# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 5 Discussion
The entire network, along with the publicly accessible interface, was developed as part of the INTERREG V-A SK-CZ/2019/11 project CLEVERNET--\"Deploying innovative sensor networks in cross-border regions\". The main activities of the project commenced in January 2021. Over time, it will be possible to analyse data without the impact of the pandemic, and increasingly accurate data will be available as the network is constantly troubleshooting and calibrating sensors. Further analysis and applications for using such a network will be the subject of future studies. The data from the sensors is still publicly available; it can be viewed, compared, and downloaded practically without restrictions in a user-friendly environment. However, the truth is that in practice, it still has no other function than a presentational--informational one. However, while the current usage of the sensor data is limited to informational and presentational purposes, there is significant potential for its future application. For example, city planners and policymakers could use the data to make informed decisions about traffic management, public transportation improvements, and infrastructure development. The data could also be valuable for researchers studying urban mobility patterns, environmental impacts, and smart city technologies. Furthermore, businesses and entrepreneurs might find innovative ways to leverage the data to create or enhance existing services. For instance, companies involved in logistics and transportation could optimise their routes and operations based on real-time traffic information. Similarly, startups could develop applications that provide residents and visitors with up-to-date information on traffic conditions, helping them to choose the most efficient travel routes. Apart from the mentioned usage of the publicly available data from the sensors, we can mention that the usability of data from the sensors is very wide and understanding the inputs and outputs of traffic flow values provides several significant benefits that can enhance various aspects of traffic management, optimisation of road networks and intersections and planning new roadways, expanding existing ones, and designing effective traffic management strategies or optimising public transportation routes and schedules. Moreover, analysing traffic flow data helps optimise traffic signal timings at intersections. By understanding traffic patterns and congestion levels, traffic signals can be adjusted to improve vehicle throughput, reduce delays, and enhance overall traffic flow. Data-driven insights enable the implementation of adaptive traffic signal systems that adjust signal timings in real-time based on current traffic conditions, leading to more efficient traffic management. From the macroscopic point of view, traffic flow values are used to create and refine models that simulate overall traffic patterns and congestion in a city. These models help in understanding traffic dynamics and in planning infrastructure improvements. On the other hand, from the microscopic point of view, detailed traffic flow data supports models that simulate individual vehicle interactions and movements. These models are useful for studying the effects of specific roadway changes or traffic management strategies. Finally, real-time traffic flow data help in detecting incidents such as accidents or road closures. This allows for timely responses and implementation of alternative routing strategies to minimise disruption. In summary, the benefits of analysing traffic flow values are extensive and impact various facets of traffic management, urban planning, and transportation efficiency. By leveraging these insights, cities can enhance their traffic systems, improve safety, and provide better services to residents and travellers. In summary, while the immediate application of the sensor data in the city of Zilina may be limited, its potential for future use is vast.