# Transit Traffic Filtration Algorithm from Cleaned Matched License Plate Data
## 1 Introduction  
The increasing challenges posed by a growing global population have increased the complexity of transportation systems, necessitating effective transit traffic filtration strategies in various regions. Transit traffic filtration in traffic data analysis refers to the process of distinguishing between vehicles merely passing through a city and those that stop within it. This task enables the separation of the internal urban traffic flow from the external vehicle entry, allowing a more accurate analysis of the dynamics of city traffic. The critical need for efficient management of transit traffic comes from widespread problems of congestion, environmental degradation, and energy consumption that hinder the effectiveness of mobility networks. These challenges span urban, highway, and rural areas, underscoring the importance of transit traffic filtration in understanding traffic structure in regions. This, in turn, leads to various benefits such as alleviating congestion, reducing travel times, minimizing fuel consumption, and mitigating environmental impact, as noted by [PERSON] et al. [1].  
In densely populated urban settings, the filtration of transit traffic plays a key role in addressing the intricate dynamics of mobility. Beyond mere functionality, it contributes to improving the quality of life of urban residents [2], aligning with the broader objective of creating environmentally friendly and livable cities through more effective urban traffic planning tools.  
Rural areas, although distinct, are not exempt from transit traffic challenges. Identifying and filtering transit traffic in rural settings is crucial to maintaining accessibility and connectivity. Sustainable transportation practices in these regions contribute to the preservation of the landscape and mitigate the environmental impact of transportationactivities. At the same time, they contribute significantly to the daily traffic conditions in cities [3].  
Highways, as critical connectors between regions, face challenges of congestion. Effective highway traffic filtration involves understanding long-distance travel dynamics, optimizing traffic flow, and considering economic implications. This approach not only facilitates smoother travel experiences but also improves the economic vitality of regions interconnected by highways.  
The analysis of the current state of the art suggests that there is a lack of significant research focused specifically on the identification and filtration of transit traffic from overall origin-destination data. The vast majority of studies focus on the identification of real travel time for forecast or navigation purposes. These two tasks, although similar, have different goals and thus results. The method proposed in the subsequent chapters of this work is focused on the filtration of outliers based on travel time data rather than unveiling the true travel time.  
The insights derived from this research provide a tool for establishing evidence-based policies tailored to the specific needs of urban, highway, and rural settings. Policymakers can leverage these findings to develop targeted strategies that optimize regional mobility, reduce environmental impact, and improve the efficiency of transportation systems.  
The further structure of this paper introduces the most common methodologies found in the current literature, proposes a transit identification and filtration algorithm, and verifies real-world data collected in Czech Republic urban, rural, and highway traffic. The results of this research have multiple contributions to the current state of the art in the field of transport planning and management, by enabling data-driven decision making for future policy and traffic planning.