# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 3 Route Guidance Models
### Multi-Class Route Guidance  
Multi-class traffic assignment (MCTA) is a comprehensive approach that surpasses the limitations of studying static and dynamic traffic assignments in isolation. Traditionally, static traffic assignment models assume fixed travel demands and fixed route choices, neglecting the dynamic nature and interactions between vehicles on the road. On the other hand, dynamic traffic assignment considers real-time variations in travel conditions but often fails to account for the diverse characteristics of vehicle types. By combining the strengths of both approaches, MCTA offers a more realistic representation of traffic scenarios. This integrated methodology addresses the complexities associated with diverse user/vehicle classes or types, considering the dynamic nature of traffic flow and accommodating different user groups, including traditional (private vehicles, public transit, pedestrians, and cyclists) and Connected Autonomous Vehicles (CAVs), providing a more effective and sustainable approach to urban mobility. From the reviewed literature, it was found that most of the studies used analytical-based and simulation-based approaches. For instance, a study employed variation inequality based on a route-based approach for creating a mixed-modes multi-class model for a transportation network of Connected Autonomous Vehicles (CAVs), in particular [75]. The model is solved using the gradient projection (GP) algorithm. Each mode needed an asymmetric interaction cost function. Results indicated that the travel time is considerably lowered as the percentage of CAVs rose. In addition, even more routes were opened to CAV driving, and computation time was found to be more effective. Nevertheless, to assess the model's effectiveness, computational tests should be run on a variety of network topologies, congestion levels, and zonal densities. In 2016, [PERSON] and [PERSON] examined how autonomous vehicles (AVs) could affect traffic behavior, specifically in shared road scenarios. The authors developed a cell transmission model (CTM) to account for variations in capacity and backward wave speed about the proportion of AVs within the network. The CTM also considers driver reaction times to predict the impact of reduced reaction times on capacity and wave speed. Regarding intersections, the authors adapted the early method for intelligent traffic management to dynamic traffic assignment models. The study found that intersection controls can be a significant bottleneck in the model and that the early method performs better than traffic signals when the proportion of AVs is high enough. The authors conclude by emphasizing the potential of AV technology and its anticipated availability in the coming years. They propose a shared road dynamic traffic assignment (DTA) model incorporating multiclass CTM predictions and intersection control modifications to account for AVs. Finally, the authors propose the multiclass framework for studying the impact of mixed intersection controls on routing in diverse traffic scenarios. In addition, [PERSON] and [PERSON] [76] conducted a study on the multi-criteria or cost-versus-time network equilibrium and system optimum problem in transportation networks. The study used an optimal control formulation to examine different values of time (VOT) for various user classes. The researchers investigated whether user-optimal flows depend on the unit (time or money) used to measure travel disutility in the presence of road pricing. They also explored the existence of uniform link tolls that can support a multi-class user equilibrium flow pattern as a system optimum when the system objective function is measured in either money or time units. The study found that, under road pricing, the same multi-class network equilibrium flow is achieved regardless of whether the generalized travel cost is measured in cost or time units. The researchers identified a uniform link toll pattern that supports a cost-based system optimum as a multi-class user equilibrium flow pattern [76].