Exploratory framework for analysing road traffic accident data with validation on Gauteng province data

Tebogo Makaba, Wesley Doorsamy, Babu Sena Paul

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Article number1834659
JournalCogent Engineering
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • dimensionality reduction
  • exploratory data analysis
  • key statistical
  • machine learning
  • road traffic accidents

ASJC Scopus subject areas

  • General Computer Science
  • General Chemical Engineering
  • General Engineering

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