Fundamental diagram of mixed traffic flow of CAVs with different connectivity and automation levels

被引:1
作者
Jiang, Yangsheng [1 ,2 ,3 ]
Chen, Hongyu [1 ]
Cong, Hongwei [1 ]
Wu, Yunxia [1 ]
Yao, Zhihong [1 ,2 ,3 ]
机构
[1] Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu 610031, Peoples R China
[2] Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data Appl, Chengdu 610031, Peoples R China
[3] Southwest Jiaotong Univ, Natl United Engn Lab Integrated & Intelligent Tran, Chengdu 610031, Peoples R China
基金
中国国家自然科学基金;
关键词
Fundamental diagram; Automation levels; Mixed traffic flow; Connected automated vehicles; Traffic capacity; Penetration rates; CELL TRANSMISSION MODEL; ADAPTIVE CRUISE CONTROL; AUTONOMOUS VEHICLES; IMPACT; STABILITY; BENEFITS; DESIGN;
D O I
10.1016/j.physa.2024.129904
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This paper proposes the fundamental diagram model for a mixed traffic flow environment that considers different car-following modes under various levels of automation and connectivity. First, the SAE classification of vehicle automation levels, from L0 to L5, has been further divided into connectivity and automation levels. Second, the car-following modes that affect carfollowing characteristics are analyzed in mixed traffic flow with different levels of connectivity and automation. Different car-following models are used to describe the car-following characteristics of different types of modes. Third, the fundamental diagram model of mixed traffic flow is further derived based on the different levels of automation and connectivity. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates (PRs) of the car-following modes on the traffic capacity of the mixed traffic flow. The results show that (1) with a higher PR of the automation level of the vehicle, the greater the traffic capacity. At the same time, as the automation levels increase, the change in the penetration rate of high automation levels has a more and more significant impact on traffic capacity. (2) Traffic capacity increases with increased PR of higher automation levels, representing an increase of approximately 11.1 % (3) Compared with connectivity, the PR of automation levels significantly impacts traffic capacity, which is approximately 52.6 %.
引用
收藏
页数:17
相关论文
共 68 条
  • [31] Impact of cooperative adaptive cruise control on multilane freeway merge capacity
    Liu, Hao
    Kan, Xingan
    Shladover, Steven E.
    Lu, Xiao-Yun
    Ferlis, Robert E.
    [J]. JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 22 (03) : 263 - 275
  • [32] Connected Vehicles: Solutions and Challenges
    Lu, Ning
    Cheng, Nan
    Zhang, Ning
    Shen, Xuemin
    Mark, Jon W.
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2014, 1 (04): : 289 - 299
  • [33] Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data
    Milanes, Vicente
    Shladover, Steven E.
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 48 : 285 - 300
  • [34] Cooperative Adaptive Cruise Control in Real Traffic Situations
    Milanes, Vicente
    Shladover, Steven E.
    Spring, John
    Nowakowski, Christopher
    Kawazoe, Hiroshi
    Nakamura, Masahide
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2014, 15 (01) : 296 - 305
  • [35] Studying Traffic Safety During the Transition Period Between Manual Driving and Autonomous Driving: A Simulation-Based Approach
    Miqdady, Tasneem
    de Ona, Rocio
    Casas, Jordi
    de Ona, Juan
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (06) : 6690 - 6710
  • [36] Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management
    Mohajerpoor, Reza
    Ramezani, Mohsen
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 109 : 194 - 210
  • [37] Secondary task engagement and vehicle automation - Comparing the effects of different automation levels in an on-road experiment
    Naujoks, Frederik
    Purucker, Christian
    Neukum, Alexandra
    [J]. TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2016, 38 : 67 - 82
  • [38] Evaluating the safety impact of connected and autonomous vehicles on motorways
    Papadoulis, Alkis
    Quddus, Mohammed
    Imprialou, Marianna
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2019, 124 : 12 - 22
  • [39] Safety benefits of arterials' crash risk under connected and automated vehicles
    Rahman, Md Sharikur
    Abdel-Aty, Mohamed
    Lee, Jaeyoung
    Rahman, Md Hasibur
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 100 : 354 - 371
  • [40] Fundamental diagram estimation by using trajectories of probe vehicles
    Seo, Toru
    Kawasaki, Yutaka
    Kusakabe, Takahiko
    Asakura, Yasuo
    [J]. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2019, 122 : 40 - 56