Development of Traffic Imaging for Accident Detection System Using AI: A Review
Prof. Rahul Bhandekar1, Sanjana Fulzele2 , Chitaksha Bhoyar 3
1Project Guide, Department of Computer Science & Engineering, Wainganga College of Engineering and Management, Nagpur, Maharashtra.
23Students, Department of Computer Science & Engineering, Wainganga College of Engineering and Management, Nagpur, Maharashtra.
Abstract-
Road accidents are one of the most relevant causes of injuries and death. This is also one of the serious issues, which can possibly cause disabilities, injuries and even fatalities. There are many of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like rain, cloudy, and fog cause partial visibility and it may become difficult as well as risky to drive on such roads. This project aims to provide an Overview of the area of the art in the prediction of road accidents through clustering techniques and machine learning algorithms. The dramatic increase in road traffic accidents in the world is causing serious problems in every aspect of human lives. The most important and meaningful nature of traffic characteristics, causation analysis, and associations between different causal factors have been ignored. Moreover, the traffic accident data is only used to conduct a rudimentary statistical analysis and data mining efforts which results only in patterns and statistics. The main targets of this road accident data classification are to identify the major and key factors that cause the road traffic accident and form policies and preventive actions that would reduce the accident severity level. Machine learning algorithms are used to analyze the data, extract hidden patterns, predict the severity level of the accidents and summarize the information in a useful format.
Keywords – Road Accident Prediction, Machine Learning, K-Means Clustering, Traffic Safety, Accident Severity etc.
