Open Access

ARTICLE

ASLP-DL —A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

Saba Awan^1,*, Zahid Mehmood^2,*

1 Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
2 Department of Computer Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan

* Corresponding Authors: Saba Awan. Email: ; Zahid Mehmood. Email:

(This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)

Computers, Materials & Continua 2024, 78(2), 2535-2555. https://doi.org/10.32604/cmc.2024.047337

Received 02 November 2023; Accepted 28 December 2023; Issue published 27 February 2024

Abstract

Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent. The framework optimizes hidden layers and integrates data augmentation, Gaussian noise, and dropout regularization for improved generalization. Sensitivity and factor contribution analyses identify influential predictors. Evaluated on three diverse crash record databases—NCDB 2018–2019, UK 2015–2020, and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%, a Roc Area of 0.751, an F-estimate of 0.941, and a Kappa score of 0.0629 over the NCDB dataset. The proposed framework consistently outperforms traditional methods, existing machine learning, and deep learning techniques.

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Keywords

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