Paper Title
Road accident prediction and classification using machine learning
Article Identifiers
: Keywords
Road Safety Prediction System, Accident Severity Prediction
Abstract
With the escalating number of vehicles, road safety has become a pressing concern, claiming 1.2 million lives annually worldwide. In Hyderabad alone, 2367 injury accidents were reported in 2017, causing significant loss of life and economic damage. To address this issue, we've employed machine learning algorithms to predict accident severity at specific times and locations. Factors such as speed limit, age, weather, vehicle type, light conditions, and day of the week were used for model training, leveraging a dataset from the UK government spanning 2005-2015. Random Forest was selected for its highest accuracy of 86.86%. Using Python, Scikit-Learn, NumPy, and Matplotlib, we built and tested the model on Google Collab and deployed it on Microsoft Azure's GPU-powered virtual machine. OpenWeatherMap API provides real-time weather and light data, while TextLocal API sends SMS notifications to authorities. A user-friendly web app, secured with HTTPS and a custom domain, facilitates input and output display. This predictive model promises to significantly aid traffic planning and management, potentially reducing road accidents in the future.
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How To Cite (APA)
Devansh Pradhan, Apurv Upadhyay, Arpita Pandey, Ashish Gupta, & Dr. Rajkumar Gaur (March-2024). Road accident prediction and classification using machine learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), b273-b278. https://ijnrd.org/papers/IJNRD2403129.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : b273-b278
Other Publication Details
Paper Reg. ID: IJNRD_215106
Published Paper Id: IJNRD2403129
Downloads: 000122256
Research Area: Engineering
Author Type: Indian Author
Country: GORAKHPUR, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403129.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403129
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