# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
###### Abstract
With proper system calibration, magnetic sensors can measure speed, traffic flow intensity, and congestion [32] and also categorise vehicles by length [33; 34; 35]. However, magnetic sensors also have disadvantages. There may be discrepancies in the vehicle detection process because there is a problem with a blind zone of the geomagnetic signal. This problem can occur between vehicle axles, specifically when the sensor detects longer vehicles with extended chassis--trucks, buses, and SUVs. Consequently, multiple detections are also possible during low-speed manoeuvres. As illustrated in Figure 2, the bus shows increased magnetic interference signals from its front and rear wheel areas. Nevertheless, a blind zone between the front and rear axles within the magnetic signal may lead to incorrect vehicle identification by the sensor. Figure 1: The basic scheme of WSN. The suitability of using a specific type of sensor depends on many application conditions. From the perspective of evaluating the measured data, it is necessary to find procedures and algorithms that ensure the most accurate results [36]. The global outbreak of the COVID-19 pandemic has reshaped the dynamics of urban life in unprecedented ways. Cities worldwide experienced significant disruptions in various sectors, including transportation. The impact of the COVID-19 pandemic on mobility was extensive, mainly due to the implementation of widespread lockdowns, social distancing measures, and other anti-pandemic interventions. Quarantine measures, social distancing, travel restrictions, and movement bans reduced mobility worldwide. Like many others, the city of Zilina in Slovakia faced the intricate challenge of managing urban mobility during these extraordinary times. Understanding the impacts of the pandemic on traffic and transportation systems is crucial for effective urban planning and policymaking. This study delves into the nuances of traffic dynamics, with a particular focus on the pandemic period. We will examine how traffic density changed during the analysed period. In response to urban traffic's complex and dynamic nature, this research employs advanced artificial intelligence models, specifically neural networks, to model and analyse the intricate interplay between various factors influencing traffic. Neural networks have proven invaluable tools in capturing and understanding complex relationships within large datasets, making them well-suited for modelling the multifaceted aspects of urban mobility. The study leverages real data from a network of sensors strategically placed throughout Zilina, capturing the movements of vehicles across key intersections. This article focuses exclusively on magnetic sensors integrated into WSN. These sensors provide real-time information, allowing for a comprehensive analysis of traffic patterns. Importantly, the study incorporates variables related to the COVID-19 pandemic, sourced from international databases tracking government responses and public health measures. By integrating pandemic-related variables into the neural network model, we aim to analyse how anti-pandemic measures have influenced traffic flow. The study not only seeks to clarify the pandemic impacts but also brings valuable insight for future urban planning in a post-pandemic era. This research also shows the potential of neural network models in modelling the complexities of traffic dynamics. Beyond the immediate implications for Zilina, the findings of this study may inform broader discussions on resilient and adaptive urban transportation systems in the face of unforeseen challenges. The following sections of the article correspond to the structure of a scientific study. The Literature Review section captures the current state of knowledge in the research area. The Methodology and Data section describes the data used in the study and the process of its thorough preparation and also describes the research methodology and briefly explains  
Figure 2: Blind detection zone between front and rear axle of a long bus.