Publication Date: 2024/02/26
Abstract: The main aspect of the project is to indicate accident possibility to the driver by using machine learning algorithms. This is useful to reduce road accidents pose significant threats to public safety, and predicting the likelihood of accidents can play a crucial role in implementing preventive measures. This project focuses on developing a Road Accident Prediction System using machine learning techniques. By considering the road conditions, weather conditions, speed, traffic density, time of day, junction type, month, road quality, vehicle type, population density, age of the driver, alcohol or drug influence, and vehicle condition. In addition, the system enables real-time predictions through user interaction, allowing individuals to input specific conditions and receive instant assessments of accident risk. This interactive feature enhances user engagement and awareness regarding potential risks associated with varying road scenarios. The abstracted model serves as a foundation for more advanced predictive systems, fostering the development of proactive safety measures and contributing to the overall enhancement of road safety. The project emphasizes the importance of leveraging machine learning for accident prediction and encourages further exploration in the domain of intelligent transportation systems. Here we are using ml algorithms Decision tree, Clustering, Regression Models, Anomaly Detection and using readings of the Sensors to measure weather conditions, vehicle speed,road conditions that are used to detect the potholes on the roads and these data are collected and train the dataset by using algorithms.
Keywords: Road Accident Prediction, Prevention, Weather Conditions, Speed Detection, Population Density, Alocholor Drug Influence, Machine Learning Algorithms and IOT sensors.
DOI: https://doi.org/10.5281/zenodo.10706890
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB909.pdf
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