Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

As the accident-prone sections and bottlenecks, highway weaving sections will become more complicated when it comes to the mixed-traffic environments with connected and automated vehicles (CAVs) and human-driven vehicles (HVs). In order to make CAVs accurately identify the driving behavior of manual-human vehicles to avoid traffic accidents caused by lane changing, it is necessary to analyze the characteristics of the mandatory lane-changing (MCL) process in the weaving area. An analytical MCL method based on the driver’s psychological characteristics is proposed in this study. Firstly, the driver’s MLC pressure concept was proposed by leading in the distance of the off-ramp. Then, the lane-changing intention was quantified by considering the driver’s MLC pressure and tendentiousness. Finally, based on the lane-changing intention and the headway distribution of the target lane, an MLC positions probability density model was proposed to describe the distribution characteristics of the lane-changing position. Through the NGSIM data verification, the lane-changing analysis models can objectively describe the vehicle lane-changing characteristics in the actual scenarios. Compared with the traditional lane-changing model, the proposed models are more interpretable and in line with the driving intention. The results show significant improvements in the lane-changing safe recognition of CAVs in heterogeneous traffic flow (both CAVs and HVs) in the future.

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours.

This study proposes a cooperative lane‐changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane‐changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane‐Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle’s longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane‐changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle’s lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014 m, 0.5631 m, and −0.7601 m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability.

Most existing longitudinal control strategies for connected and automated vehicles (CAVs) have unclear adaptability without scientific analysis regarding the key parameters of the control algorithm. This paper presents an optimal longitudinal control strategy for a homogeneous CAV platoon. First of all, the CAV platoon models with constant time-headway gap strategy and constant spacing gap strategy were, respectively, established based on the third-order linear vehicle dynamics model. Then, a linear-quadratic optimal controller was designed considering the perspectives of driving safety, efficiency, and ride comfort with three performance indicators including vehicle gap error, relative speed, and desired acceleration. An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. Based on the Matlab/Simulink experimental simulation, the analysis results show that the proposed strategy can significantly reduce the gap error and relative speed and improve the flexibility and initiative of the platoon control strategy compared with the unoptimized strategies. Sensitivity analysis was provided for communication lag and actuator lag in order to prove the applicability and effectiveness of this proposed strategy, which will achieve better distribution of system performance.

In many cases, the final path selection of travellers’ is not the shortest path, due to the limited computing power and high cost of path search. To solve the problem, this paper proposes a day-to-day (DTD) stochastic traffic flow assignment model that regulates the traffic flow based on the travel time (travel cost) and residual congestion of optional paths. The regulation mechanism is called the mixed regulation. Then, the authored proved the existence, uniqueness and stability of the model solution. The proposed model was verified through simulation on a Nguyen-Dupuis road network. The results show that traffic flows and travel times of all paths reached the equilibrium state, thanks to the DTD mixed regulation for 20~30 days. From the traffic flows and congestion degrees of different sections, it can be seen that our model with mixed regulation diverts the traffic flow to the sections with a low congestion degree, and encourages travellers to drive through the sections with a low traffic flow. In addition, the congestion degrees of the four most congested sections decreased by 5.8%, 4%, 7% and 1.2%, respectively, and the entire road network exhibited a slight downward trend in mean congestion degree. These results prove that our model can uniformize the traffic flow, improve the operation efficiency and alleviate the congestion of the road network. These findings shed new light on the control, guidance and planning of traffic flow in road networks.