Development of Traffic Imaging for Accident Detection System Using AI

Abstract

Traffic accidents are one of the leading causes of fatalities and severe injuries, endangering the lives and health of individuals. These accidents might have a variety of reasons, some internal to the driver while others are external. When there is low visibility because of unfavorable weather conditions like rain, clouds, and fog, driving can be challenging and even dangerous. Using algorithmic machine learning and approaches for clustering, this project aims to provide a summary of advanced methods for traffic accident predicting. The rising global vehicle accident rate has profound implications for all aspects of human life. Despite their importance, factors including causality assessment, traffic features, and the connections between different contributing components have typically been ignored. Moreover, the majority of the data on traffic accidents that is now accessible is used for data extraction and basic statistical analysis, which offers limited understanding of patterns and statistics. Through the identification of significant contributing factors and the development of preventative methods, this road accident information category seeks to lessen the severity of subsequent accidents. Machine learning algorithms are used to analyze data, identify hidden patterns, predict the impact of an event, and swiftly disseminate this information.

Introduction

The World Health Organization reports a high number of road fatalities globally each year, with traffic volumes increasing rapidly and causing more accidents. Predicting road accidents is a critical area of transportation safety research due to the many factors influencing accidents, such as road geometry, traffic patterns, driver behavior, weather, and visibility. However, there is no standard method for identifying accident-prone zones.

Machine learning and data mining techniques, especially clustering and classification algorithms, have shown promise in analyzing large accident datasets to detect patterns, identify hazardous locations, and predict accident frequency. Various studies have applied algorithms like XGBoost, Naïve Bayes, Decision Trees, Random Forest, and Logistic Regression to improve accident prediction accuracy.

The methodology in this research involved collecting accident data from sources like Kaggle, preprocessing it using Python libraries (Pandas and NumPy), and applying k-means clustering for unsupervised categorization of accident zones into high and low risk. Logistic regression was then used to predict accident likelihood with an accuracy of 86%. Visualization of results helped interpret patterns such as accident frequency during different times of the day.

Conclusion

Road Accidents are caused by various factors. By going through all the research papers it can be concluded that Road Accident cases are hugely affected by the factors such as types of vehicles, age of the driver, age of the vehicle, weather condition, road structure and so on. Thus we have build an application which gives efficient prediction of road accidents based on the above mentioned factors. The classification algorithm of the entire dataset. In the Road Accident prediction final result is to find the percentage of accident in particular area. Having lower number of features helps the algorithm to converge faster and increases accuracy. In the Road Accident prediction final result is to find the percentage of accident in particular area. Then we apply logistic regression on these features and obtain the least error Since k-means clustering is an unsupervised learning technique used for unlabeled data, the data in this article are not labelled into any particular cluster. Additionally, regression analysis with a sizable accident data set was employed in this work to determine the causes of traffic accidents. Plotting of the identified contributing elements to the accident is done through analysis and is shown as a graph. This provides a great deal of insight into accident situations and causes. And in the end, this aids the government in modifying road safety regulations to account for various accident and circumstance kinds.

References

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Copyright

Copyright © 2025 Prof. Rahul Bhandekar, Sanjana Fulzele, Chitaksha Bhoyar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.