Review On Development Of An Accident Detection System Utilizing Traffic Imaging And Machine Learning Techniques

Abstract

One of the major causes of deaths and serious injuries is traffic accidents, which pose a serious risk to people\'s health and even their lives. Numerous causes, some internal the driver and others external, might cause these incidents. Driving may be difficult and even dangerous when there is poor visibility due to unfavorable weather conditions including rain, clouds, and fog. With the use of machine learning algorithms and clustering techniques, this project seeks to give an overview of sophisticated approaches for forecasting traffic accidents. The increasing frequency of auto accidents worldwide has far-reaching effects on many facets of human existence. Aspects including causality evaluation, traffic characteristics, and the relationships between various contributing components have been generally disregarded, despite their significance. Furthermore, the majority of the traffic accident data that is currently available is utilized for data mining and rudimentary statistical analysis, providing little insight into statistics and patterns. This road accident data categorization aims to reduce the severity of additional accidents by identifying important contributing elements and developing preventive strategies. Algorithms for machine learning are used to evaluate data, find hidden patterns, forecast the severity of an occurrence, and quickly distribute this knowledge.

Introduction

The World Health Organization reports a troubling number of annual road fatalities globally. With traffic volume increasing rapidly, traffic accidents have become a major concern, making road accident prediction a critical area in transportation safety. Key influencing factors include road conditions, traffic flow, driver behavior, and environmental elements.

Problem Identification
Road accidents are difficult to predict due to numerous variable factors like weather, driver behavior, and road infrastructure. Current traffic systems lack effective methods for identifying accident-prone locations. Machine learning can process large and complex datasets to detect patterns, making it suitable for predicting and analyzing road accidents.

Objectives

1. Identify and apply machine learning techniques to classify and analyze road accidents.

2. Assist in visual recognition of traffic incidents.

3. Save time in accident detection and response.

4. Provide timely and appropriate responses.

Literature Survey
Various studies have applied machine learning and data mining algorithms such as XGBoost, Fast R-CNN, Naive Bayes, Decision Trees, Multi-layer Perceptron, and Apriori to identify accident hotspots, assess causes, and predict fatalities. The findings consistently show human error as a major cause of accidents. Tools like Weka, Orange, and RapidMiner are commonly used to implement these models with high accuracy.

Methodology
The project uses data collected from Kaggle and government sources. Python, along with Pandas and NumPy, is used for data analysis. Logistic regression is the main algorithm applied for modeling relationships between accident factors and outcomes. The methodology involves data collection, preprocessing, analysis, and visualization.

Tools and Platforms
Machine learning applications fall into three categories:

1. Data Mining – Used to analyze vast datasets (e.g., in healthcare and traffic safety).

2. Software Applications – Used in fields like speech and image recognition.

3. Self-Customizing Programs – Algorithms that adapt user experiences (e.g., personalized content feeds).

The system uses training and testing datasets for predictive modeling. A web-based interface with GUI allows users to load data and view results, helping in real-time traffic accident monitoring and response

Conclusion

K-means clustering is a method of unsupervised learning that is employed in this work for the unlabeled data; as a result, the results are not categorised into any clusters. Regression methods were used as well in this work to determine the causes of traffic accidents using an enormous amount of accident data. Analysis is carried out to determine the accident-related parts that occur simultaneously and are then shown in graph form. This adds significantly to our understanding of accident situations and causes. And in the long run, this assists the Government in modifying the traffic safety rules to account for different accident kinds and circumstances.

References

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Copyright

Copyright © 2025 Ms. Twinkle Vinod Katare, Mr. Rahul Nawkhare, Mr. Rahul Bhandekar. 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.