# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 2 Literature Review
The study showcased how federated learning can enhance adaptive traffic management, making co-simulation a valuable tool for future intelligent transportation systems. In the energy optimization domain, [PERSON] et al. investigated route and speed co-optimization in connected vehicle networks, leveraging Vissim-based co-simulation to analyze fuel efficiency under different traffic conditions [20]. Their research demonstrated the role of real-time data integration in improving the accuracy of energy consumption models, reinforcing the necessity of robust co-simulation frameworks for optimizing connected and automated vehicle operations. Despite these advances, significant challenges remain in co-simulation research. Data synchronization between multiple simulation tools continues to be a major issue. Many platforms focus only on vehicle-level interactions, failing to capture multimodal dynamics such as pedestrian gap acceptance or cyclist responses to passing vehicles. Physiological responses, including stress and cognitive load, are often overlooked, despite their relevance in evaluating road user behavior. Additionally, most co-simulation platforms do not fully integrate human decision-making, relying on pre-programmed agents rather than real-time user interactions. To address these gaps, recent research has moved toward multi-participant co-simulation environments, where multiple human users interact within a shared virtual space. Simulator research has increasingly focused on developing multi-participant simulator setups, where multiple human users can simultaneously interact in a virtual traffic environment. [PERSON] et al. (2015) investigated driver-pedestrian interactions using a multiparty simulator, where a human driver in a driving simulator interacted with a real pedestrian in a pedestrian simulator ([PERSON] et al., 2015). Their study demonstrated that drivers exhibited different yielding behaviors when encountering a real human pedestrian compared to a pre-programmed agent, emphasizing the importance of real-time human interactions in multimodal simulation systems. [PERSON]. ([PERSON] et al., 2015) expanded on these concepts by developing a networked 3D virtual environment that supports multi-user driving simulations. Their study demonstrated that human-driven behaviors significantly influence congestion propagation, reinforcing the importance of human-in-the-loop interactions in urban traffic modeling ([PERSON] et al., 2015). [PERSON] and [PERSON] (2003) developed a linked simulation environment, where two drivers in separate simulators interacted within the same virtual world ([PERSON] et al., 2015). Their study examined collision avoidance behavior, finding that mutual viewing times between drivers significantly influenced reaction strategies in high-risk scenarios. [PERSON] and [PERSON] (2015) explored the benefits and challenges of multi-driver simulator studies, particularly in evaluating cooperative intelligent transportation systems (ITS) ([PERSON] and [PERSON], 2015). Their work highlighted key issues such as data synchronization, real-time communication between drivers, and the scalability of simulation platforms, which remain major challenges in large-scale co-simulation environments. [PERSON] et al. (2011) investigated multi-driver simulation for studying ADAS effects on individual and group behavior ([PERSON] et al., 2015). Their study demonstrated that cooperative ADAS technologies, such as merging assistants and hazard warnings, influenced not only individual drivers but also the overall traffic network. Their work reinforced the necessity of multi-user simulators to analyze system-wide behavioral impacts. Another critical limitation of many existing simulator-based studies is their reliance on pre-programmed agents that lack reactive, adaptive behavior. While this approach enables controlled experiments, it reduces ecological validity by failing to capture the dynamic interplay between road users. For example, [PERSON] et al.