On the driver's stochastic nature in car-following behavior: Modeling and stabilizing based on the V2I environment

Ying Luo; Yanyan Chen; Kaiming Lu; Jian Zhang; Tao Wang; Zhiyan Yi; Ying Luo; Yanyan Chen; Kaiming Lu; Jian Zhang; Tao Wang; Zhiyan Yi

doi:10.3934/era.2023017

Electronic Research Archive

2023, Volume 31, Issue 1: 342-366. doi: 10.3934/era.2023017

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Research article

On the driver's stochastic nature in car-following behavior: Modeling and stabilizing based on the V2I environment

1.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
2.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
3.
Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, UT 84112, United States of America

Received: 22 August 2022 Revised: 17 October 2022 Accepted: 20 October 2022 Published: 02 November 2022

The driver's stochastic nature is one of the important causes of traffic oscillation. To better describe the impact of the driver's stochastic characteristics on car-following behavior, we propose a stochastic full velocity difference model (SFVDM) considering the stochastic variation of the desired velocity. In order to mitigate traffic oscillation caused by driving stochasticity, we further propose a stable speed guidance model (S-SFVDM) by leveraging vehicle-to-infrastructure communication. Stochastic linear stability conditions are derived to demonstrate the prominent influence of the driver's stochasticity on the stability of traffic flow and the improvement of traffic flow stability by the proposed guidance strategy, respectively. We present numerical tests to demonstrate the effectiveness of the proposed models. The results show that the SFVDM can capture the traffic oscillation caused by the driver's stochastic desired velocity and reproduce the same disturbance growth pattern as in the field experiment. The results also indicate that the S-SFVDM can significantly expand the stable area of traffic flow to decrease the negative impact on traffic flow stability caused by the driver's stochastic nature.
- stochastic car-following model,
- stochastic stability analysis,
- V2I environment,
- stable control
Citation: Ying Luo, Yanyan Chen, Kaiming Lu, Jian Zhang, Tao Wang, Zhiyan Yi. On the driver's stochastic nature in car-following behavior: Modeling and stabilizing based on the V2I environment[J]. Electronic Research Archive, 2023, 31(1): 342-366. doi: 10.3934/era.2023017

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Abstract

The driver's stochastic nature is one of the important causes of traffic oscillation. To better describe the impact of the driver's stochastic characteristics on car-following behavior, we propose a stochastic full velocity difference model (SFVDM) considering the stochastic variation of the desired velocity. In order to mitigate traffic oscillation caused by driving stochasticity, we further propose a stable speed guidance model (S-SFVDM) by leveraging vehicle-to-infrastructure communication. Stochastic linear stability conditions are derived to demonstrate the prominent influence of the driver's stochasticity on the stability of traffic flow and the improvement of traffic flow stability by the proposed guidance strategy, respectively. We present numerical tests to demonstrate the effectiveness of the proposed models. The results show that the SFVDM can capture the traffic oscillation caused by the driver's stochastic desired velocity and reproduce the same disturbance growth pattern as in the field experiment. The results also indicate that the S-SFVDM can significantly expand the stable area of traffic flow to decrease the negative impact on traffic flow stability caused by the driver's stochastic nature.

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