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

Xuling, LIU; ZHANG, Xiaoning · 2025 · DOAJ

DOI: 10.7307/ptt.v37i6.854

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Summary

This study addresses traffic congestion and inefficiency in freeway merging areas, where the lack of coordination between mainline and ramp vehicles often leads to delays. The authors propose a merging optimization framework for connected and autonomous vehicles (CAVs) that leverages real-time communication to coordinate lane changes and trajectories. The research is motivated by the potential of CAV technology to enhance traffic flow efficiency and safety through cooperative driving strategies, specifically targeting the challenges of unbalanced traffic distribution and the need for dynamic, real-time control in multi-lane merging zones. The methodology divides the merging area into two distinct zones: a cooperative lane-change area and a trajectory optimization area. In the cooperative lane-change area, a roadside communications and computing centre (RCCC) determines the optimal number of vehicles to change lanes to balance downstream traffic flow. The system calculates the optimal sequence and combination of lane changes by evaluating delay times across six specific cooperative scenarios, which account for safety distances and vehicle acceleration/deceleration constraints. In the trajectory optimization area, the model refines each vehicle’s speed and acceleration using a linear time-discrete optimization model to maximize total speed while ensuring collision-free merging. The framework was simulated using VISSIM version 8.0, integrated with Python for control logic, across various traffic demand levels and split ratios. Simulation results demonstrate that the proposed optimization framework performs effectively, particularly as traffic demand increases. Compared to uncontrolled scenarios, the CAV-controlled scenarios exhibited superior performance in terms of enhanced trip efficiency, reduced total delay time, and a lower number of stops. The framework successfully balanced traffic distribution between lanes and facilitated smooth merging by dynamically adjusting vehicle behaviors. The study confirms that integrating cooperative lane-changing strategies with trajectory optimization significantly improves operational efficiency and safety in intelligent traffic management systems. The significance of this work lies in its comprehensive approach to merging optimization, combining vehicle-specific collaboration methods with lane-changing strategies in a CAV environment. By providing a structured framework for real-time coordination, the study offers innovative technical support for intelligent transportation systems. It highlights the practical benefits of CAV technology in mitigating congestion at critical infrastructure points, suggesting that such cooperative strategies can be vital for managing high-volume traffic flows and improving overall freeway performance.

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• traffic density