ANFIS-Based Traffic Accident Prediction Model for Karnataka State
DOI:
https://doi.org/10.22399/ijcesen.2137Keywords:
Accident prediction, Machine learning, ANFIS, Fuzzy subtractive clustering, Weather ForecastingAbstract
Given the seriousness of road traffic accidents as a public health concern, it is critical to comprehend the variables linked to an increase in the severity of injuries sustained by those who intervene in accidents. To improve road safety, make better decisions about road safety, and lessen the severity of crashes in the future, it is crucial to identify these elements. The study aimed to collect traffic data, analyse it to identify suitable variables for accident prediction, and to develop an accident predictive model suitable for Indian conditions. The dataset comprised of 67 blackspots, each containing 16 variables. One innovative aspect of this study involves the utilization of the fuzzy subtractive clustering algorithm to predict the accident. This approach holds theoretical promise in terms of computational efficiency. This contrasts with the traditional exponential increase in computational load with data dimensionality. Root Mean Squared Error (RMSE), and Coefficient of Determination (R²) metrics were used to assess the model performance after the data was divided into training and validation sets, R2 of 0. 67 is obtained. The study emphasises the potential of machine learning to improve traffic safety.
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