# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 6 Results  
The display interface (Figure 6) divides the screen into four distinct sections:  
* Map, districts, and measuring points;
* Display of the selected measuring point time series;
* Graph showing the time series of the selected zone(s);
* Various information for the selected zone.  
The proposed algorithm demonstrates promising results in automating the process of determining the traffic zone using real-time data from the WAFE platform. Using crowdsourced information on traffic conditions, the algorithm effectively identifies congestion groups and efficiently delineates traffic zones.  
One of the key advantages of the proposed algorithm is its ability to adapt to dynamic changes in traffic patterns and incidents. Unlike traditional methods that rely on static traffic data or manual surveys, the algorithm can update traffic zone boundaries based on fresh information, without city knowledge.  
Figure 5: Creation of the TAZ.  
Figure 6: Visualization in Python—screenshot from the prepared application.  
Furthermore, the algorithm offers scalability and applicability to different urban environments, thanks to its data-driven approach and flexibility in parameter settings. By adjusting parameters such as cluster density thresholds and zone merging criteria, the algorithm can be tailored to specific cities or regions with varying traffic characteristics and infrastructure layouts.  
However, several limitations and challenges should be considered when interpreting the results of the algorithm.  
The primary limitation of this type of data processing is the quality and quantity of the raw data, which can only be guaranteed if the observed traffic is sufficiently high and the ratio of system users (e.g., WAAE) is adequate. Consequently, the process is primarily applicable in large urban areas.  
Data quality: The accuracy and reliability of the algorithm depend on the quality of the data obtained from the WAAE platform. Although WAAE provides valuable information on traffic conditions, the data can be subject to biases and inaccuracies, particularly in areas with low user coverage or sparse reporting.  
Computational complexity: The spatial analysis techniques used in the algorithm may involve computationally intensive operations, especially when processing large volumes of traffic data. Efficient implementation and optimization strategies are essential to ensure scalability and performance.  
Validation and ground truth: Validating the results of the algorithm requires ground truth data on traffic conditions, which may be challenging to obtain, especially for historical comparisons. Although historical traffic data and manual surveys can provide some validation, they may not fully capture the dynamic nature of traffic patterns.  
User privacy and data security: The use of crowd-sourced data from platforms such as WAAE raises concerns about user privacy and data security. Ensuring compliance with privacy regulations and implementing robust data anonymization techniques are essential to address these concerns.  
Despite these challenges, the proposed algorithm represents a significant advancement in the field of transport modeling. By harnessing the power of real-time data and advanced analytics, the algorithm has the potential to revolutionize the way traffic zones are determined and managed, leading to improved traffic models and, through that, enhanced overall mobility in urban areas.