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MOHA : A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors. / Lin, Qin; Zhang, Yihuan; Verwer, Sicco; Wang, Jun.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 2, 2019, p. 790-796.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Lin, Q, Zhang, Y, Verwer, S & Wang, J 2019, 'MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors', IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 2, pp. 790-796. https://doi.org/10.1109/TITS.2018.2823418

APA

Lin, Q., Zhang, Y., Verwer, S., & Wang, J. (2019). MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors. IEEE Transactions on Intelligent Transportation Systems, 20(2), 790-796. https://doi.org/10.1109/TITS.2018.2823418

Vancouver

Lin Q, Zhang Y, Verwer S, Wang J. MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors. IEEE Transactions on Intelligent Transportation Systems. 2019;20(2):790-796. https://doi.org/10.1109/TITS.2018.2823418

Author

Lin, Qin ; Zhang, Yihuan ; Verwer, Sicco ; Wang, Jun. / MOHA : A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors. In: IEEE Transactions on Intelligent Transportation Systems. 2019 ; Vol. 20, No. 2. pp. 790-796.

BibTeX

@article{dc18fedd0b164d10ab7906f977756d02,
title = "MOHA: A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors",
abstract = "This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.",
keywords = "car-following behavior, Computational modeling, Data mining, Data models, Hybrid automaton, Learning automata, Numerical models, simulation and control., Time series analysis, Vehicles",
author = "Qin Lin and Yihuan Zhang and Sicco Verwer and Jun Wang",
note = "Accepted author manuscript",
year = "2019",
doi = "10.1109/TITS.2018.2823418",
language = "English",
volume = "20",
pages = "790--796",
journal = "IEEE Transactions on Intelligent Transportation Systems",
issn = "1524-9050",
publisher = "IEEE",
number = "2",

}

RIS

TY - JOUR

T1 - MOHA

T2 - A Multi-Mode Hybrid Automaton Model for Learning Car-Following Behaviors

AU - Lin, Qin

AU - Zhang, Yihuan

AU - Verwer, Sicco

AU - Wang, Jun

N1 - Accepted author manuscript

PY - 2019

Y1 - 2019

N2 - This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.

AB - This paper proposes a novel hybrid model for learning discrete and continuous dynamics of car-following behaviors. Multiple modes representing driving patterns are identified by partitioning the model into groups of states. The model is visualizable and interpretable for car-following behavior recognition, traffic simulation, and human-like cruise control. The experimental results using the next generation simulation datasets demonstrate its superior fitting accuracy over conventional models.

KW - car-following behavior

KW - Computational modeling

KW - Data mining

KW - Data models

KW - Hybrid automaton

KW - Learning automata

KW - Numerical models

KW - simulation and control.

KW - Time series analysis

KW - Vehicles

UR - http://www.scopus.com/inward/record.url?scp=85048553144&partnerID=8YFLogxK

U2 - 10.1109/TITS.2018.2823418

DO - 10.1109/TITS.2018.2823418

M3 - Article

AN - SCOPUS:85048553144

VL - 20

SP - 790

EP - 796

JO - IEEE Transactions on Intelligent Transportation Systems

JF - IEEE Transactions on Intelligent Transportation Systems

SN - 1524-9050

IS - 2

ER -

ID: 45517217