| 引用本文: | 秦严严,胡兴华,李淑庆,何兆益,许明涛.智能网联环境下混合交通流稳定性解析[J].哈尔滨工业大学学报,2021,53(3):152.DOI:10.11918/201907172 |
| QIN Yanyan,HU Xinghua,LI Shuqing,HE Zhaoyi,XU Mingtao.Stability analysis of mixed traffic flow in connected and autonomous environment[J].Journal of Harbin Institute of Technology,2021,53(3):152.DOI:10.11918/201907172 |
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| 摘要: |
| 为分析由智能网联车辆（connected and autonomous vehicles, CAV）与人工驾驶车辆构成的混合交通流稳定性能,提出一种理论解析方法. 应用泰勒公式对跟驰模型进行线性化处理,并应用传递函数理论推导不同CAV比例下的混合交通流稳定性判别条件,针对CAV前车加速度反馈系数进行参数敏感性分析. 考虑开放性边界条件下的小扰动传播特性,设计混合交通流稳定性的数值仿真实验. 结果表明:CAV不稳定的速度范围在人工驾驶车辆不稳定的速度范围以内；CAV比例的增加有利于将交通流从不稳定状态转变为稳定状态；CAV前车加速度反馈系数越大,混合交通流关于CAV比例与平衡态速度的稳定域越大,在CAV比例达到23%时,混合交通流可在全速度范围内稳定. 研究成果可理论计算CAV混合交通流稳定域,可为该混合交通流关于CAV比例与平衡态速度的稳定性分析提供依据. |
| 关键词: 交通流 稳定性分析 智能网联车辆 跟驰模型 数值仿真 |
| DOI：10.11918/201907172 |
| 分类号:U491.112 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFB1601000)；重庆市技术创新与应用示范专项重点示范项目(cstc2018jscx-mszdX0112)；河南省重点研发与推广专项(科技攻关)项目(192102310470)；中国博士后科学基金面上项目(2018M642790) |
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| Stability analysis of mixed traffic flow in connected and autonomous environment |
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QIN Yanyan1,HU Xinghua1,LI Shuqing1,HE Zhaoyi1,XU Mingtao2
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(1. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2.School of Civil Engineering, Zhengzhou University, Zhengzhou 470001, China)
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| Abstract: |
| To analyze the stability of traffic flow mixed with manned vehicles as well as connected and autonomous vehicles (CAV), a theory analysis method was proposed. The car-following models were linearized by using Taylor formula. The transfer function theory was used to derive the stability criterion of mixed traffic flow with different CAV proportions. Meanwhile, parametric sensitivity analysis was conducted on acceleration feedback coefficient of CAV. Considering the propagation characteristics of small disturbances under open boundary conditions, numerical simulation experiments on the stability of mixed traffic flow were designed. Research results show that the unstable speed range of CAV was within the unstable speed range of manned vehicles. The increase of CAV proportion was helpful to transform the traffic flow from unstable state to stable state. The larger the acceleration feedback coefficient of CAV was, the larger the stability regions of the mixed traffic flow with respect to CAV proportion and equilibrium speed would be. When the CAV proportion increased to 23%, the mixed traffic flow was stable within full equilibrium speeds. The research findings can theoretically calculate stability regions of CAV mixed traffic flow, and can provide evidence for analyzing stability of mixed traffic flow from the perspectives of CAV proportions and equilibrium speeds. |
| Key words: traffic flow stability analysis connected and autonomous vehicles car-following model numerical simulation |
