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Network-level accident-mapping: Distance based pattern matching using artificial neural network.

Lipika Deka1, Mohammed Quddus1

  • 1School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, United Kingdom.

Accident; Analysis and Prevention
|January 23, 2014
PubMed
Summary

A new accident mapping algorithm accurately snaps traffic incidents to road segments, improving accident research and risk analysis. This method enhances accuracy in complex road networks, outperforming existing techniques.

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Area of Science:

  • Traffic safety research
  • Geographic Information Systems (GIS)
  • Data science

Background:

  • Accurate traffic accident mapping is crucial for identifying accident hotspots and modeling risks.
  • Existing algorithms lack transferability, struggle in dense networks, and fail to address data inaccuracies.
  • Current methods are often dataset-specific and perform poorly in diverse road environments.

Purpose of the Study:

  • To develop a novel, robust accident mapping algorithm adaptable to various datasets and road network complexities.
  • To address uncertainties in accident data and digital road network information.
  • To improve the accuracy of assigning traffic accidents to correct road segments.

Main Methods:

  • A distance-based pattern-matching approach using common variables (road name, type, vehicle direction, location).
Keywords:
Accident-mappingArtificial neural networkPattern-matching

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  • An Artificial Neural Network (ANN) with a single-layer perceptron to learn feature importance for accurate link identification.
  • Incorporation of uncertainties inherent in accident and road network data.

    • Main Results:

    • The developed algorithm demonstrates significantly improved accuracy compared to existing methods.
    • Effective handling of data uncertainties and varying road network complexities.
    • Successful validation using a UK accident dataset.

    Conclusions:

    • The new algorithm offers a more accurate and adaptable solution for traffic accident mapping.
    • It overcomes limitations of previous methods, enabling more reliable accident research.
    • This approach facilitates better identification of accident hotspots and risk assessment.