A Relationship Model between Accident Factors and the Traffic Accident Severity Using Logistic Regression Model

Authors

  • H Halim Doctoral Student, Department of Civil Engineering, Faculty of Engineering, Hasanuddin University, Jl. Poros Malino Km. 6 Gowa, 92172, Indonesia
  • M I Ramli Lecturer, Department of Civil Engineering, Faculty of Engineering, Hasanuddin University Jl. Poros Malino Km. 6 Gowa, 92172, Indonesia
  • S. A Adisasmita Lecturer, Department of Civil Engineering, Faculty of Engineering, Hasanuddin University Jl. Poros Malino Km. 6 Gowa, 92172, Indonesia
  • S. H Aly Lecturer, Department of Environmental Engineering, Faculty of Engineering, Hasanuddin University Jl. Poros Malino Km. 6 Gowa, 92172, Indonesia
  • J Prasetijo Lecturer, Department of Rail Transportation, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia

DOI:

https://doi.org/10.20956/ijesca.v4i2.1391

Keywords:

Accident factors, traffic accidents severity, logistic regression model

Abstract

The present paper purposes to develop the relationship model between the factors of accidents and severity level of traffic accidents by using multinomial logistic regression model approach, for a case study the traffic accident in Makassar City, Indonesia. In further, the study evaluates the traffic accident factors which significantly influence the traffic accident severity level. In this regard, the outcome variable is the severity level of the traffic accident which has three attributes, i.e., death, serious injury, and minor injury. The explanatory variables involve victim characteristics and traffic accident characteristics. The present study used the traffic accident database during 2012 – 2015 which recorded by the traffic police agency in the city. The model calibration results show that the relationship model has a good accuracy level. The victim position and the collision types significantly influence the severity accident level. The results provide basic information for efforts in reducing traffic accidents. 

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Published

2018-02-05

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Section

Articles