click here to download project abstract of data mining techniques
This report presents the results from the research study on applying large scale data mining methods into analysis of traffic accidents on the Finnish roads. The data sets collected from traffic fatal accidents are huge, multidimensional, and heterogeneous. Moreover, they may contain incomplete and erroneous values, which make its exploration and understanding a very demanding task. The target data of this study was collected by the Finnish Road Administration Datasets. The intention is to investigate the usability of robust clustering, association and frequent item sets, and visualization methods to the road traffic accident analysis. While the results show that data mining techniques
the selected data mining methods are able to produce understandable patterns from the data, finding more fertilized information could be enhanced with more detailed and comprehensive data sets. K-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to
characterize these Surface Condition. The rules revealed different factors associated with road accidents at different drunk and drive with varying accident frequencies. The association rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of fatal accidents.
Analyzing road traffic fatal accidents using data mining techniques involves delving into vast datasets containing accident details, demographics, weather conditions, and road characteristics. By employing algorithms and statistical models, patterns, correlations, and predictive insights emerge, aiding in understanding the factors contributing to accidents.
This analysis scrutinizes various aspects: identifying high-risk zones, determining common causal factors, and recognizing recurring patterns leading to fatal incidents. Through clustering methods or decision trees, it categorizes accidents based on similarities and highlights key attributes influencing their severity.
The ultimate goal is prevention and intervention. By recognizing trends and risk factors, policymakers, law enforcement, and urban planners can implement targeted safety measures. These might include improved road infrastructure, enhanced traffic management strategies, or public awareness campaigns, ultimately striving towards a safer road environment for all.
This process integrates technology and data to drive informed decisions, aiming for a significant reduction in road fatalities.
