Capturing Car-Following Behaviors by Deep Learning
Wang, Xiao1; Jiang, Rui2; Li, Li3; Lin, Yilun4; Zheng, Xinhu5; Wang, Fei-Yue4,6
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2018-03-01
Volume19Issue:3Pages:910-920
SubtypeArticle
Abstract

In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers' actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

KeywordMicroscopic Car-following Model Deep Learning Recurrent Neural Network (Rnn) Gated Recurrent Unit (Gru) Neural Networks
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TITS.2017.2706963
WOS KeywordINTELLIGENT TRANSPORTATION SYSTEMS ; TRAFFIC FLOW MODELS ; SHORT-TERM-MEMORY ; NEURAL-NETWORKS ; ARCHITECTURES ; CALIBRATION ; STABILITY ; ALGORITHM ; FRAMEWORK ; DESIGN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91520301 ; National Key R&D Program in China(2016YFB0100906) ; 71621001)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000427222600021
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21981
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorLi, Li
Affiliation1.Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Shaanxi, Peoples R China
2.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
3.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
5.Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55414 USA
6.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Wang, Xiao,Jiang, Rui,Li, Li,et al. Capturing Car-Following Behaviors by Deep Learning[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2018,19(3):910-920.
APA Wang, Xiao,Jiang, Rui,Li, Li,Lin, Yilun,Zheng, Xinhu,&Wang, Fei-Yue.(2018).Capturing Car-Following Behaviors by Deep Learning.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,19(3),910-920.
MLA Wang, Xiao,et al."Capturing Car-Following Behaviors by Deep Learning".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 19.3(2018):910-920.
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