Forecasting Road Traffic Accident Using Deep Artificial Neural Network Approach in Case of Oromia Special Zone

Raja, Kannaiya; Kaliyaperumal, Karthikeyan; Velmurugan, L.; Thanappan, Subash

doi:10.21203/rs.3.rs-735065/v1

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…To predict injury severity, the TabVAE model comprised multiple networks: the observation network gathered observed data; the heterogeneity network used contrastive learning and variational autoencoder technique extracted unobserved heterogeneity; and the aggregation network combined the observed and unobserved data. [47] forecasted road accidents and employed various ANN models to predict the severity of road crashes. [48] conducted a literature review of the neural network methodologies that were used in the analysis of traffic accidents.…”

Section: Crash Injury Predictionmentioning

confidence: 99%

Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

Sattar,

Ishak,

Affendey

et al. 2024

IEEE Access

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Crash severity prediction is a challenging research area, where the objective is to accurately assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing studies is to precisely assess the potential severity of crashes under diverse circumstances, such as weather conditions, vehicle attributes, road characteristics and layout, and traffic control factors. This effort aids authorities in establishing effective emergency response systems. The novelty and objective of our work involve contributing to this research area by employing a graph architecture to capture relationships among various crash records to uncover any hidden patterns that traditional ML models might overlook. The current study extends existing knowledge by leveraging Graph Neural Networks (GNN) and comparing their performance to popular ensemble-based models, which include Extreme Gradient Boosting (XGBoost) and Random Forest (RF) and Artificial Neural Networks (ANNs). Real data from the United Kingdom (UK) was employed to achieve our goal. All models underwent training using the training dataset, followed by performance evaluation using diverse metrics such as the accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), confusion matrix, and computational cost on the test dataset. Overall, our proposed GNN-based model demonstrated better performance when compared to other models. Specifically, the GNN model outperformed all other models across all metrics. For instance, the accuracy stood at 85.55% for GNN as compared to 83.36%, 83.18%, and 83.27% for XGBoost, RF, and ANNs models, respectively. The GNN model assisted in identifying hidden patterns by considering non-linear relationships among crash records. Thus, the model had the potential to improve its ability to predict severe accidents, which could in turn significantly improve emergency response efforts and reduce the likelihood of severe accidents resulting in fatalities.

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Section: Crash Injury Predictionmentioning

confidence: 99%

Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

Sattar,

Ishak,

Affendey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

BULUT

2023

Journal of Polytechnic

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As global urbanization accelerates, road safety remains a pressing concern, underscored by escalating traffic accidents and fatalities. Road Traffic Injuries (RTI) have become the eighth leading cause of death worldwide. The article delves deep into the potential of machine learning in predicting traffic accidents, their severity, and causal factors. This study comprehensively evaluates machine learning models on traffic accident records sourced from the Addis Ababa City Police Department. Comprising 12,316 records with 15 features, the dataset underwent preprocessing techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Min-Max scaling. Five algorithms – Random Forest (RF), Gaussian Naive Bayes, CatBoostClassifier, LightGBM, and XGBoost – were tested for their prediction accuracy. The findings spotlight the dominance of the RF model, achieving a peak accuracy of 92.2% post-SMOTE and Min-Max application. A comparative analysis with existing literature showed that while RF is a recurrently effective model across various datasets, data preprocessing and model suitability to specific datasets is paramount. This study underscores the potential of machine learning in traffic accident analysis and the nuanced choices researchers must make for optimal outcomes.

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Forecasting Road Traffic Accident Using Deep Artificial Neural Network Approach in Case of Oromia Special Zone

Cited by 2 publications

References 7 publications

Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

Road Crash Injury Severity Prediction Using a Graph Neural Network Framework

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

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