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Predicting passenger satisfaction in public transportation using machine learning models

Author

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  • Ruiz, Elkin
  • Yushimito, Wilfredo F.
  • Aburto, Luis
  • de la Cruz, Rolando

Abstract

Enhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables.

Suggested Citation

  • Ruiz, Elkin & Yushimito, Wilfredo F. & Aburto, Luis & de la Cruz, Rolando, 2024. "Predicting passenger satisfaction in public transportation using machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:transa:v:181:y:2024:i:c:s0965856424000430
    DOI: 10.1016/j.tra.2024.103995
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    References listed on IDEAS

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