摘要
Purpose–On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety.The connected and autonomous vehicles(CAVs),with their capabilities of real-time communication and precise motion control,hold a great potential to facilitate ramp merging operation through enhanced coordination strategies.This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs,focusing on the latest trends and developments in the research field.Design/methodology/approach–The review comprehensively covers 44 papers recently published in leading transportation journals.Based on the application context,control strategies are categorized into three categories:merging into sing-lane freeways with total CAVs,merging into singlane freeways with mixed traffic flows and merging into multilane freeways.Findings–Relevant literature is reviewed regarding the required technologies,control decision level,applied methods and impacts on traffic performance.More importantly,the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review,which facilitates further advancement in this research topic.Originality/value–Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades,devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps.Despite the significant progress made,an up-to-date review covering these latest developments is missing to the authors’best knowledge.This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs,focusing on the latest developments in this field.Based on the review,the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.
基金
grateful to VINNOVA(ICV-Safe,2019–03418),Area of Advance Transport and AI Center(CHAIR)at the Chalmers University of Technology for funding this research.
