A System for Collecting and Analyzing Road Accidents Big Data

Many factors explain traffic accidents, such as the type of the accident site, its environment, the driver's behavior, and other uncertain complex factors. As a result, the occurrence of road accidents is non-linear, so it is necessary to explore the correlation between data from many aspects to minimize the risk. After data preprocessing following a classification using the datamining tools, relevant information can be deduced about the causes of the high-frequency accidents. Depending on the results obtained, we can verify the accuracy of the extracted information, and this can help predict new situations with similar data in the future. The aim is to choose the most accurate extraction process, by analyzing the characteristics of the data and their relationship with the analysis and the extraction process. In this paper, we propose a decision-making system for the traffic accident data analysis in order to extract information relevant to the prevention of the road risk. This system is based on appropriate datamining techniques for collecting, pre-processing and exploring accident data to categorize road accidents and identify the most problematic sites.