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Latent Semantic Index Based Feature Reduction for Enhanced Severity Prediction of Road Accidents

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Abstract

Traditional approaches do not have the capability to analyse the road accident severity with different road characteristics, area and type of injury. Hence, the road accident severity prediction model with variable factors is designed using the ANN algorithm. In this designed model, the past accident records with road characteristics are obtained and pre-processed utilizing adaptive data cleaning as well as the min-max normalization technique. These techniques are used to remove and separate the collected data according to their relation. The Pearson correlation coefficient is utilized to separate the features from the pre-processed data. The ANN algorithm is used to train and validate these retrieved features. The proposed model’s performance values are 99, 98, 99 and 98% for accuracy, precision, specificity and recall. Thus, the resultant values of the designed road accident severity prediction model with variable factors using the ANN algorithm perform better compared to the existing techniques.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Civil Engineering Department, Deenbandhu Chhotu Ram University of Science and Technology, 131039, Murthal, Haryana, India

    Saurabh Jaglan & Sunita Kumari

  2. Civil Engineering Department, National Institute of Technology, 136119, Kurukshetra, Haryana, India

    Praveen Aggarwal

Authors
  1. Saurabh Jaglan
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  2. Sunita Kumari
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  3. Praveen Aggarwal
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Contributions

The corresponding author claims the major contribution of the paper including formulation, analysis and editing. The co-authors provides guidance to verify the analysis result and manuscript editing.

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Correspondence to Saurabh Jaglan.

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Saurabh Jaglan, Kumari, S. & Aggarwal, P. Latent Semantic Index Based Feature Reduction for Enhanced Severity Prediction of Road Accidents. Opt. Mem. Neural Networks 33, 221–235 (2024). https://doi.org/10.3103/S1060992X24700103

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  • DOI: https://doi.org/10.3103/S1060992X24700103

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