# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.7 Efficiency, Safety, and Passenger Comfort  
In this section, the papers that concurrently evaluated efficiency, safety, and passenger comfort because these factors are crucial for controlling traffic were examined. Reference [117] offered an uncoupled and dispersed solution that employs graph-based algorithms to optimize longitudinal trajectories for many vehicles at metropolitan crossings while considering efficiency, passenger comfort, and accident avoidance. Compared to the intersection control approach for human-driven autos and a non-cooperative control strategy, the recommended method can improve junction performance. Reference [76] created an automated intersection control (AIC) to enhance system fairness, safety, throughput, travel time, and passenger experience. To schedule vehicles and ensure that they cross the junction quickly and smoothly, the quality-of-experience-oriented autonomous intersection control (QEOIC) method was recommended by the authors. By predefining the decision zone and dividing the junction into numerous collision zones, they also developed a scheduling algorithm to determine the priority of the automobiles in different collision regions, which linearized the collision constraints. They emphasized that the suggested method may be used to implement real-time traffic control. Similarly, ref. [77] suggested a self-organizing and collaborative framework to direct vehicles over a junction without confrontation. The proposed technique performed better than the conventionally actuated process in terms of overall latency. To prevent accidents and traffic jams, increase safety, and enhance passenger comfort, ref. [36] presented Dynamic Traffic Congestion Management in Social Internet of Vehicles (SloV). In their article, they suggested a traffic scheduling algorithm to maximize throughput for the movement of vehicles at a road intersection while including social ties between the moving vehicles and the roadside units (RSUs). The algorithm uses the amount of traffic on the given route to predict the flow rate of vehicles for lanes at junctions. A condition matrix is created to ensure smooth traffic flow while considering various road segment routes. Social interactions are developed on various travel-related demands to make driving more secure, responsive, and enjoyable. The simulation findings show how the suggested plan is more effective than the Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm in terms of high traffic throughput, service rate, and a reduction in total journey time, delay time, and average waiting time. The Elman neural network (ENN) model was created by [118] and optimized with the sparrow search algorithm (SSA) to establish a connection between connected autonomous vehicles and human-driven automobiles (CAVs). The numerical simulations demonstrate that the driving approach can increase road capacity and reduce traffic oscillations. Traffic effectiveness, safety, and driving comfort enhance as CAV penetration rises. Recently, in 2022, using the same approach, ref. [49] examined communication quality prediction in mobile Internet of Vehicles (IoV) networks. New Outage Probability (OP) terms that may evaluate the performance of model were developed. Then, an intelligent OP prediction strategy using an Elman model was suggested for real-time OP prediction. This was assessed using information produced by the OP terms. The findings collected suggest that the Elman-based strategy delivers superior forecasting effect than other approaches in terms of computing difficulty and accuracy rate. Considering Machine Learning techniques in recent years, ref. [50] provided a thorough overview of Variable Speed Limit (VSL) and Ramp Metering (RM) control algorithms, encompassing the most modern methods based on reinforcement learning.