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Transport behavior and government interventions in pandemics: A hybrid explainable machine learning for road safety

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  • Abdulrashid, Ismail
  • Zanjirani Farahani, Reza
  • Mammadov, Shamkhal
  • Khalafalla, Mohamed

Abstract

During a pandemic, transportation authorities and policymakers face significant challenges in identifying and validating new travel behavior and how it affects traffic crash patterns to develop effective safety strategies. A timely assessment of an emergency incident’s long-term impact and the development of appropriate response strategies are critical for managing future occurrences. This study investigates to answer these research questions (RQs):

Suggested Citation

  • Abdulrashid, Ismail & Zanjirani Farahani, Reza & Mammadov, Shamkhal & Khalafalla, Mohamed, 2025. "Transport behavior and government interventions in pandemics: A hybrid explainable machine learning for road safety," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:transe:v:193:y:2025:i:c:s1366554524004320
    DOI: 10.1016/j.tre.2024.103841
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    1. Abdulrashid, Ismail & Chiang, Wen-Chyuan & Sheu, Jiuh-Biing & Mammadov, Shamkhal, 2025. "An interpretable machine learning framework for enhancing road transportation safety," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
    2. Wang, Wenyang & Luo, Yuping & Jiang, Zihan & Zhou, Jibin & Jia, Peng, 2025. "A deep learning framework for global transportation energy carbon emission forecasting: integrating generative pre-trained transformer with multi-scale feature analysis," Energy, Elsevier, vol. 338(C).

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