Exploratory framework for analysing road traffic accident data with validation on Gauteng province data
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1834659 |
| _version_ | 1797760291150233600 |
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| author | Tebogo Makaba Wesley Doorsamy Babu Sena Paul |
| author_facet | Tebogo Makaba Wesley Doorsamy Babu Sena Paul |
| author_sort | Tebogo Makaba |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T18:56:28Z |
| format | Article |
| id | doaj.art-722c97dc13594b6bb9ba1fb757dead02 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T18:56:28Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-722c97dc13594b6bb9ba1fb757dead022023-08-02T06:53:06ZengTaylor & Francis GroupCogent Engineering2331-19162020-01-017110.1080/23311916.2020.18346591834659Exploratory framework for analysing road traffic accident data with validation on Gauteng province dataTebogo Makaba0Wesley Doorsamy1Babu Sena Paul2University of JohannesburgUniversity of JohannesburgUniversity of JohannesburgExploratory 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.http://dx.doi.org/10.1080/23311916.2020.1834659exploratory data analysiskey statisticaldimensionality reductionmachine learningroad traffic accidents |
| spellingShingle | Tebogo Makaba Wesley Doorsamy Babu Sena Paul Exploratory framework for analysing road traffic accident data with validation on Gauteng province data Cogent Engineering exploratory data analysis key statistical dimensionality reduction machine learning road traffic accidents |
| title | Exploratory framework for analysing road traffic accident data with validation on Gauteng province data |
| title_full | Exploratory framework for analysing road traffic accident data with validation on Gauteng province data |
| title_fullStr | Exploratory framework for analysing road traffic accident data with validation on Gauteng province data |
| title_full_unstemmed | Exploratory framework for analysing road traffic accident data with validation on Gauteng province data |
| title_short | Exploratory framework for analysing road traffic accident data with validation on Gauteng province data |
| title_sort | exploratory framework for analysing road traffic accident data with validation on gauteng province data |
| topic | exploratory data analysis key statistical dimensionality reduction machine learning road traffic accidents |
| url | http://dx.doi.org/10.1080/23311916.2020.1834659 |
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