# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 4 Results  
In this section, the classification results of the proposed algorithm are provided. To further highlight the added value of the proposed method, a comparison was made with the most common methods currently applied in the field. The outlier detection algorithms discussed in Section 2 were applied to the travel time data sets for routes A, B, and C. Specifically, the algorithms evaluated include the Jang algorithm, the Dion-Rakha algorithm versions 1 and 2, and the Robinson-Polak algorithm. The classification results of these analyses are shown in Figures 3-7. To provide a complete assessment, the confusion matrices along with the \(F_{2}\)-score metric are presented in Table 3.  
Figure 3 illustrates the classification results obtained using the Jang algorithm. On route A (Figure 3a), the algorithm demonstrated an \(F_{2}\)-score of 0.933, with 47 false negatives (FNs) detected and only 1 false positive (FP) result. However, on route B (Figure 3b), the algorithm exhibited an \(F_{2}\)-score of 0.812. A notable number of 259 FNs suggests difficulties in responding to sudden changes in travel time behavior. Similarly, when applied to route C (Figure 3c), the algorithm achieved an \(F_{2}\)-score of 0.939. Despite this, it showed a relatively balanced distribution of falsely classified observations, with 18 FPs and 29 FNs. Positively, the algorithm demonstrated the ability to react to gradually increasing travel times. Figure 4 shows the classification results obtained using the Dion-Rakha version 1 algorithm. On route A (Figure 3(a)), the algorithm performed with a commendable \(F_{2}\)-score of 0.947, with a result of 7 detected FNs and 10 FPs. On route B (Figure 3(b)), the algorithm exhibited an \(F_{2}\)-score of 0.724, with 42 FPs and 196 FNs, also suggesting difficulties in responding to sudden changes in travel time behavior. When applied to route C (Figure 3(c)), the algorithm achieved an \(F_{2}\)-score of 0.802, with 74 FPs and 20 FNs. Despite its ability to capture gradually increasing travel times, the algorithm had difficulties establishing a valid travel time range in time intervals following abrupt changes, leading to a higher number of FNs. Figure 3: Results of Jang algorithm. Figure 5 presents the classification results obtained using the Dion-Rakha version 2 algorithm. In particular, for route A (Figure 5a), the algorithm produced results identical to those of the Dion-Rakha version 1 algorithm. On route B (Figure 5b), the algorithm achieved an \(F_{2}\)-score of 0.763, with 38 FPs and 151 FNs. Although this algorithm showed fewer difficulties in responding to abrupt changes in travel times compared to version 1, it encountered similar challenges in establishing valid travel time ranges as the Dion-Rakha version 1 algorithm. Applying the algorithm to route C (Figure 5c) resulted in an \(F_{2}\)-score of 0.697, the lowest of all algorithms compared on this route, with 113 FPs and 19 FNs. This algorithm exhibited a notably higher number of FPs, indicating challenges in consistently establishing valid travel time ranges throughout the data set. Figure 4: Results of Dion–Rakha v1 algorithm. Figure 6 shows the classification results achieved using the Robinson-Polak algorithm. For route A (Figure 5(a)), the algorithm yielded an \(F_{2}\)-score of 0.854, with a result of 0 detected FNs and 123 FPs. This represents the lowest \(F_{2}\)-score among all algorithms tested on route A, indicating challenges, particularly during morning periods, when travel times gradually decrease. However, on route B (Figure 5(b)), the algorithm exhibited a notably high \(F_{2}\)-score of 0.923, with 0 FPs and 97 FNs, suggesting minimal difficulties in adapting to abrupt changes in travel time behavior for this route configuration. When applied to route C (Figure 5(c)), the algorithm achieved an \(F_{2}\)-score of 0.905, detecting 2 FPs and 165 FNs. In particular, the algorithm encountered challenges in classifying travel time observations  
Figure 5: Results of Dion–Rakha v2 algorithm.