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Latent class model for car following behavior

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  • Koutsopoulos, Haris N.
  • Farah, Haneen

Abstract

Car-following behavior, which describes the behavior of a vehicle while following the vehicle in front of it, has a significant impact on traffic performance, safety, and air pollution. In addition, car-following is an essential component of micro-simulation models. Over the last decade the use of microscopic simulation models as a tool for investigating traffic systems, ITS applications, and emission impacts, is becoming increasingly popular. The paper presents a flexible framework for modeling car-following behavior that relaxes some limitations and assumptions of the most commonly used car following models. The proposed approach recognizes different regimes in driving such as car-following, free-flow, emergency stopping, and incorporates different decisions in each regime, such as acceleration, deceleration, and do-nothing depending on the situation. A case study using NGSIM vehicle trajectory data is used to illustrate the proposed model structure. Statistical tests suggest that the model performs better than previous models.

Suggested Citation

  • Koutsopoulos, Haris N. & Farah, Haneen, 2012. "Latent class model for car following behavior," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 563-578.
    • Handle: RePEc:eee:transb:v:46:y:2012:i:5:p:563-578
    DOI: 10.1016/j.trb.2012.01.001
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    References listed on IDEAS

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    Cited by:

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    2. Hurtubia, Ricardo & Nguyen, My Hang & Glerum, Aurélie & Bierlaire, Michel, 2014. "Integrating psychometric indicators in latent class choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 135-146.
    3. Kai Yuan & Victor L. Knoop & Serge P. Hoogendoorn, 2017. "A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate," Transportation Science, INFORMS, vol. 51(3), pages 852-862, August.
    4. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    5. Sheu, Jiuh-Biing & Wu, Hsi-Jen, 2015. "Driver perception uncertainty in perceived relative speed and reaction time in car following – A quantum optical flow perspective," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 257-274.
    6. Silvano, Ary P. & Koutsopoulos, Haris N. & Farah, Haneen, 2020. "Free flow speed estimation: A probabilistic, latent approach. Impact of speed limit changes and road characteristics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 283-298.
    7. Varotto, Silvia F. & Farah, Haneen & Toledo, Tomer & van Arem, Bart & Hoogendoorn, Serge P., 2018. "Modelling decisions of control transitions and target speed regulations in full-range Adaptive Cruise Control based on Risk Allostasis Theory," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 318-341.

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