# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 2 Literature Review  
Urban mobility is a critical aspect of modern cities, impacting transportation efficiency, environmental sustainability, and overall quality of life. Understanding urban mobility patterns becomes increasingly important as cities continue to grow and evolve. In recent years, advancements in sensor technology and data collection have allowed researchers to delve deeper into the dynamics of urban mobility.  
To model traffic flow, researchers have turned to deep learning techniques. In [37], the authors comprehensively reviewed the latest advancements in utilising neural network algorithms to address various traffic forecasting issues, including road traffic flow, speed prediction, and passenger flow estimation. The survey also examines the advantages and disadvantages of graph neural networks compared to other deep learning models for traffic forecasting. A study by [38] offers an overview of deep learning techniques used in traffic flow prediction models and highlights the influence of various factors on the performance of these models in different scenarios.  
Thanks to the rapid evolution in deep learning and neural networks, better evaluation of measured data, their interpretation, and future prediction should be achieved. However, traditional machine learning models often struggle with the complex association features between road sections, especially when dealing with the exponential growth in the number of vehicles. In their study [39], the authors proposed a method for sharing information between road segments and used it for their prediction model, which combines convolutional neural networks and road segment grouping algorithms to predict traffic congestion more accurately. Similarly, in [40], the authors explored neural network-based traffic-flow prediction models. The authors used artificial neural networks to model traffic volume in major junctions in Istanbul. In [41], the authors aimed their study at demonstrating machine learning algorithms' ability to predict road traffic based on data from software traffic simulators in case of temporal unavailability of real data from road sections. Unlike microscopic traffic simulation, these neural network-based algorithms can work in real-time, making them suitable for applications such as determining variable message road sign speeds.  
In [42], the authors explored the possibilities of incorporating neural networks into processing data from traffic sensors. The study aimed to optimise the monitoring system and increase its efficiency and reliability in detecting and tracking vehicle movement on roads. In [43], the authors introduced a vehicle detection algorithm capable of tracking multiple vehicles simultaneously. They also developed a real-time vehicle tracking counter that integrates vehicle detection and tracking algorithms to measure traffic flow accurately. These models are based on deep learning algorithms and achieved an average accuracy of 92% with an average processing speed of nearly 38 frames per second. In [44], the authors introduced automatic vehicle detection and classification using deep neural networks. Their models are trained on the data from the Bangladesh vehicle images dataset, achieve an accuracy of over 83% and are highly usable in solving minor incidents on streets in order to avoid collisions, accidents or even human fatalities. In their study [45], the authors discussed using a wavelet neural network in traffic flow prediction tasks focused on the prediction for weekdays and weekends separately. Their model achieves a lower level of error in its predictions. In their study [46], the authors also considered temporal correlation between the data used for traffic flow predictions. In their model, the authors developed attention mechanisms to capture the spatiotemporal correlations of the data sequences to achieve accurate traffic flow predictions and make more rational decisions.