# A Vulnerability Index for Multimodal Transportation Networks: The Case of Korea
## 3 Methodology
### Data Construction  
The transportation network was constructed using the 2019 Korea Transport Database (KTDB), including nodes with geographic coordinates and line data indicating connections and lengths. Network pre-processing and OD matrix integration were performed in EAME4. Centroid connectors were assigned a uniform length of 0.01 km, and intermodal interactions (e.g., transfers between rail and road) were not explicitly represented in the network graph. In other words, the network reflects a topological structure based on physical connectivity but does not account for modal transfer behaviors or temporal dynamics that may exist in real-world multimodal travel. These assumptions influence the interpretation of structural connectivity in multimodal contexts. Figure 1: Summary of methodology. We utilized network and OD (Origin-Destination) data provided by the Korea Transport Database (KTDB). The OD data are provided in matrix format as TXT files, and the network data are structured as IN files, containing node and edge information. The data were processed in EMME4 to complete the assignment stage, constructing a comprehensive network. The constructed network was then exported as a CSV file and rebuilt in a Python environment to facilitate efficient network graph generation and vulnerability analysis. Each node and link in the network were equipped with attributes, such as coordinates, and administrative regions derived from the traffic data. Node data included node IDs assigned within the transportation network and their respective X and Y coordinates (see Table 1). Additionally, a Data3 custom attribute was added to represent a 5-digit administrative region code (city, district, or municipality). To ensure spatial accuracy, the coordinate data were transformed into the EPSG:4326 (WGS84) coordinate reference system. The converted coordinates, expressed in latitude and longitude, served as the basis for constructing the network graph. Thus, each node was precisely positioned within the graph, reflecting real-world geographic and administrative properties. The link data are composed of the starting and ending node IDs, representing each link's origin and destination (see Table 2). The attribute Length specifies the distance associated with each link. For centroid connectors, the extension is standardized to 0.01 km, irrespective of their physical length. For all other links, the length is derived based on the actual physical distance between nodes, ensuring accurate representation of real-world conditions. This distinction allows for precise modeling of both functional and spatial aspects of the transportation network. The weights required for network construction were derived from the total traffic volume (TotVol) collected from the traffic data as OD traffic volume. Table 3 starts with the Node ID, indicating the origin and destination, and shows the TotVol as the total traffic volume. The data include details such as the IDs of the origin and destination nodes, transportation modes (e.g., car, pedestrian), and road classifications. Modes represent the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **X-Coordinate** & **Y-Coordinate** & **Data3** \\ \hline
1 & 309905 & 554098 & 11010 \\
2 & 312213 & 551430 & 11020 \\
3 & 310307 & 548722 & 11030 \\
4 & 315001 & 550794 & 11040 \\
5 & 318852 & 549698 & 11050 \\
6 & 316844 & 553737 & 11060 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of node data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**From** & **To** & **Length** \\ \hline
1 & 221688 & 0.01 \\
1 & 227659 & 0.01 \\
1 & 340675 & 0.01 \\
2 & 221698 & 0.01 \\
2 & 340765 & 0.01 \\
2 & 340795 & 0.01 \\
100003 & 100133 & 9.35 \\
100004 & 100121 & 11.25 \\
141178 & 142332 & 0.29 \\
252543 & 252549 & 4.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of link data. types of transportation used in the network, including road-based modes such as cars and pedestrians (cp), as well as rail-based modes (r).