# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 5 Traffic Flow Models
### Mesoscopic Models
#### 5.3.2 Agent-Based Models  
Agent-Based Models (ABMs) have become a crucial tool in traffic flow modeling, offering a sophisticated means to simulate and analyze the complex behaviors of vehicular traffic and pedestrian movements. These studies are interrelated, collectively contributing to a robust theoretical and methodological framework that advances our understanding of traffic dynamics and informs practical policy decisions.  
[PERSON]'s work underscores the importance of ABMs in simulating vehicle interactions, particularly through the use of distributed computing. This approach provides a foundational methodology for studying traffic dynamics in decentralized systems, enabling more accurate simulations of real-world scenarios [209]. Building on this, [PERSON] et al. presented a microsimulation model using Modelica language, which combines equation-based and discrete-event models to simulate vehicle dynamics, fuel consumption, and CO\({}_{2}\) emissions. Their work integrates multiple modeling techniques, demonstrating the versatility of ABMs in capturing a wide range of traffic-related factors [210].  
[PERSON] and [PERSON] further extended the application of ABMs by proposing a hybrid framework designed for real-time decision making during traffic congestion. Their approach improves traffic flow by suggesting alternative routes, highlighting the practical utility of ABMs in dynamic traffic management and congestion mitigation [211]. This real-time application was complemented by [PERSON] et al., who explored the integration of environmental impacts into land-use and transport models. By highlighting the feedback loop between noise emissions and land use, they demonstrated the capability of ABMs to address broader environmental concerns within traffic simulations [212].  
[PERSON] et al. contributed to the scalability of ABMs by developing a city-scale traffic simulation for San Francisco, addressing challenges related to data availability and computational costs. Their work is essential for applying ABMs to large urban environments, where accurate and efficient simulations are critical for urban planning and policy making [213]. In a related vein, [PERSON] et al. introduced an agent-based network transmission model (ANTM) for estimating network-wide flow dynamics and testing perimeter control policies. This model further refined the application of ABMs by focusing on network-level interactions and control strategies [214].  
[PERSON] et al. tackled the computational challenges associated with ABM traffic simulations, proposing load-balancing algorithms to enhance performance in high-performance computing environments. Their work underscores the importance of computational efficiency in the practical application of ABMs, especially in large-scale simulations [215]. Similarly, [PERSON] et al. focused on automating traffic flow management in communication networks using ABMs, illustrating how these models can be applied to optimize network operations and manage traffic flow more effectively [216].  
Finally, [PERSON] et al. provide a comprehensive overview of the current state of ABMs in urban transportation, identifying key challenges and future research directions. Their review integrates the findings of previous studies, offering a synthesized perspective on the role of ABMs in traffic modeling and highlighting areas for further development [217].  
These studies collectively demonstrate the versatility and effectiveness of ABMs in addressing various aspects of traffic flow, from individual driver behaviors and environmental impacts to large-scale urban traffic management and computational efficiency. The integration of ABMs with other modeling techniques, such as cellular automata and macroscopic fundamental diagrams, enhances their capability to simulate realistic traffic scenarios and inform policy decisions, contributing to a comprehensive theoretical and methodological framework in the field of traffic flow modeling.