# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 4 Discussion  
To respond to all the research questions in Table 1 for this systematic literature review, the analytical arrangements of the significant studies are presented in this part. The study also offers specific analytical charts following the technical questions to provide a comprehensive overview of the examined material. A summary of the pertinent qualitative and quantitative data is included in Table 3.  
RQ1: What driving objectives did traffic management studies consider while using AVs? Figure 4 displays the primary driving goals that have received the greatest attention when using AVs and CVs. The combination of effectiveness, safety, ecology, and passenger comfort was the most crucial problem researchers have tackled, as shown in Figure 5.  
RQ2: What traffic management techniques have been suggested to handle the possible issues brought on by AVs? Figures 6 and 7 provide an overview of the methods put into practice to address the traffic management problems at junctions, interchanges, and roundabouts. As seen in this picture, the examined literature has mainly used optimization approaches and rule-based procedures to handle this challenge. Figure 5: Categorization of different driving objectives in the major studies. RQ3: What concerns and issues in traffic management techniques still need to be resolved? To respond to RQ3, the remaining flaws and gaps in the original research were examined while considering the methodology and validation environment. From a methodological standpoint, the available approaches were grouped into four main groups based on the outcomes investigated under RQ2: rule-based, machine learning, hybrid, and optimization-based learning. Firstly, most of the rule-based techniques now in use (such as [24; 37; 52; 53]) were created to increase junction efficiency and/or safety for AV-only or mixed-traffic situations. Rule-based approaches can be used for real-time junction management techniques and vehicle control due to their computational simplicity (e.g., [42]). Additionally, rule-based  
Figure 6: Article distribution depending on the methodology. Figure 7: Article distribution depending on techniques and driving objectives. methods are utilized to develop comprehensible and understandable models. A few rule-based methods have been verified by field testing or actual data (e.g., [42]). However, the model's goals and restrictions greatly enhance the complexity of the rule-based approach. Therefore, when more plans are considered in the rule-based strategy, the amount of enhancement of the target factors reduces. Because the rule-based technique uses statistical criteria and cannot ensure the outcomes are ideal, it has the additional disadvantage that performance may vary depending on traffic circumstances. Secondly, approaches based on optimization have been established to deal with single-goal or several-goal situations (e.g., [38; 86; 115]). Many optimization forms or searching algorithms have been devised or used to increase computing efficiency and discover optimal solutions. By adjusting objective functions, restrictions, and searching algorithms, the optimization-based approach can easily handle many goals and complicated situations. Methods based on optimization always look for the best answers for various traffic situations. Therefore, when optimality is ensured, optimization-based solutions provide optimum performance under different traffic situations. However, the time frame needed for intersection management may not always be satisfied by optimization-based approaches in terms of global optimum solutions. Additionally, when traffic volume and situation complexity increase, optimization-based approaches' computational complexity also considerably increases (e.g., [36]). As a result, only a handful of the currently used optimization-based methods were thought to be suitable for real-time control (e.g., [76; 92; 101]). Based on the outcomes of simulations, the current optimization-based methodologies have been validated.