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

Ismail Abdulrashid, Reza Zanjirani Farahani, Shamkhal Mammadov and Mohamed Khalafalla
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Ismail Abdulrashid: University of Tulsa
Reza Zanjirani Farahani: AID - AI Driven Business - Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business
Shamkhal Mammadov: McDougall School of Petroleum Engineering, The University of Tulsa
Mohamed Khalafalla: Florida A&M University

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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): RQ1: How do various spatio-temporal risk factors influence traffic crash injury severity during the different phases of the COVID-19 pandemic? RQ2: What are the key risk factors influencing injury severity in automobile crashes during the pre-pandemic, early pandemic, between the first and second waves of the pandemic, and the postpandemic era? RQ3: How do the implemented government policies and interventions during the pandemic affect transport behavior and road safety? This study presents a hybrid explainable machine learning approach based on eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to identify influential traffic crash-related risk factors for injury severity. Additionally, we propose a statistical learning approach using a nonlinear multinomial logit model to jointly analyze the count of automobile traffic crashes by injury severity and assess the impact of the COVID-19 pandemic across different phases. Our findings include a detailed analysis of system-level taxonomies across feature components, as well as the use of aggregate SHAP scores to classify crash data into high-level contributing variables during the pre-pandemic, intra-pandemic, and post-pandemic phases. The expected outcomes include insights such as identifying the best times to implement travel restrictions to reduce traffic accidents, understanding shifts in traffic flow patterns across pandemic phases, and determining effective public health interventions that can reduce both traffic accidents and congestion. Furthermore, the study reveals that the initial pandemic phase saw a significant decrease in traffic volume and accident rates. In contrast, the subsequent pandemic and post-pandemic phases saw an increase in severe accidents due to risky driving behaviors, emphasizing the importance of adaptive safety measures.

Keywords: Travel behavior; Pandemic; Machine learning; Road safety; Transport planning; Government intervention (search for similar items in EconPapers)
Date: 2025-01
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Published in Transportation Research Part E: Logistics and Transportation Review, 2025, 193, pp.103841. ⟨10.1016/j.tre.2024.103841⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04765768

DOI: 10.1016/j.tre.2024.103841

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