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车辆跟驰模型研究进展

杨龙海 张春 仇晓赟 李帅 王晖

杨龙海, 张春, 仇晓赟, 李帅, 王晖. 车辆跟驰模型研究进展[J]. 交通运输工程学报, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013
引用本文: 杨龙海, 张春, 仇晓赟, 李帅, 王晖. 车辆跟驰模型研究进展[J]. 交通运输工程学报, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013
YANG Long-hai, ZHANG Chun, CHOU Xiao-yun, LI Shuai, WANG Hui. Research progress on car-following models[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013
Citation: YANG Long-hai, ZHANG Chun, CHOU Xiao-yun, LI Shuai, WANG Hui. Research progress on car-following models[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 125-138. doi: 10.19818/j.cnki.1671-1637.2019.05.013

车辆跟驰模型研究进展

doi: 10.19818/j.cnki.1671-1637.2019.05.013
基金项目: 

国家自然科学基金项目 71471046

吉林省交通运输厅交通运输科技项目 2017-1-18

详细信息
    作者简介:

    杨龙海(1970-), 男, 安徽巢湖人, 哈尔滨工业大学副教授, 工学博士, 从事交通流理论研究

  • 中图分类号: U491.2

Research progress on car-following models

More Information
  • 摘要: 梳理了近70年关于跟驰模型的研究, 根据建模方法将其分为理论驱动与数据驱动2类模型, 并归纳了跟驰模型的研究热点; 从人类因素、基础设施、交通信息、异质交通流、新建模型理论5个方面对理论驱动类跟驰模型的研究进行了综述; 根据所用机器学习算法的不同, 从模糊逻辑、人工神经网络、实例学习、支持向量回归、深度学习5个方面对数据驱动类跟驰模型的研究进行了综述。分析结果表明: 理论驱动类跟驰模型以理论推演交通现象, 对影响因素的考量难以全面, 部分人类因素难以量化, 驾驶人决策制定过程的解释不够准确, 异质交通流的跟驰模型缺乏一般交通条件下有效性的理论基础和形式化证明; 数据驱动类跟驰模型以交通现象归纳交通规律, 由于数据的来源、评价指标及评价方法不同, 导致应用机器学习算法得到的模型无法系统比较; 数据驱动类模型侧重于从微观角度研究驾驶行为特性, 对复杂交通现象(如交通震荡、迟滞等)的解释性不强; 跟驰模型的研究应创新数据采集方法, 捕捉驾驶人的心理倾向、感知特性和认知能力, 并量化人类因素的影响和充分利用大数据; 数据驱动类跟驰模型应为无人驾驶技术发展提供技术支持; 在自动驾驶完全普及之前, 人工驾驶与自动驾驶混合场景下的驾驶人跟驰行为特性尚待深入研究。

     

  • 图  1  跟驰模型发展历程

    Figure  1.  Development process of car-following models

    图  2  跟驰模型研究方向分类

    Figure  2.  Research direction classifications of car-following models

    图  3  减速过程

    Figure  3.  Process of deceleration

    图  4  加速过程

    Figure  4.  Process of acceleration

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    LUO Ying, QIN Wen-hu. Combination low-speed car-following model based on IDM and RBFNN[J]. Application Research of Computers, 2019, 37(8): 1-7. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ202008023.htm
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  • 收稿日期:  2019-04-23
  • 刊出日期:  2019-10-25

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