# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 3 Materials and Methods
### Framework for Algorithm Development and Verification  
To develop and validate the proposed method, a data set collected over a 3-year period was used by dedicated efforts of the Czech Technical University in Prague, Faculty of Transportation Sciences. The initial data set was derived from 16 different origin-destination traffic surveys conducted between 2020 and 2022. These traffic surveys focused on evaluating the volume of transit traffic in urban or suburban areas of the Czech Republic. Data were collected methodically using Automatic Number Plate Recognition (ANPR) cameras during the day, in 12 h periods. Each survey consisted of several locations for data collection. The origin-destination pairs were then identified based on vehicle timestamps indicative of the routes traveled by the detected vehicles. Temporal alignment of records allowed for reconstruction of vehicle routes. From the data analyzed, 6643 origin-destination pairs were obtained, which captured a diverse range of real-world traffic scenarios, each influencing the travel times observed within the defined origin-destination pairs. Table 2 shows an example of the data set used for the paired vehicle travel times along a selected segment of the origin-destination pair. The origin-destination pairs predominantly showcased the travel times of vehicles that traveled on urban roads, with occasional inclusions of highway links. This heterogeneous environment resulted in a wide spectrum of travel times, depicting various traffic scenarios throughout the day. Most pairs exhibited stable travel times, reflecting consistent traffic flow throughout the monitored day. However, instances of congestion-induced increases in travel times, where temporary interruptions led to elevated travel times before returning to normal length, were observed throughout the day. These congestion-related events were observed during peak traffic hours and during the day, demonstrating the dynamic and stochastic nature of urban traffic patterns. An example of temporary spikes in vehicle travel times was recorded in traffic accidents. Another instance involved a pair affected by road maintenance works, where traffic lights were used to manage vehicular movement in a single direction. This unique scenario \begin{table}
\begin{tabular}{c c c c} \hline \hline
**License Plate** & **Entry Time** & **Exit Time** & **Travel Time (s)** \\ \hline
5 AP***9 & 08:35:09 & 08:46:03 & 654 \\ \hline
3 AV***0 & 08:35:16 & 08:46:11 & 655 \\ \hline
6M6***1 & 08:35:18 & 08:46:15 & 657 \\ \hline
8U9***9 & 08:35:24 & 17:08:24 & 30,780 \\ \hline
6E3***9 & 08:35:27 & 08:46:17 & 650 \\ \hline
5H1***1 & 08:36:04 & 08:46:30 & 626 \\ \hline
4M7***8 & 08:36:06 & 08:46:35 & 629 \\ \hline
6P6***1 & 08:36:07 & 08:46:38 & 631 \\ \hline
2H7***4 & 08:36:08 & 08:46:40 & 632 \\ \hline
2H3***8 & 08:36:11 & 08:46:44 & 633 \\ \hline
5H5***2 & 08:36:19 & 08:47:10 & 651 \\ \hline
3 SD***8 & 08:36:21 & 08:47:14 & 653 \\ \hline
5E6***1 & 08:36:39 & 08:47:16 & 637 \\ \hline
6E0***7 & 08:36:41 & 08:47:20 & 639 \\ \hline
3 AU***2 & 08:36:42 & 08:47:22 & 640 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of selected origin–destination pair segment vehicle travel times data set. introduced fluctuations in travel times, which further contributed to the diverse range of observed traffic patterns. The diverse nature of the data obtained and the variability in traffic scenarios, which includes both vehicles passing through and vehicles with partial destinations, proved to be highly problematic for the investigated existing solutions and therefore advocated for the development of the proposed algorithm. For the development of the proposed algorithm, the main premise was the ability to perform repeated filtration of transit traffic, using self-calibration to travel times samples on different pairs of origin-destination locations without manual interference into the algorithm. For this purpose, an auto-regression analysis was implemented.