| 基于守恒高阶模型和支持向量机的多车道车辆换道模型 |
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| 引用本文： | 张立灿,郭明旻,林志阳,张鹏,段雅丽.基于守恒高阶模型和支持向量机的多车道车辆换道模型[J].计算物理,2022,39(1):83-95. |
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| 作者姓名： | 张立灿 郭明旻 林志阳 张鹏 段雅丽 |
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| 作者单位： | 1. 中国科学技术大学数学科学学院, 安徽 合肥 2300262. 复旦大学航空航天系, 上海 2004333. 同济大学经济与管理学院, 上海 2000924. 上海市应用数学和力学研究所, 上海大学力学与工程学院, 上海 200072 |
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| 基金项目： | 国家重点研发计划(2018YFB1600900)；;国家自然科学基金(11972121)； |
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| 摘 要： | 针对高速公路车辆换道问题, 提出一个多车道车辆换道模型。利用支持向量机(SVM)在多维特征下二分类问题的优势, 将SVM和Lagrange坐标下的高阶守恒模型(CHO)结合, 通过全离散跟车模型生成原始数据, 采用SMOTE(Synthetic Minority Oversampling Technique)算法对数据进行预处理, 采用双指标评估度SVM进行训练, 建立多车道车辆换道仿真模型。仿真结果表明: 基于支持向量机和CHO模型的换道模型, 驾驶车能够就当前的驾驶环境, 准确地作出决策, 有效地模拟高速公路上真实的多车道驾驶情况。
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| 关 键 词： | 支持向量机 SOMTE算法 特征选择 CHO模型 多车道交通流 |
| 收稿时间： | 2021-02-02 |
A Lane Changing Model Based on High Order Conservation Model and Support Vector Machine |
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| ZHANG Lican,GUO Mingmin,LIN Zhiyang,ZHANG Peng,DUAN Yali.A Lane Changing Model Based on High Order Conservation Model and Support Vector Machine[J].Chinese Journal of Computational Physics,2022,39(1):83-95. |
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| Authors: | ZHANG Lican GUO Mingmin LIN Zhiyang ZHANG Peng DUAN Yali |
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| Institution: | 1. School of Mathematical Science, University of Science and Technology of China, Hefei, Anhui 230026, China2. Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China3. School of Economics and Management, Tongji University, Shanghai 200092, China4. Shanghai Institute of Applied Mathematics and Mechanics, School Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China |
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| Abstract: | A lane changing model for multi-lane traffic flow is proposed.It makes use of advantages of Support Vector Machine (SVM) in a binary classification problem with multi-dimensional features and combines with Conserved Higher-Order traffic flow model (CHO) in Lagrange coordinates.The original data is generated with a fully discrete car following model and preprocessed by Synthetic Minority Oversampling Technique (SMOTE) algorithm.The SVM is trained with two indexes evaluation.It shows that the lane changing model based on SVM and CHO simulates effectively real multi-lane driving behavior based on current driving environment on expressway. |
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| Keywords: | SVM model SMOTE algorithm feature selection CHO model multi-lane traffic flow |
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