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Traffic Congestion Detection among Manual and Autonomous Vehicles using AHP Algorithm
Abstract
Autonomous vehicles (AVs) are the newest intelligent transportation system (ITS) solutions that can move without human intervention. These goods
continue their trajectory with a variety of sensors comprising various components. Utilizing these technologies effectively within the logistics business
might generate a competitive advantage. There are numerous AVs on the market today, with some being superior to others in terms of build quality, range of functions, and design. Choosing an efficient, optimal, and dependable vehicle is one of the most logistical planning issues. Consequently, selecting an AV
based on a number of criteria is a multi-criteria decision-making (MCDM) challenge. This study focuses on the application of the Analytic Hierarchy
Process (AHP) as one of the multi-criteria decision-making (MCDM) techniques to evaluate road transport vehicles. AHP is one of the most popular approaches for evaluating transportation and traffic projects. This study provides a complete evaluation of AHP-evaluated studies on road transport vehicles. Several databases, including Web of Science and Scopus, were queried to collect research articles for the study. The focus of the research is on road transport vehicles, however the AHP method's performance in the road sector is briefly examined. The results indicate that AHP is utilised in the majority of research evaluating electric and driverless vehicles.Finally, the outcomes of the research are examined, and suggestions for further research are made.
Keywords
References
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