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February 21, 2026Traffic Injury Prevention1 citations

Machine learning and Geographic Information Systems (GIS)–based model for road crash severity prediction and behavioural pattern analysis

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LALara A. Al-MashagbaPSPutra SumariMMMohammadnour Mashagba

Key Points

  • The study aims to develop a hybrid machine learning and GIS framework for predicting road crash severity and analyzing related behaviors.
  • Developed a machine learning and GIS-based hybrid framework.
  • Analyzed spatial and behavioral patterns related to road crashes.
  • Examined the impact of temporal and infrastructural factors.
  • Achieved highly accurate predictions of crash severity.
  • Identified significant influence of temporal and infrastructural factors over driver characteristics.
  • Provided insights for evidence-based engineering and enforcement strategies.

Abstract

The proposed hybrid ML-GIS framework offers highly accurate severity prediction and reveals spatial and behavioral patterns critical for targeted safety interventions. Findings highlight the dominant influence of temporal and infrastructural factors over driver characteristics, supporting evidence-based engineering and enforcement strategies in Jordan.

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Cite This Study

Al-Mashagba et al. (2026) studied this question.

synapsesocial.com/papers/69994ba9873532290d01fbbahttps://doi.org/10.1080/15389588.2025.2610435
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