Data mining in road crash analysis: the context of developing countries. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Raihan, Md Asif; Hossain, Moinul; Hasan, Tanweer
• Is Part Of: International journal of injury control and safety promotion. Volume 25:Issue 1(2018)
• Page Start: 41
• Page End: 52
• Publication Date: 2018-01-02
• Subjects: Data mining -- crash data -- hierarchical clustering -- random forest -- classification and regression trees; Wounds and injuries -- Prevention -- Periodicals; Wounds and Injuries -- prevention & control -- Periodicals; Consumer Product Safety -- Periodicals; 363.107
• DOI: 10.1080/17457300.2017.1323929
• Languages: English
• ISSNs: 1745-7300

Abstract

Abstract: The recent advancements in the field of data mining have made vast progress in extracting new information and hidden patterns from large datasets which are often overlooked by the traditional statistical approaches. These methods focus on searching for new and interesting hypothesis which were previously unobserved. Road safety researchers working with the crash data from developed world have seen encouraging success in obtaining new insight into crash mechanism through data mining. An attempt was made in this study to apply these advance methods and evaluate their performance in manifesting crash causes for Bangladesh. The study applies hierarchical clustering to identify hazardous clusters, random forest to find important variables explaining each of these clusters, and classification and regression trees to unveil their respective crash mechanisms for the road crash data of Bangladesh. The results identified several new interesting relationships and acknowledged issues related to quality of data.