# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 5 Discussion  
The proposed transit traffic filtration algorithm represents a significant advancement in the accurate identification of transit traffic from matched license plate data. Unlike previous methods, which often require extensive manual calibration and are prone to misclassifying valid travel times as outliers, our algorithm introduces a dynamic, interval-based analysis. This allows for a more adaptive approach to differentiating between transit and non-transit traffic, even in complex urban environments where traffic patterns can be highly variable. The key innovation of this algorithm lies in its iterative evaluation of travel times across consecutive intervals, which reduces the reliance on static assumptions about traffic behavior. By focusing on the temporal relationships between adjacent intervals, the algorithm effectively filters out travel times that deviate from the expected pattern, which typically indicates non-transit behavior. This is particularly important in urban settings where the variability in travel times is pronounced and traditional methods may struggle to maintain accuracy. The proposed algorithm has demonstrated high efficiency in all predefined objectives. Through evaluation against selected algorithms, the proposed approach has shown superior \(F_{2}\)-score metrics, consistently outperforming existing methods across a variety of test  
Figure 8: Comparison of used methods’ \(F_{2}\)-scores across different route types. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Route** & **Algorithm** & **True Positives** & **False Positives** & \begin{tabular}{c} **False** \\ **Negatives** \\ \end{tabular} & \
\begin{tabular}{c} **True** \\ **Negatives** \\ \end{tabular} &
\begin{tabular}{c} **\(F_{2}\)-Score** \\ \end{tabular} \\ \hline \multirow{4}{*}{Route A} & Proposed algorithm & 1362 & 3 & 1 & 141 & 0.982 \\  & [PERSON] & 1316 & 1 & 47 & 143 & 0.933 \\  & [PERSON]–[PERSON] v1 & 1353 & 7 & 10 & 137 & 0.947 \\  & [PERSON]–[PERSON] v2 & 1353 & 7 & 10 & 137 & 0.947 \\  & [PERSON]–[PERSON] & 1240 & 0 & 123 & 144 & 0.854 \\ \hline \multirow{4}{*}{Route B} & Proposed algorithm & 2853 & 9 & 1 & 224 & 0.968 \\  & [PERSON] & 2595 & 2 & 259 & 231 & 0.812 \\  & [PERSON]–[PERSON] v1 & 2658 & 42 & 196 & 191 & 0.724 \\  & [PERSON]–[PERSON] v2 & 2703 & 38 & 151 & 195 & 0.763 \\  & [PERSON]–[PERSON] & 2757 & 0 & 97 & 233 & 0.923 \\ \hline \multirow{4}{*}{Route C} & Proposed algorithm & 973 & 5 & 1 & 325 & 0.987 \\  & [PERSON] & 945 & 18 & 29 & 312 & 0.939 \\  & [PERSON]–[PERSON] v1 & 954 & 74 & 20 & 256 & 0.802 \\  & [PERSON]–[PERSON] v2 & 955 & 113 & 19 & 217 & 0.697 \\  & [PERSON]–[PERSON] & 809 & 2 & 165 & 328 & 0.905 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices along with accuracy metrics of evaluated algorithms. scenarios, including urban arterial routes, suburban commutes, and urban transit routes. The high \(F_{2}\)-scores achieved across these scenarios highlight the robustness of the algorithm in correctly classifying transit traffic, minimizing both false positives and false negatives. This accuracy is crucial for traffic management applications, where the precise identification of transit traffic can inform better planning and decision making. However, certain limitations of the current algorithm must be acknowledged. The algorithm's reliance on time interval-based analysis may also limit its scalability across different geographic regions or traffic scenarios. Different regions may exhibit unique traffic patterns that are not easily captured by a uniform algorithm, suggesting the need for region-specific calibrations or adjustments. Although the proposed algorithm reduces the need for manual calibration compared to existing methods, it may still require some level of customization to ensure optimal performance in diverse settings. Another notable limitation is the number of travel time records within each evaluated time interval.