# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 6 Conclusions  
In conclusion, the transit traffic filtration algorithm introduced in this study represents a significant step forward in the accurate identification and filtering of transit traffic within complex traffic environments. By minimizing the need for extensive manual calibration and providing a more adaptive and robust solution, the algorithm offers a practical tool for traffic engineers and urban planners seeking to improve traffic management and planning. The high levels of the \(F_{2}\)-score metric demonstrated in various types of routes indicate the potential for this algorithm to be widely adopted in different urban and suburban contexts. Through a comprehensive review of the literature, suitable algorithms for the validation process were identified: Jang algorithm, Dion-Rakha versions 1 and 2, and Robinson-Polak algorithm--each exhibiting distinct strengths and limitations in managing various traffic scenarios. Closer analysis has shown that current algorithms rely largely on parameter settings for calibration and therefore pose substantial obstacles to optimal performance across various traffic scenarios and effort in the setup phase. This leads to poor elasticity of those methods and non-feasibility for automated use across different routes. In comparison, the proposed algorithm sought to simplify the classification of travel time observations without the need for extensive manual calibration and adjustments, simplifying implementation and improving usability. Although the Jang algorithm and the Dion-Rakha algorithms primarily focus on travel time estimation tasks, which are less dependent on the correct identification of false negatives, they can potentially serve as effective tools for transit travel time filtering when appropriately calibrated. In contrast, the Robinson-Polak algorithm, designed to classify transit vehicles using the overtaking method, confronts limitations on routes characterized by significant time fluctuations or alternative routes. Moreover, its performance is intricately linked to initial parameter settings. Furthermore, the proposed algorithm extends beyond outlier detection; its ability to filter transit vehicles with valid travel times makes it potentially suitable for travel time estimation purposes. This dual functionality improves its utility to provide reliable data to inform other traffic users. Such promising results not only validate the effectiveness of our approach but also underscore its potential to significantly advance automated outlier detection methodologies in transportation research, while simultaneously offering added value as a travel time estimation tool. In this place, it is worth highlighting that transit traffic identification (filtration) is a task different from true travel time estimation, as lower outliers are tolerated in transit filtration, whereas they are not in travel time estimation. The proposed algorithm presents a valuable tool to improve the accuracy and efficiency of the identification of transit traffic. However, its full potential can only be realized through continued refinement and validation in diverse real-world settings. Future research should focus on addressing the identified limitations, with the goal of developing a truly universal algorithm that can be seamlessly applied across a wide range of traffic environments. Such advancements would not only enhance the utility of the algorithm for traffic engineers and urban planners but also contribute to the broader goal of creating more efficient, sustainable, and resilient transportation systems. By continuing to refine the proposed method and exploring its application in new contexts, we can move closer to achieving a comprehensive solution for transit traffic filtration. This ongoing work will be crucial in ensuring that the algorithm remains at the forefront of traffic data analysis and continues to provide valuable insights to improve urban mobility and reduce the environmental impact of transportation systems. Conceptualization, P.R. ; methodology, P.R. and M.M.