Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

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Classifying the Severity Levels of Traffic Accidents Using Decision Trees

Authors
Zamira Hasanah Zamzuri1, *, Khaw Zhi Qi1
1Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
*Corresponding author. Email: zamira@ukm.edu.my
Corresponding Author
Zamira Hasanah Zamzuri
Available Online 12 December 2022.
DOI
10.2991/978-94-6463-014-5_17How to use a DOI?
Keywords
Traffic accidents; Classification; Decision trees; Severity level
Abstract

Road accident is one of the main causes of deaths in Malaysia as well as heart disease and cerebrovascular disease. This study aims to identify the main factors that drive the occurrence of road accidents in Malaysia. Thus, preventive measures can be designed to reduce the incidence of road accidents. The relationship between the severity of road accidents and influencing factors such as vehicle movement, traffic system, marking and road geometry are also studied. The Classification and Regression Tree (CART) and Chi-square Automatic Interaction Detector (CHAID) techniques are used to identify the effects of factors in this study. The results from the decision tree show that the main factors that determine the severity of the accident are the type of vehicle, the type of violation, lighting, and severity of the driver’s injuries. The performances of the two classification models are compared based on the prediction accuracy and models reliability. It is found that CHAID performs slightly better than CART and offers richer information in terms of influential factors and decision rules. The information in this study is important with the hope that road users can be vigilant and avoid being exposed to causes that allow them to be involved in accidents.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)
Series
Advances in Computer Science Research
Publication Date
12 December 2022
ISBN
978-94-6463-014-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-014-5_17How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zamira Hasanah Zamzuri
AU  - Khaw Zhi Qi
PY  - 2022
DA  - 2022/12/12
TI  - Classifying the Severity Levels of Traffic Accidents Using Decision Trees
BT  - Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)
PB  - Atlantis Press
SP  - 173
EP  - 181
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-014-5_17
DO  - 10.2991/978-94-6463-014-5_17
ID  - Zamzuri2022
ER  -