What is it about?
Roadway accidents are very common and cause a great threat to developing and developed countries. Some basic knowledge is very important for everyone because safety of road accident and its prediction is more important in present days. Most of the variables influence the frequent accidents, and these are as road features, weather conditions, type of accident, road condition. These parameters or influential components are used in selecting the effective model for evaluating the accident reasons. This paper presents a model system for analysis of road accidents prediction and interpretation using KNN classification model. By using this described method, the best and effective performance of the road accident prediction model and their reasons is discovered with K-nearest neighbor classification. The described method is compared with the previous methods like logistic regression (LR) and Naïve Bayes (NB). According to the performance parameters, it can be clear that best model of road accident prediction is discovered. Therefore, the government takes the suggestive results from the model and improves the road safety measurements.
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Why is it important?
The emphasis on the importance of basic knowledge regarding road safety and accident prediction underscores the need for public awareness and education. Understanding the factors that contribute to road accidents can lead to more informed decisions by both drivers and policymakers. The adoption of a model system using the K-nearest neighbor (KNN) classification model for analyzing and predicting road accidents signifies a shift towards data-driven approaches in tackling road safety issues. This method allows for the identification of patterns and factors that are frequently associated with accidents. By comparing the KNN model with previous methods, such as logistic regression (LR) and Naïve Bayes (NB), the research highlights advancements in predictive modeling for road accidents. This comparative analysis is crucial for understanding the strengths and limitations of various models and selecting the most effective one for predicting road accidents.
Perspectives
The adoption of the KNN classification model for road accident prediction marks a significant technological innovation. This method utilizes historical accident data, considering various factors such as road features, weather conditions, accident types, and road conditions, to predict future accidents. This predictive capability is crucial for preemptive safety measures. The method's effectiveness is underscored by comparing its performance with other predictive models, such as logistic regression (LR) and Naïve Bayes (NB). Such comparisons are vital for validating the superiority or efficiency of the KNN model in the context of road safety.
Balajee Maram
SR University
Read the Original
This page is a summary of: Analysis of Road Accidents Prediction and Interpretation Using KNN Classification Model, September 2022, Springer Science + Business Media,
DOI: 10.1007/978-981-19-4052-1_18.
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