# Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles
## 7 Applications of AV Traffic Modeling
### Infrastructure Design and Planning  
The deployment of autonomous vehicles (AVs) requires significant advancements in infrastructure design and planning, as demonstrated by various interconnected research studies that collectively contribute to the theoretical framework of this evolving field. [PERSON] et al. proposed a mathematical framework for modeling and deploying multi-lane AV zones in mixed-user traffic networks, comparing designs where lanes are either fully dedicated to AVs or shared with human-driven vehicles (HVs). Their findings highlight improvements in the total system travel time (TSIT) when AV lanes are strategically integrated, providing a foundational basis for optimizing lane utilization in mixed-traffic scenarios [266].  
Building on this, [PERSON] et al. emphasized the importance of AV-dedicated lanes, particularly in scenarios with varying market penetration rates and infrastructure construction behaviors. Their research suggests that AV lanes are most beneficial to the network when AV penetration is below 55%, reinforcing the necessity of adaptive infrastructure strategies to accommodate different levels of AV adoption [267]. This aligns with broader infrastructure planning needs identified by other studies, which underscores the role of dedicated AV zones in facilitating vehicle platoons and enhancing the overall network performance [268].  
[PERSON] et al. extended the discussion to the integration of electric vehicle (EV) infrastructure, addressing the impact of EV charging stations on power grids. Their use of socio-economic data to predict concentrations of electric mobility and optimize grid planning highlights the interdependence between AV and EV infrastructure, further emphasizing the need for holistic planning approaches [269]. [PERSON] et al. added to this framework by advocating for a strategic multiyear infrastructure plan to accommodate Connected Autonomous Vehicles (CAVs), which prioritizes infrastructure investments to maximize long-term benefits and ensure scalability [270].  
The adaptability of traffic and transport models is another critical aspect highlighted in this section. [PERSON]'s work calls for open-source approaches in infrastructure modeling to prevent knowledge loss and ensure that models can evolve with new applications such as in-car systems and intelligent transportation systems (ITSs) [271]. This adaptability is crucial for sustaining innovation and maintaining relevance in a rapidly changing technological landscape.  
[PERSON] et al. contributed to the theoretical foundation by introducing multi-resolution simulation frameworks for cooperative vehicle-infrastructure systems, focusing on the intricacies of data transmission and interaction. Their work emphasizes the importance of detailed simulation models in understanding and optimizing the dynamic interactions between AVs and infrastructures [272]. [PERSON] et al. further explored these dynamics through an analytical model based on Stochastic Petri Net theory, which evaluates VANET (Vehicular Ad Hoc Network) infrastructures, considering mobility and network parameters [273]. This approach provides a methodological tool for assessing the complex interactions within AV networks.  
These studies collectively underscore the importance of comprehensive infrastructure planning and modeling to support the integration and optimization of AV technologies in modern transportation networks.