Smart Transportation: A Comprehensive Survey on Personalized Path Planning Algorithms

Smart transportation systems are pivotal to the evolution of urban mobility, offering efficient, sustainable, and personalized travel experiences. Central to this advancement is the development of personalized path-planning algorithms for autonomous vehicles. This comprehensive survey delves into the state-of-the-art algorithms that enable tailored navigation for users, considering individual preferences and real-time traffic conditions. We examine heuristic approaches, machine learning techniques, and their integration in creating adaptive routes that optimize for time, energy efficiency, and user comfort. Our analysis includes a critical review of the existing literature, highlighting key contributions and identifying potential research gaps. The survey also discusses the practical implications of these algorithms in real-world scenarios, assessing their impact on the efficiency of autonomous vehicle fleets and user satisfaction. By synthesizing current findings and outlining future directions, this paper aims to provide a roadmap for researchers and practitioners in intelligent transportation systems, fostering the development of more sophisticated and user-centered autonomous vehicles