Long memory is important: A test study on deep-learning based car-following model | |
Wang, Xiao1; Jiang, Rui2; Li, Li3; Lin, Yi-Lun4; Wang, Fei-Yue4 | |
发表期刊 | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS |
ISSN | 0378-4371 |
2019-01-15 | |
卷号 | 514页码:786-795 |
通讯作者 | Jiang, Rui(jiangrui@bjtu.edu.cn) ; Li, Li(li-li@tsinghua.edu.cn) |
摘要 | Whether long memory effect plays an important role in car-following models remains unsolved. In this paper, we study the possible relationship between long memory effect and hysteresis phenomena observed in practice. Especially, we have compared the performance of different deep learning based car-following models that take various time-scale historical information as inputs. Test show that hysteresis phenomena can be correctly simulated only by car-following models with long memory. So, we argue that car-following models should embed long memory effect appropriately. (C) 2018 Elsevier B.V. All rights reserved. |
关键词 | Car-following Long memory Hysteresis Deep learning |
DOI | 10.1016/j.physa.2018.09.136 |
关键词[WOS] | CONGESTED TRAFFIC STATES ; PHASE-TRANSITIONS ; NEURAL-NETWORKS ; FLOW ; CALIBRATION ; HYSTERESIS ; STABILITY ; ALGORITHM ; DIAGRAM ; DELAY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Natural Science Foundation of Beijing Municipality, China[9172013] ; Beijing Municipal Commission of Transport, China[ZC179074Z] ; Beijing Municipal Science and Technology Commission Program, China[D171100000317002] ; National Natural Science Foundation of China[61790565] ; National Natural Science Foundation of China[61790565] ; Beijing Municipal Science and Technology Commission Program, China[D171100000317002] ; Beijing Municipal Commission of Transport, China[ZC179074Z] ; Natural Science Foundation of Beijing Municipality, China[9172013] |
项目资助者 | National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission Program, China ; Beijing Municipal Commission of Transport, China ; Natural Science Foundation of Beijing Municipality, China |
WOS研究方向 | Physics |
WOS类目 | Physics, Multidisciplinary |
WOS记录号 | WOS:000450137000070 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22588 |
专题 | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 复杂系统管理与控制国家重点实验室 |
通讯作者 | Jiang, Rui; Li, Li |
作者单位 | 1.Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China 2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 3.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China 4.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100080, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Xiao,Jiang, Rui,Li, Li,et al. Long memory is important: A test study on deep-learning based car-following model[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,2019,514:786-795. |
APA | Wang, Xiao,Jiang, Rui,Li, Li,Lin, Yi-Lun,&Wang, Fei-Yue.(2019).Long memory is important: A test study on deep-learning based car-following model.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,514,786-795. |
MLA | Wang, Xiao,et al."Long memory is important: A test study on deep-learning based car-following model".PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 514(2019):786-795. |
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