Cooperative Lane-Changing Optimisation of Connected and Autonomous Vehicles in Freeway Merging Area

In freeway merging areas, vehicles exhibit flexibility in lane-changing manoeuvres to facilitate merging. However, the lack of effective communication among vehicles leads to inadequate coordination between mainline and ramp vehicles at the merge point, increasing the likelihood of traffic congestion. The technology of connected and autonomous vehicles allows information interaction and cooperation between vehicles, which can effectively solve this problem and improve the efficiency of vehicle merging. This study proposes a merging optimisation framework for connected and autonomous vehicles, dividing the merging area into cooperative lane-change and trajectory optimisation areas. To simulate and manage connected and autonomous vehicles’ behaviour, the research employs VISSIM for scenario creation and leverages both VISSIM COM and Python for control purposes. In the cooperative lane-changing area, the optimal number of lane-changing vehicles is determined by considering traffic distribution in the inner and outer lanes downstream of the confluence area. Subsequently, the sequence and combination of these vehicles are established based on connected and autonomous vehicles’ cooperative lane-changing mode analysis. Within the trajectory optimisation area, the model refines each vehicle’s speed and acceleration, guiding connected and autonomous vehicles to merge smoothly and safely at the confluence point. The simulation results show that the optimisation framework for the freeway merging area proposed in this study performs well. As the level of demand increases, the scenario with control demonstrates superior performance in terms of enhanced trip efficiency, diminished total delay time and a reduction in the number of stops.