Log-linear modelling is advanced as a procedure to identify factors that underlie the relative frequency of occurrence of various characteristics. The purpose of this study is to present a modelling effort using log-linear models to estimate the relationships between driver’s fault and carelessness and the traffic variables such as gender, accident severity, and accident time. The study was conducted in four different districts in Ankara, the capital of Turkey. There were 1,325 people selected for the study; and they were asked whether they had been in an accident. Four hundred and forty-eight of them answered that they had been involved in an accident. As drivers, 276 out of 448 people, namely 61.6%, had traffic accidents. The data on the variables, namely gender, driver’s fault and carelessness, accident severity and accident time, were collected through a questionnaire survey. Detailed information has been created based on this information. The analysis showed that the best-fit model regarding these variables was the log-linear model. Furthermore, the odds ratio between these variables, the associations of the factors with the accident severity and the contributions of various factors, and the multiple interactions between these variables were assessed. The obtained results provide valuable information in regard to preventing undesired consequences of traffic accidents.
Abdel-Aty, M., Chen, C.L., Schott, J.R.: An assessment of the effect of driver age on traffic accident involvement using log-linear models. Accident Analysis and Prevention 30(6), 1998, 851-861
Agresti, A.: Categorical Data Analysis. John Wiley, New York, 1990
Decarlo, E. T., Laczniak, R.N., Azevedo K.A., Ramaswami, S.N.: On the Log-Linear Analysis of multiple response data. Marketing Letters 11(4), 2000, 349-361
Iacobucci, D., Ann, L. McGill.: Analysis of Attribution Data: Theory Testing and Effects Estimation. Journal of Personality and Social Psychology. 59(3), 1990, 426-441
Jang, T.Y.: Analysis on reckless driving behavior by log-linear model, KSCE Journal of Civil Engineering, 10 (4), 2006, 297-303
Kim, K., Nitz, L., Richardson, J., Li, L.: Analyzing the relationship between crash types and injuries in motor vehicle collisions in Hawaii. Transportation Research Record.1467, 1995a, 9-13
Kim, K., Nitz, L., Richardson, J., Li, L.: Personal and behavioral predictors of automobile crash and injury severity, Accident Analysis and Prevention, 27, 4, 1995b, 469-481
Lawal, B.: Categorical Data Analysis with SAS and SPSS Applications, Lawrence Erlbaum Associates, Inc., London, (2003)
Lourens, P. F., Vissers, J. A. M. M., Jessurun, M.: Annual mileage, driving violations and accident involvement in relation to drivers’ sex, age and level of education. Accident Analysis and Prevention, 31 (1), 1999,593-597
Richardson, J., Kim, K., Li, L., Nitz, L.: Patterns of motor vehicle crash involvement by driver age and sex in Hawaii. Journal of Safety Research. 27(2), 1996, 117-125
Upton, J.G.: The Analysis of Cross-tabulated Data. John Wiley and Sons, New York, 1977
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