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
Smart Transportation: A Comprehensive Survey on Personalized Path Planning Algorithms
2024-01-18
940298 byte
Conference paper
Electronic Resource
English
Path-Planning Algorithms for Public Transportation Systems
British Library Conference Proceedings | 2001
|Personalized mobile-assisted smart transportation
IEEE | 2016
|Comprehensive transportation planning
Engineering Index Backfile | 1958
|Impacts of Comprehensive Planning and Smart Growth Initiatives on Transportation
Transportation Research Record | 2005
|