# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 3 Route Guidance Models
It calculates the percentage difference in travel time between a sub-optimal route and the best available route [5; 34; 35]. A lower RGP value indicates that the recommended route is closer to the optimal route and hence a better navigation option. A value of 0 for (RGPmax) means that the algorithm will only terminate when the solution procedure will achieve the exact routing decisions in two successive iterations. A value of 0.0001 indicates that the algorithm will stop when the best-known solution is within 0.01% of the previously obtained solution. The appropriate RGPmax value depends on several factors, including the nature of the data, the accuracy of the input data, the complexity of the network, the type of analysis being performed, and the desired level of accuracy. In general, a lower RGPmax value indicates greater accuracy, but this may come at the expense of increased computational complexity and processing time. The goal relative gaps are typically adjusted to values between \(10^{-8}\) and \(10^{-14}\) in comparison studies; however, these accuracy levels are unnecessary for most applications. From the literature, it was found that a relative gap of 0.0001 is commonly used as a requirement to ensure that the traffic assignments are sufficiently converged to achieve link-flow stability [36]. A study conducted by [PERSON] et al. (2019) examined the use of a dynamic route guidance system in a congested urban area. The system used real-time traffic data and machine learning techniques to predict traffic flow and recommend alternative routes to drivers. The study found that drivers who used the system experienced a 23.5% reduction in the travel time compared to those who did not use it. This improvement was attributed to the system's ability to identify and route around congested areas in real time [37]. In addition, [PERSON] et al. (2020) focused on the use of a cooperative route guidance system on highways. The system used vehicle-to-vehicle communication to share real-time traffic information among drivers and recommend the most efficient route based on current traffic conditions. The study found that drivers who used the system experienced a 17.8% reduction in travel time compared to those who did not use it. The authors attributed this improvement to the system's ability to leverage shared information to optimize route planning [38]. There have been many studies that considered the impact of route guidance systems on travel time [39], travel cost [40; 41], driver behavior [17; 19; 42; 43; 44; 45], cell occupancy and travel demand [42], and vehicle performance [46]. Another way to classify routing systems is based on the techniques used to make routing decisions. Such classification includes static and dynamic route guidance. Both can be divided into two groups based on the fundamental method types used:  
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2. Simulation-based methods include link performance functions [47], point queue models [48], the cell transmission model [49], and the Lighthill-Whitham-Richards (LWR) model [50]. 3. Analysis-based methods include mathematical programming [51], variational inequality [41], nonlinear complementary problems [23], and differential complementary and differential variational problems [52]. Generally, route guidance with various route choice models can be broadly categorized into two categories: user optimal [41] and system optimal [17; 45], depending on the research objective. Route guidance models can be categorized into three groups based on the assignment scheme: path-based [53], link-based [54], and origin-based, which some research also refers to as intersection-based [45]. The main classification of traffic assignment followed in this paper includes the static and dynamic traffic assignment categories. The following subsections include the discussion of both categories.