Vehicle-following model in mixed traffic flow considering interaction potential of multiple front vehicles

ZONG Fang; WANG Meng; ZENG Meng; SHI Pei-xin; WANG Li

doi:10.19818/j.cnki.1671-1637.2022.01.021

Volume 22 Issue 1

Feb. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(1): 250-262

ZONG Fang, WANG Meng, ZENG Meng, SHI Pei-xin, WANG Li. Vehicle-following model in mixed traffic flow considering interaction potential of multiple front vehicles[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 250-262. doi: 10.19818/j.cnki.1671-1637.2022.01.021

Citation:

ZONG Fang, WANG Meng, ZENG Meng, SHI Pei-xin, WANG Li. Vehicle-following model in mixed traffic flow considering interaction potential of multiple front vehicles[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 250-262. doi: 10.19818/j.cnki.1671-1637.2022.01.021

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Vehicle-following model in mixed traffic flow considering interaction potential of multiple front vehicles

doi: 10.19818/j.cnki.1671-1637.2022.01.021

ZONG Fang^1
,,
WANG Meng¹,
ZENG Meng²,
SHI Pei-xin¹,
WANG Li^{3
,
,}

1.
College of Transportation, Jilin University, Changchun 130022, Jilin, China
2.
College of Engineering, Zhejiang Normal University, Jinhua 321004, Zhejiang, China
3.
Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China

Funds:

National Key Research and Development Program of China 2018YFB1600500

National Natural Science Foundation of China 61873109

More Information

Author Bio:
ZONG Fang(1971-), female, professor, PhD, zongfang@jlu.edu.cn

WANG Li(1978-), male, professor, PhD, Li.wang@ncut.edu.cn
Received Date: 2021-08-01
Publish Date: 2022-02-25

Abstract

Abstract

The mixed traffic flow consisting of automated vehicle (AV) and regular vehicle (RV) was analyzed. Based on the full velocity difference (FVD) model, a vehicle-following model for two types of vehicles (AV and RV) in mixed traffic flow was constructed by considering the factors of the headway, velocity, velocity difference and acceleration difference of multiple front vehicles and one rear vehicle. By introducing the molecular dynamics, the model also quantitatively expressed the influence degree of a surrounding vehicle on the host vehicle. According to the data collected from the vehicle-following field test mixed with AVs and RVs, the model parameters were globally optimized to obtain the highest accuracy. The stability of traffic flow for the vehicle-following model and FVD model was compared, and the influence of velocity on the stability of traffic flow was analyzed. Numerical simulation was designed to simulate the common traffic scenarios including urban areas and expressways, and the accuracy of the proposed model was analyzed. Simulation results indicate that the stability of traffic flow improves by considering the information from surrounding multiple vehicles, and the small velocity can reduce the stability. The proposed model can respond to the behaviours of the whole platoon in advance and simulate the dynamics characteristics of AVs better. In urban areas, compared with the FVD model, the average maximum error and average error of RV for the proposed model reduce by 0.18 m · s^-1 and 13.12%, respectively, and the accuracy improves by 4.47%. In expressways, compared with the adaptive cruise control (ACC) model provided by PATH Laboratory, the average maximum error and average error of AV for the proposed model reduce by 7.78% and 26.79%, respectively, and the accuracy improves by 1.12%. In addition to providing model basis for AV-following control and queue control in mixed traffic flow, the proposed model can be utilized in vehicle-following behavior simulation for AV and RV. 1 tab, 7 figs, 38 refs.
- traffic control,
- mixed traffic flow,
- vehicle-following model,
- traffic simulation,
- automated vehicle,
- regular vehicle

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References(38)

References

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Vehicle-following model in mixed traffic flow considering interaction potential of multiple front vehicles

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