A Comprehensive Intelligent Traffic Monitoring System Based on a Novel Integration of Neutrosophic Multi-Criteria Decision-Making Techniques

With the spread of road accidents and traffic congestion that costs countries and governments a lot of money in addition to the loss of human lives, and since traditional methods of monitoring traffic have not been as effective as desired, attention has been drawn to the search for more effective solutions to the problem of monitoring and regulating traffic. With the spread of technology and the Internet of Things, UAVs have emerged as a promising tool for monitoring traffic, as they can fly for a sufficient period and operate in difficult climatic conditions, in addition to their ability to monitor traffic congestion and prefer less crowded roads for cars, as well as record road accidents and crimes and inform officials in real-time. Due to the many available types of UAVs, choosing the appropriate type with multiple and contradictory characteristics is a very difficult task. Therefore, in this research, we propose a new approach that combines the OWCM and WASPAS techniques, integrated with the neutrosophic set for the first time for selecting and evaluating UAVs used in traffic monitoring and regulation. The use of a neutrosophic set is an effective way to address the decision-making problem of ambiguity, where linguistic information is transformed into neutrosophic interval numbers using a new scale introduced in this paper, which highlights the importance of criteria and expert-based choices. The proposed approach has been proven effective in selecting a UAV for traffic monitoring and dealing with ambiguity efficiently through sensitivity analysis and comparison.