Evaluating the Location of the Park-and-Ride System Using Multi-Criteria Methods: A Systematic Review

Abstract

The integration of intelligent transportation systems (ITS) in urban infrastructure has increased significantly, and one of the most notable examples is the development of autonomous vehicles (AVs). AVs have become a solution to various driving problems, such as performing complete overtaking maneuvers (OM). These maneuvers are considered one of the most difficult to carry out. Although there are many papers on OM maneuvers with AVs, not all of these studies focus on the performance of complete OM. Therefore, a comprehensive and scientific exploration of the analysis of complete OM with AVs is lacking. This study aims to address this gap through a systematic review following the PRISMA protocol as methodology, examining 51 articles published between 2008 and 2024 in the Science Direct, Scopus, and Web of Science (WOS) databases. The results showed that methodologies such as Model Predictive Control (MPC), Fuzzy Control (FC), and sigmoidal functions are used most to perform complete OM with AVs. MPC is the most relevant methodology due to its capability to be combined with other control systems and its predictive ability. FC and sigmoidal functions are also appropriate for dealing with inaccuracies and non-linear features associated with overtaking maneuvers. However, there are still complications related to computational complexity and sensor limitations. Future studies should consider and integrate the development of comprehensive systems that combine multiple real-time control methodologies and offer a robust combination of sensors. This review contributes to teaching studies that reveal promising opportunities for complete OM with AVs research and provide access to methodologies that could be optimized based on technological advances and emerging needs of the ITS sector. Addressing these knowledge gaps is essential to achieving safer and more efficient overtaking maneuvers by AVs.