# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 2 Literature Review  
The integration of driving simulators with traffic simulation tools has gained increasing attention in transportation research as a means to improve the realism of simulation-based studies. Traditional driving simulators have often lacked realistic background traffic behavior, while microscopic traffic simulators provide detailed network-wide traffic operations but lack human interactions. To bridge this gap, researchers have explored co-simulation frameworks that integrate driving simulators with traffic modeling tools, allowing for the simultaneous study of human behavior and network-wide traffic interactions. This section discusses key advancements in co-simulation research, focusing on the methodologies used and the challenges that remain in creating scalable, multimodal simulation environments. Figure 1: Research framework. One of the earliest applications of co-simulation in traffic engineering did not involve driving simulators but instead focused on integrating traffic models with intelligent transportation systems (ITS). [PERSON] and [PERSON] (2003) developed a virtual-reality-based traffic-driving simulation system to examine driver responses to real-time ITS information [15]. Their work highlighted the advantages of simulation-based evaluations over traditional survey-based methods, demonstrating how an integrated virtual environment can provide a more accurate assessment of traveler decision-making. This research emphasized the importance of real-time interaction with dynamic traffic conditions, setting the stage for later developments in co-simulation frameworks. One of the first co-simulation frameworks specifically designed for driving simulators was proposed by [PERSON] and [PERSON] (2011) [10]. They integrated SCANeR with AIMSUN, emphasizing the importance of synchronization and consistency between individual driver behavior models and macroscopic traffic flow dynamics. Their work laid the foundation for effective co-simulation operations, ensuring that human-driven vehicle behaviors align with network-wide traffic flow, which remains a fundamental challenge in co-simulation research today. [PERSON] and [PERSON] (2011) extended these approaches by developing a co-simulation framework for multimodal transportation, incorporating SCANeR and AIMSUN [16]. Their study was one of the first to ensure that autonomous vehicles interacted with pedestrians and cyclists in a realistic manner. They highlighted the limitations of pre-programmed agents, showing that real-time decision-making by human users is necessary to improve the ecological validity of simulations. Their framework also demonstrated the need for adaptive traffic management strategies to accommodate multimodal interactions effectively. More recently, [PERSON] and [PERSON] (2023) explored driving simulator integration with microscopic traffic simulators, emphasizing perceptual consistency in urban driving [17]. Their study validated that mixed-reality simulation enhances ecological validity, particularly when introducing connected and automated vehicles (CAVs) into the experiment. Their findings reinforced the importance of reducing synchronization delays to improve user experience and simulation accuracy. A co-simulation digital twin framework using SUMO and AirSim was introduced to extend co-simulation techniques beyond traditional vehicle networks [18]. This approach allowed for the integration of unmanned aerial vehicles (UAV) into urban traffic management, highlighting the interoperability challenges between autonomous drones and vehicle traffic. The study underscored the need for high-fidelity traffic simulation models that can support emerging urban air mobility systems. The FedCars project further advanced co-simulation by introducing an efficient scheduling framework for federated learning in connected vehicle networks [19]. Their integration with CARLA-based co-simulation demonstrated machine learning-driven traffic control, ensuring real-time synchronization between vehicles and infrastructure.