Mitigating traffic oscillation through control of connected automated vehicles: A cellular automata simulation

被引:0
|
作者
Wang, Yi [1 ,2 ]
Jiang, Yangsheng [1 ,2 ,3 ]
Wu, Yunxia [2 ,3 ]
Yao, Zhihong [1 ,2 ,3 ]
机构
[1] Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu 610031, Sichuan, Peoples R China
[2] Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data Appl, Chengdu 611756, Sichuan, Peoples R China
[3] Southwest Jiaotong Univ, Natl United Engn Lab Integrated & Intelligent Tra, Chengdu 611756, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Mixed traffic flow; Traffic oscillation; Connected automated vehicles; Cellular automata model; Platoon; AUTONOMOUS VEHICLES; HUMAN-DRIVEN; MODEL; FLOW; SYSTEM;
D O I
10.1016/j.eswa.2023.121275
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the advent and advancement of connected and automated vehicle technologies, it is now possible to control connected and automated vehicles (CAVs) in mixed traffic flows to reduce traffic oscillations and enhance traffic flow performance. Recently, a control strategy for autonomous vehicles (AVs) named "The Follower Stopper controller" (FS) has been proposed. However, the existing studies for the FS strategy only considered the situation that all CAVs execute the FS strategy individually. And the impact on the mixed traffic flow when CAVs in the mixed traffic flow execute FS strategies as platoons is not investigated. To address this limitation, this paper proposes a control strategy for CAVs that considers the driving behavior of CAVs platoons based on the FS strategy, which allows CAVs to execute the FS strategy as a platoon. Then, a cellular automaton model of mixed traffic flow is developed based on the proposed strategy. Finally, a numerical simulation is conducted to analyze the impacts of the proposed strategy on traffic efficiency, traffic congestion, traffic oscillation, vehicle fuel consumption, and pollutant emissions under different penetration rates (PRs) of CAVs and traffic densities. Moreover, a comparative analysis with the FS strategy was conducted to verify the effectiveness and superiority of the proposed strategy. The result shows that (1) the proposed strategy can effectively mitigate traffic oscillations and reduce congestion. The improvement effect of the proposed control strategy on the mixed traffic flow is significantly affected by the penetration rate of CAVs as well as the traffic density. (2) Compared with the FS strategy, the proposed strategy has better performance in improving traffic efficiency, alleviating traffic oscillations, relieving congestion, and reducing fuel consumption and pollutant emissions under different traffic conditions. (3) The effectiveness of the FS strategy in improving the performance of mixed traffic flow is greatly limited by traffic density. There is a significant marginal utility of the FS strategy in improving traffic flow performance under high traffic density. However, the proposed strategy overcomes the limitations imposed by high traffic densities. The proposed strategy is highly adaptable to diverse traffic densities and can continuously enhance traffic flow performance as the penetration rate of CAVs rises.
引用
收藏
页数:17
相关论文
共 50 条
  • [21] A multi-scale control framework for urban traffic control with connected and automated vehicles
    Guo, Qiangqiang
    Ban, Xuegang
    [J]. TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2023, 175
  • [22] Oscillation growth in mixed traffic flow of human driven vehicles and automated vehicles: Experimental study and simulation
    Zheng, Shiteng
    Jiang, Rui
    Zhang, H. M.
    Tian, Junfang
    Yan, Ruidong
    Jia, Bin
    Gao, Ziyou
    [J]. TRAFFIC AND GRANULAR FLOW 2022, TGF 2022, 2024, 443 : 267 - 274
  • [23] Participatory traffic control: Leveraging connected and automated vehicles to enhance network efficiency
    Wu, Minghui
    Wang, Ben
    Yin, Yafeng
    Lynch, Jerome P.
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2024, 166
  • [24] A Distributed Platoon Control Framework for Connected Automated Vehicles in an Urban Traffic Network
    Wang, Bohui
    Su, Rong
    [J]. IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2022, 9 (04): : 1717 - 1730
  • [25] Editorial: Connected and automated vehicles (CAV) based traffic-vehicle control
    Ban, Xuegang
    Yang, Diange
    Wang, Junmin
    Hamdar, Samer
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 112 : 116 - 119
  • [26] Numerical Investigation of Traffic State Reconstruction and Control Using Connected Automated Vehicles
    Cicic, Mladen
    Barreau, Matthieu
    Johansson, Karl Henrik
    [J]. 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,
  • [27] Hierarchical Longitudinal Control for Connected and Automated Vehicles in Mixed Traffic on a Signalized Arterial
    Xiao, Xiao
    Zhang, Yunlong
    Wang, Xiubin Bruce
    Yang, Shu
    Chen, Tianyi
    [J]. SUSTAINABILITY, 2021, 13 (16)
  • [28] Delay-Compensated Distributed PDE Control of Traffic With Connected/Automated Vehicles
    Qi, Jie
    Mo, Shurong
    Krstic, Miroslav
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (04) : 2229 - 2244
  • [29] Traffic Signal Control with Connected Vehicles
    Goodall, Noah J.
    Smith, Brian L.
    Park, Byungkyu
    [J]. TRANSPORTATION RESEARCH RECORD, 2013, (2381) : 65 - 72
  • [30] Quantifying a cellular automata simulation of electric vehicles
    Hill, Graeme
    Bell, Margaret
    Blythe, Phil
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 416 : 421 - 429