Penetration effect of connected and automated vehicles on cooperative on-ramp merging

Abstract

Earlier work has established a centralised cooperative merging framework of optimally coordinating two strings of connected and automated vehicles (CAVs) passing through the on-ramp merging zone. The proposed merging strategy is capable of making a good trade-off between performance and computational cost. In this study, the authors address the problem of optimally coordinating CAVs under mixed traffic conditions, where both CAVs and human-driven vehicles (non-CAVs) travel on the roads, so as to enhance efficiency while guaranteeing safety constraints. A hierarchical cooperative merging framework is proposed for CAVs, which integrates merging sequence scheduling strategies (high level) and motion planning methods (low level). The impacts of CAV penetration (i.e. The fraction of CAVs relative to all vehicles) on throughput, delay, fuel consumption and emission are also investigated under different traffic demands. Simulation-based case studies indicate that the performance improvement becomes more significant as the CAV penetration rate increases and about 30% CAV penetration can effectively mitigate the shockwave and reduce traffic congestion.