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A comparative study of M5P, ANN and RENB models for prediction of vulnerable road accident frequency

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  • Saurabh Jaglan
  • Praveen Aggarwal
  • Sunita Kumari

Abstract

The present investigation aims to evaluate the performance of different models to calculate the Vulnerable Road User Accident Frequency (VRUAF). Nine road stretches were chosen to measure the road geometry and other similar characteristics. Many field studies were conducted to gather information about road geometry, traffic surveys and accident characteristics. A total of 17 input parameters were collected for accident frequency analysis, and three prediction approaches were applied: Fixed/Random Effect Negative Binomial (FENB/RENB) regression models, Artificial Neural Network (ANN) and M5P model tree. The variation in models' performance was observed in terms of the coefficient of correlation (0.943-0.981), root mean square error (2.274-1.655) and mean absolute error (1.746-1.351). The result suggests that the ANN model is the most accurate model where CC, MAE and RMSE values are 0.981, 1.351 and 1.655, respectively. Thus, this model can synthetically predict VRUAF under similar geometric conditions.

Suggested Citation

  • Saurabh Jaglan & Praveen Aggarwal & Sunita Kumari, 2024. "A comparative study of M5P, ANN and RENB models for prediction of vulnerable road accident frequency," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 18(3), pages 231-251.
    • Handle: RePEc:ids:ijrsaf:v:18:y:2024:i:3:p:231-251
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