# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 2 Materials and Methods
### Global Route Quality Indicator Calculation
If the value exceeds 100, the parameter contribution is set to 1, as the worst road could not exist;  
\(N_{k}\)--a normalized value based on \(ET_{k}\) and \(TDe_{k}\) calculated using \(S_{k}\) from Equation (2). It employs a logistic function (sigmoid curve) to transform the \(S_{k}\) score into a normalized value between 0 and 1. The sigmoid function is often used when converting a potentially broad range of input values into a bounded output that smoothly transitions between two extremes;  
\(e^{-S_{k}}\)--suggests that higher values of \(S_{k}\) (where expected times are much less than the delay times) will result in a higher \(N_{k}\), indicating a worse performance or higher impact. Conversely, when \(S_{k}\) is negative (expected times exceed delay), \(N_{k}\) decreases, suggesting better performance or lower impact. Equation (1) combines all five parameters with nonlinear transformations and weighted aggregations. By applying logarithmic, square root, inverse tangent, quadratic, and exponential functions, the formula captures the impacts of each parameter on the overall route quality. The sigmoid function ensures the aggregation scales appropriately, while the normalization factor balances the positive and negative influences. The final multiplication by 100 scales the indicator to a user-friendly range, facilitating comparison across different routes. The \(GRQl_{k}\) formula incorporates several advanced mathematical concepts prevalent in various fields, including ML, statistical modeling, and multi-criteria decision-making. These foundational elements include:  
* The sigmoid function is inspired by logistic regression models commonly used in ML for binary classification tasks. The sigmoid function normalizes the aggregated score to a range between 0 and 1, ensuring that the final indicator remains bounded;* The weighted aggregation principle of assigning weights to various parameters reflects MCDA techniques. In MCDA, each criterion is weighted according to its relative importance, allowing for a balanced integration of multiple factors into a single evaluative metric;
* Nonlinear transformations incorporating logarithmic, square root, inverse tangent, quadratic, and exponential transformations allow the formula to capture complex relationships between the parameters and the overall route quality. These transformations are reminiscent of feature engineering processes in statistical modeling, where raw data are transformed to enhance model performance;
* The normalization factor ensures that the positive aspects are appropriately balanced against the negative influences. This approach is similar to normalization techniques used in data preprocessing to ensure that different features contribute proportionately to the outcome. While inspired by existing mathematical frameworks and techniques, the \(GRQl_{k}\) formula is uniquely composed to address the specific route optimization requirements. Its design accommodates the integration of diverse parameters, each contributing differently to the overall quality assessment. The formula's complexity allows it to capture the multifaceted nature of route quality, balancing efficiency, safety, and road conditions in a single, comprehensive metric.