Distributed Self-Organizing Control of CAVs Between Multiple Adjacent-Ramps

Abstract

Traffic self-organizing is controlled autonomously by rules that rely on adaptation to local variations in traffic state and enable effective coordination of the vehicular traffic at a network level. Combined self-organizing network with intelligent transportation, we proposed a distributed self-organizing method for Connected and Autonomous Vehicles (CAVs), which aimed to improve the efficiency and safety between Multiple Adjacent-Ramps (multi-ARs). To make the mainline formation more stable, the speed of ramp vehicles was adjusted to ensure suitable speed and headway of the mainline formation. In the test, the multi-ARs in the East Ring Interchange on the Inner Ring Express in Chongqing was selected to collect the initial data sample by the drones and fixed-point cameras. Under the respective scenarios of conventional driving and intelligent networks, the Python, SUMO, and TraCI were adopted to run simulations and validate the proposed model. Results showed that our model could keep Time to Conflict (TTC) above 1.4s, reduce the average delay by 34.22%, reduce the lane-changing times by 28.07%, reduce single lane occupancy to 8% and improve average speed by 3.68% of multi-ARs. To verify the applicability of the proposed model, experiments were carried out under different traffic volumes, demonstrating the relevance of the proposed method for medium-to-high-density traffic flows. It can provide a basis for traffic engineers and policymakers to maintain the stable development of the urban expressways and ensure the overall operation quality of the multi-ARs.