Predicting and Assessing Road Accidents Using Autoregressive Model and Value at Risk Approach
Issued Date
2022-01-01
Resource Type
ISSN
21984182
eISSN
21984190
Scopus ID
2-s2.0-85115362762
Journal Title
Studies in Systems, Decision and Control
Volume
383
Start Page
163
End Page
175
Rights Holder(s)
SCOPUS
Bibliographic Citation
Studies in Systems, Decision and Control Vol.383 (2022) , 163-175
Suggested Citation
Roslan T.R.N., Ch’ng C.K., Misiran M., Phewchean N. Predicting and Assessing Road Accidents Using Autoregressive Model and Value at Risk Approach. Studies in Systems, Decision and Control Vol.383 (2022) , 163-175. 175. doi:10.1007/978-3-030-79606-8_13 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84408
Title
Predicting and Assessing Road Accidents Using Autoregressive Model and Value at Risk Approach
Author(s)
Other Contributor(s)
Abstract
Road accidents have claimed many lives with approximately 1.35 million deaths worldwide and deemed as a critical issue in most countries. Understanding the common factors that contribute to road accidents is not enough, and it is essential to assess the risk involved to prepare for precautionary actions. In this study, we utilize the autoregressive model and value at risk approach in predicting Malaysian road accidents occurrences and assessing the respective risk. We construct a current risk analysis theoretical framework pertain to the vehicle’s condition, forecast and investigate the relationship between the involved variables, and obtain the value at risk for road accidents. From our findings, the road accidents will increase 1.12% higher than the year 2019, along with a 2.77% increase in the number of transportations in 2020. In addition, there is a 95% confidence that in year 2020, the number of road accidents will reduce not more than 3.81%. Coherent from the analysis, there is a potential to adopt these approaches more extensively for this issue as the quantitative analysis are feasible. In addition, a strong positive relationship is found between the likelihood of road accidents and the number of transportations. Thus, prediction of road accidents and identification of its value at risk on yearly basis are beneficial to project the best course of action to deal with road accidents occurrences in the country.
