# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Flow Tracking System (13 Papers)  
The second theme of this PRISMA analysis delves into the escalating trend of urbanization. The efficient management of human and vehicular flow within urban environments is critical for the functionality of smart cities. Flow tracking systems leverage machine learning to monitor and predict movement patterns, enabling better infrastructure utilization and enhancing public safety. This section explores how various studies have addressed these challenges and the innovative solutions proposed to optimize flow tracking systems in smart cities. With 55% of the global population currently residing in urban areas and projections indicating a rise to 68% by 2050, it becomes crucial to comprehend the flow of people and events in this urban landscape. This understanding is made possible through technologies like machine learning and edge computing, which play pivotal roles in shaping sustainable smart cities. As urbanization intensifies, the movement of people in smart cities leads to challenges such as congestion and security risks. Real-time understanding of people flow statistics is essential to managing these challenges effectively. This not only aids in efficient city management but also elevates service levels in public areas. The theme explores various studies and technologies related to people flow statistics, with a particular focus on machine learning and deep learning. These technologies offer practical insights into predicting and optimizing the dynamic movements within urban environments. The narrative aims to unravel the complexities of urban life, where data-driven insights pave the way for responsive and intelligent city planning. The authors suggest a smart system for detecting and recognizing unusual human flow patterns in smart cities [46; 49; 62]. They create a people flow statistics system employing machine vision and deep learning, implementing the YOLOv3 algorithm for pedestrian detection and a Mean Shift tracking algorithm for improved tracking. Experimental simulations validate the system's practicality. In the context of population growth and urbanization, the focus also falls on utilizing machine learning for enhancing urban mobility planning, particularly in Bilbao, Spain, through the URBANITE project. The URBANITE project is described as an open-source ecosystem for testing mobility policies, providing valuable insights and a considerable acceleration in simulations using the ML module. The discussion concludes with plans to extend the ML approach to other pilot cities within the URBANITE project, enhance user-friendliness, and upgrade the research prototype into a comprehensive, open-source system for European cities [49; 62]. In this case, the machine learning approach uses data from microscopic traffic simulators to optimize city mobility policy proposals. Multiple machine learning algorithms, such as logistic regression, decision tree, random forest, linear support-vector regression, and gradient-boosting regressor, are also employed in other articles for policy prediction [45; 46; 49; 62]. As a method for enhancing smart city surveillance, the application of Machine Learning in Motion Detection and Pedestrian Tracking-assisted Intelligent Video SurveillanceSystems (ML-IVSS) also plays a pivotal role. The ML-IVSS model aims to improve abnormal behavior detection in surveillance systems by utilizing optical flows to calculate various parameters like acceleration, speed, direction, trajectory, and motion amplitudes. The model relies on a dual-stream Convolutional Neural Network (CNN) to address the lack of hidden movement data and uses a weighted sum of squares algorithm to minimize optical flow in the neighborhood. The model integrates adaptive Gaussian mixture modelling, spatial-temporal convolutional neural networks, and object detection with visual tracking approaches to enhance abnormal behavior analysis.