Enhancing Road Traffic Accident Prediction Using Hybrid Machine Learning Models

Abstract

In road safety, the ability to forecast traffic accidents correctly is central to the application of proactive responses in terms of accidents. The paper is based on a thorough strategy of improving the accuracy of predicting by a hybrid machine learning model, which is a combination of a Random Forest (RF) machine learning algorithm and Support Vector Machine (SVM). The Kaggle-gathered dataset includes road traffic accidents between 2017 and 2020, which has 32 features and 12,316 accidents. After the preprocessing of data, including Min-Max normalization, the hybrid RF-SVM model is created to take the advantages of both the ensemble learning and the margin based classifiers. The Random Forest aspect is applied to determine the most influential features to consider and the dimensionality of the datasets which can potentially lead to accidents and therefore these are managed well even in large volumes. The step simplifies the model and increases its interpretability. The SVM element then carries out the classification task, which is to optimize the boundaries of decisions by using a narrow range of features. The capability of SVM to work with both non-linear and linear relations as well as the use of kernel tricks makes it possible to guarantee the solid separation of accident-prone and non-accident-prone cases. The RF-SVM model is superior in comparison to other hybrid models like Conv-LSTM, CNN-LSTM, and CNN-GRU, which show a higher level of functionality. The suggested model is tested in Python software, which has an impressive accuracy of 99.13. These measurements highlight the fact that this model is able to make a trustworthy forecast and balance accuracy and recall. The knowledge of the process of feature selection can help the policymaker with the understanding of major elements of accident causes and provide the specific intervention to improve the road safety. The results of the present study indicate the promise of the hybrid machine learning models to the development of the predictive analytics of road traffic accidents and the subsequent creation of smarter, data-driven road safety strategies.