## 2 Literature Review  
### Data-Driven Transportation Simulation  
Transportation simulation has been a long-standing tool for system analysis, which uses computer digital models/methods to reflect complex traffic phenomena. According to the resolution level of the system, transportation simulation models can be classified into three types: macroscopic, mesoscopic, and microscopic simulation. The comparison of them is summarized in Table 1.  
Macroscopic models consider traffic flow as a continuous stream and plan the regional or overall city traffic layout. These models primarily focus on general traffic flow trends and macroscopic characteristics, e.g., traffic volume, velocity, and density. The study utilized TransCAD to estimate evacuation and sheltering demands [17]. The study used VISUM to implement urban public transport allocation [26]. Macroscopic simulation requires a few network data (e.g., basic road topology, fundamental road segment attributes, road segment capacity, and free-flow speed. ), and uses survey data as the input for OD demand. The study incorporated road network information (nodes, zones, and links within Dublin city) from VISUM and the Irish National Transport Model into its macroscopic simulation. Travel demand data encompassed OD matrix data in the form of both cars and heavy goods vehicles [27]. Based on the macroscopic traffic network, mesoscopic models position individual vehicles within the macroscopic flow to estimate traffic conditions. It emphasizes traffic flow and vehicle interactions over a broader area, and reveals metrics such as traffic volume distribution, vehicle travel times, and delays. The study employed DynusT to evaluate the system performance of different link toll schemes on real highways [22]. DTAlite [28] and MATSim [20] are widely used in multi-modal systems for their flexibility and ease of customization offered using open-source software. Compared to macroscopic simulation models, mesoscopic simulation requires higher data resolution, such as detailed road topology and lane configuration in road network data. The study utilized road network and demand data provided by the government officially. The researchers highlighted the potential use of more detailed preference data for developing and calibrating more sophisticated travel demand models under evacuation scenarios. Microscopic models focus on the interaction between each vehicle/traveler. The study rigorously evaluated various signal phase settings using the developed system interfaced with CORSIM microsimulation [29]. The study estimated the vehicle states based on connected vehicle data and simulated a real intersection in VISSIM for testing [30]. \ \begin{table}
\\begin{tabular}{c c c c c} \\hline \\hline
**Simulation** & **Common** & **Granularity** & **Object** & **Ease of** \\\\
**Model** & **Software** & **Granularity** & **Description** & **Data Fusion** \\\\ \\hline Macroscopic & TransCAD [17; 18], & lower & traffic flow & lower \\\\ simulation & VISUM [19] & & & \\\\ Mascoscopic & MATSim [20], & & & \\\\ simulation & DTALite [21], & medium & specific & medium \\\\  & DynusT [22; 23] & & & \\\\ Microscopic & VISSIM [24], & & & \\\\ simulation & SUMO [25] & higher & each vehicle & higher \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 1: Comparison of macro-, meso-, and micro-simulation models. Transmodeler was applied to match vehicles operating characteristics for the purpose of emissions reduction and safety [31; 32]. Microscopic simulation has the most rigorous demand for network data, requiring complete road geometries and traffic signal timing data. It also requires detailed time-dependent traffic demand data, such as route choice, trip chaining, and vehicle attribute data. Recently, research focus has gradually shifted from single simulation to hybrid simulation, i.e., macro- and micro-integrated transportation modeling and simulation [33], and meso- and micro-integrated simulation [34].