Optimizing Urban Safety and Traffic Management: A Machine Learning Approach to Sensor Placement for Intelligent Transportation and Crime Detection Systems

Abstract

This thesis explores the use of machine learning algorithms for optimizing sensor placement in urban areas to improve crime prevention and traffic management. Focused on the City of Toronto as a case study, it integrates traffic sensor data with vehicular crime statistics to propose a model predicting potential hotspots for traffic violations and optimal locations for crime-prevention sensors. This research employs the use of Random Forest, Long Short-Term Memory, Fourier Series Neural Networks, Support Vector Machine, and the Feed Forward Neural Network, to provide insights that are actionable for safer, smart cities. Providing these results for government officials, law enforcement agencies, and research with an ease of access cloud-based tool to serve as a Software as a Service format. The study underscores the importance of leveraging advanced data analytics in urban planning, suggesting a direction for future research and implementation in intelligent transportation systems.