adjacent** \\ **Stations as** \\ **the Destination** \\ \end{tabular} \\ \hline Power & \(30\) min & \(-22.934\%\) & \(-12.888\%\) \\ equipment incidents & \(60\) min & \(-26.623\%\) & \(-18.349\%\) \\ \hline Object intrusion & \(30\) min & \(-17.539\%\) & \(-11.355\%\) \\  & \(60\) min & \(-19.622\%\) & \(-15.373\%\) \\ \hline Trackside & \(30\) min & \(-12.836\%\) & \(-9.096\%\) \\ equipment incidents & \(60\) min & \(-16.433\%\) & \(-10.580\%\) \\ \hline Signal & \(30\) min & \(-10.308\%\) & \(-4.853\%\) \\ equipment incidents & \(60\) min & \(-11.004\%\) & \(-8.022\%\) \\ \hline Train incidents & \(30\) min & \(-5.128\%\) & \(-2.721\%\) \\  & \(60\) min & \(-4.750\%\) & \(-5.682\%\) \\ \hline Door incidents & \(30\) min & \(-2.889\%\) & \(-1.695\%\) \\  & \(60\) min & \(-3.363\%\) & \(-2.462\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Short-term passenger flow impacts of different incident types on the OD passenger flow of neighboring stations. network performance. The longer the resolution time, the more severe the disruption to passenger flow, highlighting the need for efficient and timely incident management. ## 5 Discussion

Given these findings, several policy implications emerge. It is crucial for subway systems to develop a tiered emergency response system for different incident types. Incidents such as power equipment incidents and object intrusion, which cause significant delays, should be classified as first-level incidents, while trackside equipment incidents and signal equipment incidents should be second-level, and train incidents and door incidents should be third-level incidents. These tiers would enable the implementation of tailored emergency measures, such as directing passengers to waiting areas for repair, implementing flow diversion to prevent overcrowding, or initiating bus connections. Additionally, modeling the spatiotemporal changes in passenger flow at neighboring stations can help identify stations likely to be impacted by the incident, allowing for early warnings and preemptive safety measures to prevent the escalation of the disruption's effects. Transit agencies worldwide are beginning to explore AI-driven decision support tools, and a model like this could be integrated into their operations control centers for live scenario evaluation. For instance, one recent framework combining deep learning and incident information demonstrated substantially improved detection of ridership drops during disruptions [9].