Paper Title
ARTIFICIAL INTELLIGENCE FOR URBAN MOBILITY AND TRAFFIC MANAGEMENT: A COMPARATIVE STUDY USING PRIVACY-PRESERVING DEEP LEARNING

Abstract
Urban mobility and traffic management present significant challenges in modern smart cities, requiring innovative solutions to optimize traffic flow while ensuring data privacy. This study presents a novel artificial intelligence framework for urban mobility optimization using privacy preserving deep learning techniques. We employed Design Science Research (DSR) methodology to develop and evaluate a comprehensive AI system that integrates differential privacy mechanisms with advanced neural network architectures. Our experimental evaluation on real-world urban traffic datasets demonstrates exceptional performance with 92% accuracy while maintaining ε = 1.0 differential privacy guarantee. The proposed system incorporates multi-modal data fusion, real-time traffic prediction, and adaptive signal control mechanisms. Comparative analysis with existing approaches reveals significant improvements in traffic flow efficiency (23% reduction in average waiting time), fuel consumption (18% decrease), and carbon emissions (21% reduction). The novelty of this research lies in the integration of federated learning with differential privacy for traffic management, enabling collaborative learning across multiple traffic networks while preserving sensitive location data. Clinical significance is demonstrated through reduced emergency response times (34% improvement) and enhanced pedestrian safety metrics. Keywords - Artificial Intelligence, Urban Mobility, Traffic Management, Differential Privacy, Deep Learning, Smart Cities, Federated Learning