Prediction of road accidents on Highways in Uttar Pradesh (India) using Linear Regression Model

Throughout the globe, transportation is the backbone of every country and a necessity of every human nowadays. Numerous problems (rain, fog, snow, dust, mist, potholes, overspeed, drowsiness of driver, absence of light or low vision, night vision-related problems, etc) are encountered at runtime on highways and roads. Due to these challenging problems, human lives (in lack per year) suffer in accidents on highways and roads. Similarly, India is facing huge injuries, accidental, and fatalities cases every year that cause a lack of people to lose their lives. Nowadays, every country is suffering from huge loss of public property such as vehicles, insurance, police, infrastructure, medical/healthcare, etc. This paper focuses on road accidents in India and other states. India's current situation of road accidents is higher than that of other countries. Thus, we need to analyze such accidental data and provide a solution for the minimization of such accidental/fatality cases. So, this paperwork aims to analyze the data developed by the Ministry of Road, Transportation & Highways (MORTH) that focuses on major causes and their minimization that leads to the reduction of road accidents. Here, the proposed work uses a regression model to predict the possibility of road accidents in the future. It also focuses on a computer vision-based solution for the vehicle speed detection model which is helpful in minimization of road accidents and also provides road safety.