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From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

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  • Hamdar, Samer H.
  • Mahmassani, Hani S.
  • Treiber, Martin

Abstract

We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory – prospect theory – into an efficient microscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.

Suggested Citation

  • Hamdar, Samer H. & Mahmassani, Hani S. & Treiber, Martin, 2015. "From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 32-53.
    • Handle: RePEc:eee:transb:v:78:y:2015:i:c:p:32-53
    DOI: 10.1016/j.trb.2015.03.011
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    References listed on IDEAS

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    Cited by:

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    2. Talebpour, Alireza & Mahmassani, Hani S. & Hamdar, Samer H., 2018. "Effect of information availability on stability of traffic flow: Percolation theory approach," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 624-638.
    3. Mohammadian, Saeed & Zheng, Zuduo & Haque, Mazharul & Bhaskar, Ashish, 2023. "NET-RAT: Non-equilibrium traffic model based on risk allostasis theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
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    5. Calvert, Simeon C. & Schakel, Wouter J. & van Lint, J.W.C., 2020. "A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 42-63.
    6. 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.
    7. Punzo, Vincenzo & Montanino, Marcello, 2016. "Speed or spacing? Cumulative variables, and convolution of model errors and time in traffic flow models validation and calibration," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 21-33.
    8. van Lint, J.W.C. & Calvert, S.C., 2018. "A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 63-86.
    9. 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.
    10. Maosheng Li & Jing Fan & Jaeyoung Lee, 2023. "Modeling Car-Following Behavior with Different Acceptable Safety Levels," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    11. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish & Haque, Md. Mazharul, 2019. "Modelling car-following behaviour of connected vehicles with a focus on driver compliance," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 256-279.

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