# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.1 Efficiency
In addition, an investigation of the impact of autonomous driving vehicles on traffic jams in mixed traffic flows with both human and autonomous drivers operating randomly was performed. Another paper [11] compared three-traffic-phase ACC to traditional adaptive cruise control (ACC) (TPACC). It was demonstrated that ACC-vehicles may significantly worsen the traffic system while causing traffic breakdown and lowering highway capacity at a bottleneck throughout a wide range of dynamic parameters of classical ACC. Contrarily, the TPACC vehicles either have no impact on traffic characteristics in the same parameters as dynamic regulations of TPACC, or they can occasionally even make them better. In addition, ref. [89] outlined the general planning and decision-making methodologies for traffic management in recent years, including machine learning base, optimization base prediction-based, and graph-based approaches. Similar to this, ref. [37] defined the lane-changing decision-making of several AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) issue, where each AV bases its lane-changing judgments on the actions of both surrounding HDVs and AVs. According to thorough experimental research, their suggested MARL framework regularly beats several cutting-edge benchmarks in effectiveness, safety, and driver comfort. The authors of another study [57] employed a hierarchical design for cooperative intersection management to avoid network deadlock and reduce computational latency. They proposed the advanced cooperative vehicle-actuator system, with a deadlock-free protocol (ACVAS). It can reduce processing costs, recognize and resolve congestion, and reach conclusions quickly. Traffic management techniques aim to boost the infrastructure's capacity to reduce traffic jams. In this context, ref. [38] offered a CAV-based alternative approach for traffic management, namely SWSCAV, and evaluated its effectiveness in comparison to other traffic management systems, such as lane control signals (LCS) and variable speed limits (VSL). In a simulation of the urban mobility (SUMO) environment, the recommended technique is assessed for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The occurrence of densities surpassing 38 and 28 vehicles per km/lane at the event site is reduced by 12.68 and 8.15%, respectively, by the recommended technique. This study [90] provided a CV framework based on the Intelligent Vehicle Infrastructure Cooperative Environment that locates the vehicles using the two-way arrival time. To collect the vehicle kinematics data via the onboard system, the Kalman Filter (KF) is employed to increase the precision of the vehicle position. The associated method is then used to predict the trajectory of the vehicle. Data indicated that, compared to the results obtained before filtering, the suggested method's vehicle speed and localization errors were reduced by 66.67% and 83.33%, respectively. It has been noted that research on autonomous vehicles is now moving away from conventional statistical models and toward adaptive machine-learning methods. As a result, ref. [39] offered a variety of deep learning techniques for increased AV efficiency. Similarly, ref. [40] sought to create a conceptual framework for addressing a list of requirements that considers several machine learning algorithms to identify the best algorithm for the intelligent regulation of AVs for a smooth flow through junctions. Furthermore, ref. [41] concentrated on the Variable Speed Limit (VSL) based on Q-Learning using CAVs as actuators in the control loop, considering mixed traffic in metropolitan regions. The observed findings demonstrate that Q-Learning based VSL can adapt to changing CAV penetration rates and learn the control policy while reducing travel times. In the same manner, ref.