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Revisiting the Task–Capability Interface model for incorporating human factors into car-following models

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  • Saifuzzaman, Mohammad
  • Zheng, Zuduo
  • Mazharul Haque, Md.
  • Washington, Simon

Abstract

Human factors such as distraction, fatigue, alcohol and drug use are generally ignored in car-following (CF) models. Such ignorance overestimates driver capability and leads to most CF models’ inability in realistically explaining human driving behaviors. This paper proposes a novel car-following modeling framework by introducing the difficulty of driving task measured as the dynamic interaction between driving task demand and driver capability. Task difficulty is formulated based on the famous Task–Capability Interface (TCI) model, which explains the motivations behind driver's decision making. The proposed method is applied to enhance two popular CF models: Gipps’ model and IDM, and named as TDGipps and TDIDM respectively. The behavioral soundness of TDGipps and TDIDM are discussed and their stabilities are analyzed. Moreover, the enhanced models are calibrated with the vehicle trajectory data, and validated to explain both regular and human factor influenced CF behavior (which is distraction caused by hand-held mobile phone conversation in this paper). Both the models show better performance than their predecessors, especially in presence of human factors.

Suggested Citation

  • Saifuzzaman, Mohammad & Zheng, Zuduo & Mazharul Haque, Md. & Washington, Simon, 2015. "Revisiting the Task–Capability Interface model for incorporating human factors into car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 1-19.
    • Handle: RePEc:eee:transb:v:82:y:2015:i:c:p:1-19
    DOI: 10.1016/j.trb.2015.09.011
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    10. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish, 2019. "Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 49-75.
    11. 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.
    12. 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.
    13. 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.
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    15. 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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