Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)

Considering prevision driving behavior in car-following model

Authors
Junjun Hu, Yu Zhang, Ruiyu Zhao
Corresponding Author
Junjun Hu
Available Online December 2016.
DOI
10.2991/icsma-16.2016.73How to use a DOI?
Keywords
traffic flow; car-following model; prevision driving behavior; traffic jam
Abstract

In the paper, we develop a new car-following model considering the prevision driving behavior on a single-lane road. The model's linear stability condition is obtained by applying the linear stability theory. And through nonlinear analysis, a modified Korteweg-de Vries (mKdV) equation is derived to describe the propagating behavior of traffic density wave near the critical point. Numerical simulation shows that the new model can improve the stability of traffic flow by adjusting the driver's prevision intensity parameter, which is consistent with the theoretical analysis.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
Series
Advances in Intelligent Systems Research
Publication Date
December 2016
ISBN
978-94-6252-274-9
ISSN
1951-6851
DOI
10.2991/icsma-16.2016.73How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Junjun Hu
AU  - Yu Zhang
AU  - Ruiyu Zhao
PY  - 2016/12
DA  - 2016/12
TI  - Considering prevision driving behavior in car-following model
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 409
EP  - 412
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.73
DO  - 10.2991/icsma-16.2016.73
ID  - Hu2016/12
ER  -