# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 5 Conclusions  
Autonomous and connected vehicles have long been studied topics in the framework of ITS. This study examined current studies on traffic management strategies that use AVs and CVs to improve road safety and promote safe travel. Additionally, summarizing these strategies and analyzing the results and goals attained are interesting. This study acquired an extensive understanding of the various traffic management strategies used, the open problems in this area, associated difficulties, and future directions.  
* A comprehensive review of 315 publications that were published between 2012 and 2022 was given in this study. In the end, this research examined 100 studies on traffic management, including AVs and CVs at junctions, interchanges, and roundouts that had passed the quality evaluation. According to statistics on the number of research papers published on this subject each year from 2018 to 2022, additional research is anticipated in 2023-2024, particularly in machine learning techniques.
* The primary goal of this literature review was to describe the most recent publications in the field of connected and autonomous vehicles to understand current traffic management techniques and identify difficulties and limitations. The study addressed three research questions, as per the analytical discussions. The approach recommended by [107] generated the maximum performance for the techniques described in this research out of all the articles considered in this evaluation. However, Due to the inability of human-driven cars to rationally communicate and cooperate with other road users, mixed traffic at unsignalized intersections may be difficult to evaluate in such a technique. Rule-based approaches made up 34% of the papers chosen, followed by optimization techniques at 39%, hybrid methodologies at 13%, and 14% of the publications that were chosen employed ML techniques.
* The study assessed the behavior of the recommended approaches associated with effectiveness, safety, environmental effects, and passenger ease, and the study's findings were published. Investigators utilized numerical testing, math, simulators, mathematics, numerical testing, and other techniques in 95% of the selected articles to support their theories, whereas 5% used toy vehicles, actual automobiles, or field tests. It is recommended that AI-based traffic management structures may minimize some of the issues said by optimizing the data collection method. This may include learning traffic characteristics and human behaviors, projecting traffic attributes, and creating more effective traffic-management decisions. The recommended approaches should be more extensively assessed to cope with sensor variation, since car manufacturers install various sensor types with varying features and quality to collect data.
* Finally, RQ3 was addressed by discussing the primary research's remaining shortcomings and gaps while considering various factors, such as methodology and validation environment. In total, 90% of research has focused on pure AVs, in contrast to the reality, which will soon involve a combination of human-driven automobiles, AVs, pedestrians, and bicycles.  
As a result, one potential study area is leveraging AV characteristics to gather environmental data in mixed traffic to enhance traffic management system performance. It continues to be challenging to create approaches for taking V2X communication technology restrictions into account in simulations. Nevertheless, a further survey concentrating on traffic management can be performed, given the rising quantity of research articles in this area.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.