Bayesian network of traffic accidents in Malaysia

Zamira Hasanah Zamzuri, Akmalia Shabadin, Siti Zaharah Ishak

Research output: Contribution to journalArticle

Abstract

Exploring the causes and effects of a hazardous event such as traffic accidents have been of vital importance to society. Statistical analyses have been widely implemented to understand and deduce inferences on the cause-effect analysis, and to anticipate the occurrences of accidents in the future. One of the issues that has not been solved through conventional statistical modelling is the existence of interrelationships between variables in the data set. However, with the advent of technology and the wide application of machine learning algorithm, this problem can be solved through the application of Bayesian network analysis, which is a directed acyclic probabilistic graphical model. By using Hill Climb (HC) and Tabu algorithms, the structure of the data was studied and the relationship was estimated through conditional probability, that is based on the Bayes' theorem. The results suggests that weather plays a major role in the increase of traffic accidents, and occurs by disrupting lighting conditions which then disrupts the traffic systems. Furthermore, the results indicate that fatal accidents have a higher likelihood to occur in head-on, turn over and out of control accidents. The use of the Bayesian network creates probability estimates to enable the identification of the risk and the necessary precaution needed to be implemented.

Original languageEnglish
Pages (from-to)473-484
Number of pages12
JournalJournal of Information and Communication Technology
Volume18
Issue number4
Publication statusPublished - 1 Oct 2019

Fingerprint

Malaysia
Highway accidents
Bayesian networks
Bayesian Networks
Accidents
Traffic
Electric network analysis
Learning algorithms
Learning systems
Bayes' Formula
Lighting
Statistical Modeling
Network Analysis
Graphical Models
Bayesian Analysis
Conditional probability
Probabilistic Model
Weather
Learning Algorithm
Deduce

Keywords

  • Bayesian network
  • HC algorithm
  • Tabu algorithm
  • Traffic accidents

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Cite this

Bayesian network of traffic accidents in Malaysia. / Zamzuri, Zamira Hasanah; Shabadin, Akmalia; Ishak, Siti Zaharah.

In: Journal of Information and Communication Technology, Vol. 18, No. 4, 01.10.2019, p. 473-484.

Research output: Contribution to journalArticle

Zamzuri, Zamira Hasanah ; Shabadin, Akmalia ; Ishak, Siti Zaharah. / Bayesian network of traffic accidents in Malaysia. In: Journal of Information and Communication Technology. 2019 ; Vol. 18, No. 4. pp. 473-484.
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