Traffic control strategy plays a significant role in obtaining sustainable objectives because it not only improves traffic mobility but also enhances traffic management systems. It has been developed and applied by the research community in recent years and still offers various challenges and issues that may require the attention of researchers and engineers. Recent technological developments toward connected and automated vehicles are beneficial for improving traffic safety and achieving sustainable goals. There is a need to develop a survey on traffic control techniques, which could provide the recent developments in the traffic control strategy and could be useful in obtaining sustainable goals. This survey presents a comprehensive investigation of traffic control techniques by carefully reviewing existing methods from a new perspective and reviews various traffic control strategies that play an important role in achieving sustainable objectives. First, we present traffic control modeling techniques that provide a robust solution to obtain reasonable traffic and sustainable mobilities. These techniques could be helpful for enhancing the traffic flow in a freeway traffic environment. Then, we discuss traffic control strategies that could be helpful for researchers and practitioners to design a robust freeway traffic controller. Second, we present a comprehensive review of recent state-of-the-art methods on the vehicle design control strategy, which is followed by the traffic control design strategy. They aim to reduce traffic emissions and energy consumption by a vehicle. Finally, we present the open research challenges and outline some recommendations which could be beneficial for obtaining sustainable goals in traffic systems and help researchers understand various technical aspects in the deployment of traffic control systems.

Unsafe lane-changing maneuvers contribute to accidents and merging conflicts due to variations in traffic states and driver behaviors at on-ramp merging areas. Connected and autonomous vehicle (CAV) technologies promise significant improvements to traffic management systems, including the possibility of reducing collisions. CAVs provide various driving supports, which are expected to reduce collisions by exploiting information from surrounding vehicles. This paper proposes a collision avoidance model that predicts the occurrence of collision events associated with different vehicle movements in merging areas. A decision-making system consisting of a threat assessment model is proposed to assess the risks associated with different movements, and to avoid collisions based on safe lateral and longitudinal acceleration of the on-ramp vehicle in the merging area. Then, evasive action of the main lane vehicle is assessed based on its braking response during the merging interaction with the on-ramp vehicle. Finally, in emergency situations, a vehicle stabilization mechanism is introduced to preserve the vehicle states within the envelope of danger. The results show that the model could be used to avoid collisions in multiple scenarios and predict the occurrence of collision events associated with different vehicle movements. In summary, the collision avoidance model provides a valuable safety management tool.

Connected and autonomous vehicles (CAVs) have the ability to enhance traffic flow and road safety by significantly reducing human error. Although some collision risks may be eliminated in autonomous vehicles, other potential risks remain, especially at on-ramp merging areas. This paper proposes a collision avoidance model for on-ramp merging of autonomous vehicles under different scenarios. Simulation results suggest that the proposed method can reduce the risks of collision at on-ramp merging areas. Moreover, we assessed the effectiveness of the strategy in terms of traffic flow speed variations with different penetration rates of CAVs. Results show that the collision avoidance strategy leads to lower speed variations. This study reveals that the information obtained from the proposed collision avoidance model could be helpful for improving traffic safety and enhancing urban mobility.

Rear-end crashes are a major type of traffic crash that occur more frequently on the road, leading to a large number of injuries and fatalities each year around the world. Examining the overtaking behaviors and predicting the collision risk probability are essential issues for preventing a rear-end collision risk and improving road safety. To this end, we proposed the rear-end collision model to examine the risks associated with the lagging vehicle (LAV) movements. Specifically, this research aims to develop a model for assessing rear-end collision risks by considering different braking reaction times (BRTs) of the LAV. Firstly, we introduce the deep neural network (DNN) to learn the movements of LAV. Then, a collision-free modeling based on the deep reinforcement model (DRM) is proposed to mitigate the collision risks associated with LAV movements to nearby vehicles thus improving traffic safety. Finally, we incorporate the generalized linear model (GLM)-based BR time with driver’s driving behavior, aiming to identify the driver’s distraction with different vehicle movements. Various performance metrics, such as modified time to collision (MTTC), deceleration rate to avoid collision (DRAC), and post-collision change in velocity (Delta-V), are used to identify rear-end conflicts and to demonstrate the effectiveness of the developed model. The simulation results indicated that the developed model could reduce rear-end collision risks with the leading vehicle (LEV) based on different LAV speeds. Furthermore, the North Carolina (NC) traffic real-time crash data is used to demonstrate the efficacy of the developed model. The results indicated that different traffic conditions, such as driver behaviors, and road and climate conditions influence the severity of rear-end crashes.