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Machine learning and Geographic Information Systems (GIS)-based model for road crash severity prediction and

Lara A Al-Mashagba1, Putra Sumari1, Mohammadnour Mashagba2

  • 1School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia.

Traffic Injury Prevention
|February 18, 2026
PubMed
Summary

This study uses machine learning and GIS to predict road crash severity in Jordan. Nighttime driving and young drivers are linked to fatal crashes, with high-risk areas identified.

Keywords:
Machine learningRandom Forestassociation rule miningroad traffic crashesseverity prediction

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Area of Science:

  • Public Health
  • Data Science
  • Transportation Engineering

Background:

  • Road traffic injuries are a significant public health issue in low- and middle-income countries.
  • Jordan faces challenges with road safety, necessitating advanced analytical approaches.

Purpose of the Study:

  • To develop a hybrid machine learning (ML) and Geographic Information Systems (GIS) framework for predicting road crash severity.
  • To identify behavioral, environmental, and infrastructural factors contributing to injury outcomes in Jordan.

Main Methods:

  • Analysis of 11,345 crashes using a two-stage approach: association rule mining and ML models (Decision Tree, Random Forest, AdaBoost).
  • GIS-based kernel density estimation to identify spatial hotspots of severe and fatal crashes.
  • Data preprocessing included encoding, imputation, normalization, and outlier treatment.

Main Results:

  • Association rules identified links between nighttime driving, young drivers, and non-seatbelt use with fatal outcomes.
  • GIS analysis pinpointed high-risk crash clusters in Zarqa, Awajan, and Al-Rusayfah.
  • The Random Forest model demonstrated high predictive accuracy (98.5-99.9%), with key predictors including crash type, speed, time of day, driver age, and road characteristics.

Conclusions:

  • The hybrid ML-GIS framework provides accurate crash severity prediction and reveals critical spatial and behavioral patterns.
  • Findings emphasize the significant influence of temporal and infrastructural factors, guiding targeted safety interventions and evidence-based strategies in Jordan.