# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 3 Results
### Analyzing Extremes in \(GRQI_{k}\)  
In the pursuit of increasing transportation efficiency and safety, the analysis of \(GRQI_{k}\) focuses on understanding the extreme values and assessing how different factors influence the perceived quality and viability of routes under varying conditions. By examining scenarios that represent the upper and lower bounds of the GRQI scale, we gain valuable insights into the thresholds that define optimal and suboptimal route conditions. The \(GRQI_{k}\) calculation integrates multiple dimensions of route analysis into a single, unified metric. This metric quantifies a route's quality and highlights each contributing factor's impact. By exploring the extremes of \(GRQI_{k}\), we identify critical thresholds beyond which the route quality is significantly compromised or, conversely, where it meets the criteria for excellence. Table 1 presents the computed values of the \(GRQI_{k}\) under two distinct extreme scenarios. Each scenario demonstrates the impact of travel times, delays, and road conditions on the overall assessment of route quality. The first scenario represents a highly undesirable route. Poor safety, extremely hazardous road conditions, and a significant delay over a relatively long distance lead to a high \(GRQI_{k}\) of 100. This result aligns with the interpretation that a higher \(GRQI_{k}\) indicates a route that should be avoided due to its combination of dangers and inefficiencies. In contrast, the second scenario reflects an ideal route. Excellent safety, road conditions, and no delays result in a low \(GRQI_{k}\) of 0. This score indicates a highly recommended route that offers efficient travel with minimal risks or hazards. These scenarios illustrate the \(GRQI_{k}\) formula's ability to quantify a route's desirability. A higher \(GRQI_{k}\) highlights routes with significant issues, whereas a lower \(GRQI_{k}\) emphasizes safe, efficient, and well-maintained options. This system supports route planning and optimization decision-making by ranking routes based on quality and risk. Figure 5a-d illustrate the evolution of the \(GRQI_{k}\) as a function of four key parameters: \(ET_{k}\), \(RSa_{k}\), \(RSe_{k}\), and \(TDe_{k}\), with all other parameters held constant. Each graph demonstrates a distinct relationship between \(GRQI_{k}\) and the respective parameters, highlighting the impact of individual factors on route quality. \(TDi_{k}\) is not analyzed as it is used for parameter normalization and does not directly influence road quality. For Figure 5a, the parameters were set as \(RSa_{k}=5\), \(RSe_{k}=50\), \(TDi_{k}=10\), and \(TDe_{k}=50\). This figure shows a declining trend in \(GRQI_{k}\) as \(ET_{k}\) increases. As travel time increases, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \(RSa_{k}\) & \(RSe_{k}\) & \(TDi_{k}\) & \(ET_{k}\) & \(TDe_{k}\) & \(S_{k}\) & \(N_{k}\) & \(GRQI_{k}\) \\  & & & **(Minutes)** & **(Minutes)** & **(Minutes)** & **(Minutes)** & **(Minutes)** \\ \hline
10 & 100 & 10 & 50 & 150 & \(-10\) & 0.000045 & 100 \\
0 & 0 & 10 & 50 & 0 & 5 & 0.993300 & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: Calculated \(GRQI_{k}\) for the extreme scenarios. the route quality deteriorates, reflecting the adverse impact of inefficiency and prolonged journeys on overall quality assessment. In the case of Figure 5b, the parameters were kept constant: \(RSek=50\), \(TDi_{k}=10\), \(ET_{k}=50\), and \(TDe_{k}=50\). This graph displays a linear rise in \(GRQI_{k}\) as \(RSa_{k}\) improves. This suggests that enhancing road safety (e.g., reducing accidents or implementing better safety protocols) influences route quality positively, emphasizing safety's importance in determining route desirability. For Figure 5c, the parameters were set as \(RSa_{k}=5\), \(TDi_{k}=10\), \(ET_{k}=50\), and \(TDe_{k}=50\). This figure underscores a linear increase in \(GRQI_{k}\) with \(RSe_{k}\).