Accident Forecasting using IoT and Deep Learning Techniques

Road accidents are a major cause of injury and death worldwide, and there is a need for accurate and proactive accident prevention measures. In recent years, Internet of Things (IoT) devices have been increasingly used for collecting real-time data on various parameters that affect the likelihood of accidents, such as weather conditions, traffic volume, and road conditions. Additionally, deep learning techniques have shown promise in predicting the likelihood of accidents based on these parameters. This research work proposes a novel framework for accident forecasting that combines IoT devices and deep learning techniques. The proposed framework includes a network of IoT devices placed on roads that collect real-time data on various parameters, and a deep learning ensemble methods that uses these parameters to forecast the occurrences of accidents in the traffic. Here, this study utilizes the deep learning ensemble methods and LSTM neural network to process the time-series data from the IoT devices and make predictions. The model is trained and tested using a large dataset of past accidents and corresponding IoT data. Our model uses 31 different parameters to forecast accidents. Finally, the proposed model achieves high accuracy in predicting the likelihood of accidents, with an accuracy of 93.6 percent. The proposed platform can be used to improve road safety by enabling the implementation of more precise and proactive accident prevention measures by providing real-time accident forecasting.