## 2 Literature Review
### Data-Driven Transportation Simulation
Additionally, with the rapid development of intelligent transportation systems (ITS), the volume of traffic data has surged significantly, driving transportation simulation towards a more data-driven direction with better data utilization, making the application of data in traffic simulation more extensive and profound. Regarding road networks, OpenStreetMap ([https://www.openstreetmap.org/](https://www.openstreetmap.org/), accessed on 12 October 2022) (OSM) is a frequently used road network data source. As for travel demand, fast-growing communication networks and positioning systems have promoted the emergence of LBS data. Data development improves the accuracy and diversity of transportation simulation. This, in turn, enables exploring a more comprehensive range of application scenarios and possibilities, providing enhanced support and guidance for transportation planning and management. Table 2 presents a summary of some previous simulation models' data sources and simulation scales. Conventionally, traditional transportation simulation modeling mainly relies on non-LBS data (data not based on Location-Based Services), which has several disadvantages: First, its limited resolution undermines the precision required for accurate simulations. Second, the inability to update in real-time hinders the timely reflection of changing traffic conditions. Third, the spatial resolution is limited, and it is difficult to delicately depict the traffic network. Additionally, spatial resolution constraints hinder the detailed depiction of the traffic network. Finally, the difficulty in capturing individual behaviors and external factors closely related to traffic metrics results in discrepancies between simulation outcomes and real-world scenarios. For accuracy, researchers are exploring the adoption of LBS data sources such as data from telecom carriers, smartphone-based apps, and Bluetooth devices. A few studies have fully used geospatially accurate Global Positioning System (GPS) data for the simulation [15; 16]. However, they only retain the start and end point information of GPS data, and discard the key waypoint data extracted from GPS data during the research process. Both methods fail to fully utilize the precise information provided using LBS (such as the individual's actual route choices), which not only affects the accuracy of the simulation, but also increases the complexity and difficulty of subsequent model calibration. \ \begin{table}
\\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \\hline \\hline
**Researchers** & **Simulation Demand Data** & **Simulation Network Data** & **Main Work** \\\\ \\hline [PERSON] et al. [10] & Real and virtual vehicle & Network of the local University’s campus & Present a simulation platform to examine real-world driver responses to feedback control. \\\\ [PERSON] et al. [16] & GPS (LBS) & Network of Shanghai & Highlight the potential of MATSim in simulating large-scale dynamic transport scenarios. \\\\ [PERSON] and [PERSON] [35] & Census data set & Network of Paris and its surroundings & Introduce the process for generating a synthetic travel demand based on open data/software. \\\\ [PERSON] et al. [15] & Phone GPS (LBS) & Network of the entire North America & Illustrate the value of data-driven simulations in estimating and predicting travel demand. Upgrade the SiMTraM model to better suit Indian traffic scenarios. \\\\ [PERSON] et al. [36] & Virtual vehicle & Two roads (34 km and 8 km) in the city & Conduct evacuation time estimates and obtain petrochemical enterprise & Conduct evacuation time estimates and obtain reliable evacuation simulation results. \\\\ [PERSON] et al. [37] & Survey data & Network of the town with a petrochemical enterprise & Conduct evacuation time estimates and obtain reliable evacuation simulation results. \\\\ \\hline \\hline \\end{tabular}
\\end{table} 
Table 2: Data-driven transportation simulation.