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Car following behavioral stochasticity analysis and modeling: Perspective from wave travel time

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  • Tian, Junfang
  • Zhu, Chenqiang
  • Chen, Danjue
  • Jiang, Rui
  • Wang, Guanying
  • Gao, Ziyou

Abstract

This paper analyzes the car following behavioral stochasticity based on two sets of field experimental trajectory data by measuring the wave travel time series τ˜n(t) of vehicle n. The analysis shows that (i) No matter the speed of leading vehicle oscillates significantly or slightly, τ˜n(t) might change significantly; (ii) A follower's τ˜n(t) can vary from run to run even if the leader travels at the same stable speed; (iii) Sometimes, even if the leader's speed fluctuates significantly, the follower can keep a nearly constant value of τ˜n(t). The Augmented Dickey-Fuller test indicates that the time series ξn(t)=dτ˜n(t)/dt follows a mean reversion process, no matter the oscillations fully developed or not. Based on the finding, a simple stochastic Newell model is proposed. The concave growth pattern of traffic oscillations has been derived analytically. Furthermore, simulation results demonstrate that the new model well captures both macroscopic characteristic of traffic flow evolution and microscopic characteristic of car following.

Suggested Citation

  • Tian, Junfang & Zhu, Chenqiang & Chen, Danjue & Jiang, Rui & Wang, Guanying & Gao, Ziyou, 2021. "Car following behavioral stochasticity analysis and modeling: Perspective from wave travel time," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 160-176.
    • Handle: RePEc:eee:transb:v:143:y:2021:i:c:p:160-176
    DOI: 10.1016/j.trb.2020.11.008
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    6. Haizhen Li & Claudio Roncoli & Weiming Zhao & Yongfeng Ju, 2024. "Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway," Sustainability, MDPI, vol. 16(8), pages 1-23, April.
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