# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 6 Proposed Communication Network Architecture for RGS  
To simulate the integrated route guidance system, two applications are to be utilized. Dynasmart [120] is used to simulate the traffic network side of the route guidance system. With its robust features for modeling and simulating dynamic routing algorithms and strategies, Dynamasmart allows for the assessment of the effectiveness and efficiency of different route guidance approaches under various conditions. Dynasmart also generates a trajectory file, which provides detailed information about vehicles' mobility patterns within the traffic network. This trajectory file is essential for communication simulations. Moreover, Dynasmart can produce an average travel time for the traffic network based on the simulated vehicle trajectories. This average travel time serves as a benchmark or a reference point in the initial stages of simulation. It also provides important information about the performance of the traffic network, which can be used to evaluate how well the communication system improves the travel time and efficiency. Secondly, Network Simulator 3 (NS3) [121] is used to simulate the communication network side. NS3 is a powerful and effective tool for simulating communication networks due to its detailed modeling of network protocols and high degree of flexibility. By integrating NS3 into MATLAB [122], researchers can create realistic communication network simulation models that accurately represent the behavior of the RGS architecture while considering factors such as packet loss, latency, and network congestion. NS3 can provide performance indicators for the simulated network, including packet delivery rate, end-to-end delay, and goodput. These indicators will provide insights into how efficient and reliable the communication network is. Finally, the end-to-end delay values obtained from NS3 can be passed to the Dynasmart benchmark scenario and calculate the new travel time (TS run2). Denote the the new travel time as \(T_{2}\) and the benchmark travel time as \(T_{1}\). Then, the difference between \(T_{2}\) and \(T_{1}\) (i.e., saving) will represent the effect of the communication network on the performance of the route guidance system. This integrated approach enables the performance evaluation as well as the reliability of the communication network within the route guidance framework. This will help in the iterative process of refining and optimizing the underlying system to enhance overall effectiveness. The integrated architecture is shown in Figure 2. To design a robust communication network for an RGS system, four distinct architectural scenarios can be considered as explained in what follows. 1. Distributed V2V Network: In this decentralized scenario, there is no central processing unit. Instead, vehicles themselves act as the main source for optimal path computation. They periodically broadcast their status- such as location, speed, heading, and nearby traffic conditions, which are received by surrounding vehicles within range. Upon receiving these data, each vehicle aggregates it with its own information to create a localized view of the traffic environment, factoring in vehicle density, speed patterns, and potential obstacles. Based on the shared data, vehicles independently compute the optimal route to their destinations. They consider real-time traffic and predictive models, constantly refining their routes based on the most current information, as depicted in Figure 3.  
Through this approach, vehicles can dynamically adjust their routes in response to changing conditions. This decentralized V2V network offers scalability, allowing it to grow without requiring additional centralized infrastructure. By leveraging shared data, vehicles can process real-time traffic information independently, enabling faster route adjustments and improving the overall RGS responsiveness.