# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 3 Materials and Methods
### Verification of the Method  
To facilitate the analysis and validation of the proposed algorithm, 120 origin-destination pairs were deliberately selected from the extensive data set to ensure the inclusion of examples representing stable flows, as well as fluctuations caused by congestion events, traffic accidents, and road maintenance. The selected origin-destination pairs were then divided into three different categories based on their inherent characteristics, one of which is the travel time pattern during the day. Traffic flow volume at 12 h in these selected pairs ranged from 180 to 10,000 vehicles, further contributing to the robustness of the data set used. The categories are represented by three example routes, named routes A to C, each showing distinct travel time patterns and the corresponding traffic scenarios. To assess the effectiveness and precision of the proposed algorithm, it was necessary to determine valid travel time values for each pair of origin-destination locations. Valid travel times between the origin-destination pairs, identified as _'transit traffic'_, represent vehicles that traveled from their origin location to their destination without stopping on the way. This process was carried out manually by the authors based on their domain-specific knowledge and expertise. The results of this initial classification can be seen in Figure 2.  
For the purpose of evaluating the new algorithm, four existing methods were chosen as the benchmark (the Jang algorithm, Dion-Rakha version 1, Dion-Rakha version 2, and the Robinson-Polak algorithm). The choice of these algorithms was motivated by their calibration difficulty and robustness in different traffic scenarios. As the purpose of this work is to create a robust tool for automated transit filtration without the need for extensive setup and calibration, other methods described in the Section 2 were deemed unfit for these conditions. The configuration of the internal parameters of the compared algorithms was in accordance with the recommended values in the literature [7,8,15]. As the algorithm's objective was outlier classification, a comparative evaluation consisted of analyzing the confusion matrices generated from the results of each applied algorithm. These measures helped to assess the algorithm's ability to correctly classify travel times and the instances of misclassification (false positives and false negatives). The \(F_{\beta}\)-score metric, derived from the confusion matrix data, provided a single numerical value able to weigh sensitivity and accuracy according to the specific needs of each task, where the parameter \(\beta\) indicates how many times sensitivity is more important than accuracy. Due to the emphasis on high sensitivity, the parameter \(\beta\) was set to 2 for this task. This setting reflects the fact that sensitivity is twice as important as accuracy. In order to evaluate the algorithm's responsiveness to sudden fluctuations in travel times, the collected travel time data were plotted in the travel time-daytime domain (see examples in Figure 2). This visual analysis led to the identification of three different categories of routes (origin-destination pairings) that were used as testing data for the responsiveness of the proposed classification algorithm. 1. Route A--urban arterial route An important feature of this route is an intersection with a directional ramp that facilitates the transition from a ring road to a main arterial road that enters the city. Other various exits along the route serve nearby residential and commercial areas, allowing vehicles to stop and thus observe non-transit travel times. Furthermore, the presence of a dedicated bus lane plays a key role in influencing travel times along this route, particularly during the morning peak hours. A visual analysis of Figure 1(a) shows that morning travel times peak at almost 30 min, reflecting the impact of capacity limitations due to infrastructure characteristics.