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Learning-based methods for spatial road safety analysis using in-vehicle telematics data: A systematic review.

Simone Paradiso1, Apostolos Ziakopoulos1, George Yannis1

  • 1National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, Greece.

Journal of Safety Research
|June 15, 2026
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Summary
This summary is machine-generated.

Artificial intelligence and telematics data are transforming road safety research. Integrating these technologies with spatial analysis enhances safety assessments and enables behavior-tailored interventions for safer mobility.

Keywords:
Artificial intelligenceIn-vehicle telematics dataLearning-based methodsRoad safetySpatial analysis

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Area of Science:

  • Road safety research
  • Spatial analysis
  • Artificial Intelligence (AI)

Background:

  • Traditional road safety monitoring relies on statistical methods.
  • In-vehicle telematics data and AI offer new possibilities for spatial analysis in road safety.
  • Telematics-informed spatial scales move beyond traditional geometric analyses.

Purpose of the Study:

  • To conduct a structured literature review on learning-based methods, spatial analysis, and surrogate safety measures from telematics data.
  • To analyze data collection, feature engineering, and spatial scale selection in existing studies.
  • To synthesize findings on methodological challenges and advancements in AI for road safety.

Main Methods:

  • Systematic literature review following PRISMA guidelines.
  • Identification and analysis of 44 relevant studies.
  • Narrative synthesis focusing on data, features, spatial scale, and methodologies (from econometrics to deep learning).

Main Results:

  • Methodological challenges include the interplay between data sources, spatial scale, and analytical frameworks.
  • Key features and their importance in telematics-based safety analysis were identified.
  • Recent deep learning methodologies offer significant advantages for road safety analysis.

Conclusions:

  • AI and telematics data are revolutionizing road safety research and generating actionable insights.
  • Clarifying the links between data, features, and spatial scale is crucial for robust safety analysis.
  • Telematics-informed AI models facilitate driver behavior-specific safety interventions.