## 3 Materials and Methods
### Model Initialization
#### 3.3.2 Data Processing  
MATSim requires an initial travel demand plan, which includes detailed fixed-place activities (e.g., home, work, shopping), their locations, start times, and durations for each agent (traveler). These plans are loaded into the travel network for route planning and travel mode assignment, while travel paths and modes can also be predefined. It allows for better integration with LBS data and supports various multi-modal travel options. We perform the following data processing steps to convert the original data to MATSim demand and supply files. (a) Adaptation of Supply Data to Simulation Modeling. The road network is stored in the format of XML (Extensible Markup Language), consisting of two core data sets: intersection nodes and roads, which correspond to vertices and edges in graph theory, respectively. The road network data in Section 3.2 and their corresponding relationships with the required network format of MATSim are shown in Table 3, in which roads and key road nodes are abbreviated as \"links\" and \"nodes\" in the simulation. The information in the road segment data can be directly mapped to similar attribute labels in MATSim. Since the original data lack \"link capacity\" information, we derive the capacity of links according to their levels and the number of lanes (see Table 4 [43]). The link capacity (\\(L_{cap}\\)) is the multiplication between level-based lane capacity (\\(L_{lev-cap}\\)) and the number of lanes (\\(L_{lanes}\\)), shown as follows:  
\\[L_{cap}=L_{lev-cap}*L_{lanes} \\tag{1}\\]  
The conversion from raw data format to the MATSim road network format is accomplished by utilizing the josm-matsim-plugin ([https://github.com/matsim-org/josm-matsim-plugin](https://github.com/matsim-org/josm-matsim-plugin), v.d70 ae5a) in Python (v.3.8). Moreover, since the original road network data contain a large amount of non-road information and small roads with low traffic demand, it burdens our simulation in the route planning and LBS completion process. As a result, we conduct a filtering process to extract the road network information required for the simulation, excluding links with speed limits below 20 km/h, typically associated with residential area roads. After the screening process was completed, we once again conducted a thorough check and confirmation of the connectivity of the road network. In terms of integrating public transportation supply, we utilize public transportation information data, including schedules, route planning, station waiting times, and stop locations. Public transportation vehicle information is obtained through configuring different vehicle types, seat capacities, and standing passenger capacities for various public transportation routes. By consolidating and summarizing the public transportation data along with the basic road network data, we ultimately generate a multi-modal simulation road network. The final road network encompassed both highway and railway transportation. As shown in Figure 2, the multi-modal simulation road network included 999 nodes, 1990 links (white lines), 30 public transportation routes (lines with dark blue shading), and 5 subway lines (red dashed lines), with blue dots representing public transportation stops. During the road network generation process, we used Java OSM (JOSM, v.18746) for network visualization and manual adjustments. Additionally, we utilized the josm-matsim-plugin in Python to convert the OSM format to the MATSim road network format. \ \begin{table}
\\begin{tabular}{c c c} \\hline \\hline
**Hierarchy** & **Highway Type** & **Lane Capacity (Vehicles/Hour)** \\\\ \\hline
1 & Motorway & 2000 \\\\
2 & Trunk & 1500 \\\\
3 & Primary & 1500 \\\\
4 & Secondary & 1000 \\\\
5 & Tertiary & 600 \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 4: Functionality level of links in the transportation network. \