# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
###### Abstract  
Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model's adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis. t +
Footnote †: journal: Medical  
to [6], sensors are also essential for adaptive traffic light control at intersections, monitoring road surface conditions [7; 8], and measuring the weight of moving vehicles [9]. The selection of an appropriate sensor type depends on the specific environmental conditions of the application [10]. Typical systems for vehicle detection and classification primarily utilise ultrasonic sensors [11], acoustic sensors [12], infrared sensors [13], ultrasonic sensors [14], inductive loops [15], magnetic sensors [16], video surveillance [17], laser sensors [18], and microwave radars [19]. In the past, pneumatic sensors were also used to measure the traffic flow characteristics using a hose placed on the road. Video detection or other aforementioned sensors have already surpassed this outdated technology [20]. All sensors measure physical characteristics, such as sound waves, light emissions, changes in magnetic fields, etc. However, correct processing of these input variables determines macroscopic data--speed, intensity, and traffic flow density [21]. These characteristics can be obtained not only from traffic sensors but also, for example, from road users' mobile devices [22]. Apart from these fundamental characteristics, some cases require additional information--vehicle classification, number of axles, vehicle weight, and car presence in a parking space or in front of an intersection [23]. In some applications, the direction of travel is also essential, especially for wrong-way vehicle detection systems [24]. The system must be capable of receiving information from multiple sensors at once. Therefore, wireless sensor networks (WSNs) connect all sensors to one consistent network. WSNs consist of remote sensor devices positioned at various locations within the road network, communicating wirelessly with each other. These sensors collect crucial traffic data, which is then processed and analysed to enhance traffic management, predict traffic situations, optimise traffic flows, and improve road safety. WSNs in transportation provide an effective tool for obtaining real-time information [25]. WSNs are used not only in transportation for traffic optimisation [26] but also in many other sectors--military [27; 28], healthcare [29; 30], and more. Figure 1 illustrates the basic diagram of WSN. The principle of operation of magnetic sensors is simple--they operate based on detecting magnetic fields disrupted by metal parts of passing vehicles. It induces an electrical current in the sensors as the magnetic field changes [31].