Exploratory framework for analysing road traffic accident data with validation on Gauteng province data. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Makaba, Tebogo; Doorsamy, Wesley; Paul, Babu Sena
• Editors: Alexandridis, Alex
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: exploratory data analysis -- key statistical -- dimensionality reduction -- machine learning -- road traffic accidents; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1834659
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Exploratory data analysis (EDA) is often a necessary task in uncovering hidden patterns, detecting outliers, and identifying important variables and any anomalies in data. Furthermore, the approach can be used to gain insights by modelling the dataset through graphical representations. In this paper, we propose an exploratory framework for analysing a road traffic accidents real-life dataset using graphical representations and incorporating dimensionality reduction methods. Both Principal component and Linear discriminant analyses are performed on the dataset and the resulting performance metrics reveal some comprehensive insights of the road traffic accident patterns. The investigation also revealed which road traffic factors contribute more significantly to the events. Classification results were generated after applying the dimensionality reduction methods to the dataset and show that the application of Linear discriminant analysis dimensionality reduction together with Naïve Bayes classification performed better as compared to the other approaches for the dataset.