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PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.

Qiuyang Huang1, Hongfei Jia1, Zhilu Yuan2

  • 1College of Transportation, Jilin University, Changchun, 130012, China.

Accident; Analysis and Prevention
|July 7, 2023
PubMed
Summary

This study introduces PL-TARMI, a deep-learning framework for creating detailed citywide traffic crash risk maps. It uses accessible data to improve traffic safety guidance cost-effectively.

Keywords:
Deep learningIntelligent transportation systemsMulti-source data fusionTraffic crash risk

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Area of Science:

  • Urban planning
  • Geographic Information Systems (GIS)
  • Transportation engineering
  • Artificial Intelligence (AI)

Background:

  • Accurate citywide traffic crash risk mapping is crucial for preventing accidents.
  • Fine-grained geographic traffic crash risk inference is challenging due to complex road networks, human behavior, and extensive data needs.

Purpose of the Study:

  • To develop a deep-learning framework, PL-TARMI, for accurate, fine-grained traffic crash risk map inference.
  • To leverage easily accessible data for cost-effective traffic safety analysis and prevention guidance.

Main Methods:

  • Integration of satellite imagery and road network data.
  • Combination with accessible data sources including Point of Interest (POI) distribution, human mobility, and traffic data.
  • Development of a deep-learning model (PL-TARMI) for pixel-level risk map generation.

Main Results:

  • PL-TARMI successfully generates pixel-level traffic crash risk maps.
  • The framework effectively utilizes diverse, accessible data inputs.
  • Experimental results on real-world datasets validate the model's effectiveness.

Conclusions:

  • PL-TARMI offers a novel and effective approach to fine-grained traffic crash risk assessment.
  • The framework provides more reasonable and cost-effective traffic crash prevention guidance.
  • This method addresses the challenges of complex road networks and data requirements in risk mapping.