## 6 Accident Case Development and Implementation for the Accident Response--A Case Study
### Inference of Accident Response Rules in RBR  
CBR has some advantages, such as incremental learning, easy acquisition, and easy maintenance. However, it may fail when no relevant case is available, especially in metro accidents in which the complete records are rare. Thus, to overcome shortages of the CBR method due to the lack of relevant cases, rule-based reasoning (RBR) is added as the complementary method in the decision-making process in this paper. The integration of CBR and RBR is useful in the decision-making process [51]. Cases reveal the knowledge or experiences obtained from specific situations and rules represent the general knowledge of a specific domain from documents, such as regulations and emergency plans. In a realistic accident response process, managers need to identify the accident level first based on fault characteristics of the accident, and then take appropriate measures under the guidance of various documents. Figure 11 shows an example of an accident response process in the case of large passenger flow. Thus, in this study, RBR was used to infer the accident level and response measures. Semantic Web Rule Language (SWRL) was used to represent rules for reasoning the response measures. An SWRL rule contains an antecedent part and a consequent part, both of which were written in Web Ontology Language (OWL) classes, properties, individuals, and data values. As an example, the rules of a large passenger flow accident are presented as follows. Table 6 shows the response rules under \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Testing Query**} & \multicolumn{2}{c}{**Number of Retrievals**} & \multicolumn{2}{c}{**Performance**} \\ \cline{3-6}  & & **TP** & **FP** & **Precision** &
\begin{tabular}{c} **Feasibility of Highest** \\ **Similarity Case** \\ \end{tabular} \\ \hline
1 & Cutout & 6 & 0 & 100\% & Yes \\
2 & Single VOBC failure & 10 & 1 & 91\% & Yes \\
3 & STC equipment failure & 8 & 1 & 89\% & Yes \\
4 & TMS failure & 9 & 0 & 100\% & Yes \\
5 & SMC equipment failure & 10 & 1 & 91\% & Yes \\
6 & ATS failure & 8 & 1 & 89\% & Yes \\
7 & Metro door failure & 9 & 1 & 90\% & Yes \\
8 & Loss of indication of a turnout & 7 & 0 & 100\% & Yes \\
9 & Remote control interface unit failure & 5 & 1 & 83\% & Yes \\
10 & Emergency braking cannot be alleviated & 6 & 2 & 75\% & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Expert review results. the circumstance of level 3 passenger flow. As shown in Table 6, different staff in a specified position should take different measures to minimize the impact of accidents. Protege was used to verify the feasibility of RBR. In Protege, by iterating the SWRL rules listed in Table 6, the response measures to be taken were inferred. As seen in Figure 12, once the passenger flow level increased to level 3, setting the passenger flow divider to guide the passenger flow should be taken by the station attendant. Therefore, through encoding the SWRL rules, RBR can be used to support decision-making by deciding the response measures to the metro accident, which are recorded in documents like emergency plans. Figure 11: The handling process of a large passenger flow accident. Figure 12: The decision-making result as shown in the interface of Protege.