# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 6 Simulation Platforms for AV Traffic
### MATSim (Multi-Agent Transport Simulation)  
MATSim (Multi-Agent Transport Simulation Toolkit) is a robust and adaptable open-source platform designed for large-scale agent-based transportation planning and simulation. Its versatility allows for it to be applied across various domains, including road transport, public transport, freight transport, and regional evacuation scenarios [231]. The platform's ability to simulate diverse transportation systems is further enhanced by the BEAM (Behavior, Energy, Autonomy, and Mobility) framework, which extends MATSim's capabilities. BEAM integrates'mode choice' behavior through a multinomial logit model, offering a range of eight different transportation modes, such as biking, driving, walking, and ride hailing [232]. This extension underscores MATSim's adaptability in modeling complex urban transportation systems, where mode choice is a critical factor in understanding travel behavior and system efficiency. The complexity of calibrating such simulations, particularly when accounting for diverse mode choices, has been addressed through the implementation of a parallel Bayesian optimization method. This method significantly improves both the accuracy and efficiency of simulations by optimizing parameters in a computationally effective manner [232]. The successful application of this optimization approach highlights MATSim's potential in handling large-scale, data-intensive simulations that require precise calibrations. MATSim's modular structure is another key strength, allowing it to leverage high-performance computing resources for extensive simulations. For instance, the use of the GRID'5000 infrastructure demonstrates MATSim's capability to manage simulations that demand substantial computational power, enabling detailed and expansive transportation studies [233]. This capability is particularly relevant for simulations that encompass large urban areas or complex transportation networks. Beyond traditional transport applications, MATSim has also been adapted to explore the impact of road characteristics on cyclists' behaviors. Studies have shown that factors like road gradient and surface type can significantly affect travel time and distance, especially for longer trips [234]. This adaptation underscores MATSim's flexibility in addressing specific transportation issues, such as those related to non-motorized travel modes. MATSim's framework also supports the integration of emerging transport technologies, such as shared automated vehicles. The platform's iterative design and simulation workflows facilitate the evaluation of new transport technologies and their impacts on urban mobility. For example, [PERSON] et al. [235] explore the intersection of urban design and transport simulation using Sketch MATSim, emphasizing how iterative workflows can optimize the network design and enhance the performance of shared automated vehicles. Moreover, MATSim's adaptability extends to the incorporation of optimal control solutions for agents with specific shape and density constraints. The introduction of the MASCOT (Multi-Agent Shape Control with Optimal Transport) method demonstrates how MATSim can be used to minimize collisions and optimize source-destination assignments in complex transportation scenarios [236]. This application of optimal control techniques within the MATSim framework illustrates its potential for managing sophisticated transport systems that require precise spatial and temporal coordination. The platform's scalability and flexibility are further exemplified by its application in real-world contexts. [PERSON] et al. [237] presented MATSim OpenBerlin, a simulation framework that integrates real-world data for agent-based transport simulations in Berlin. This framework demonstrates MATSim's ability to simulate a wide range of transport scenarios and evaluate the potential impacts of different policy interventions, providing valuable insights for urban planners and policymakers.