Multivariate Survival Analysis Models for Incident Duration With Censored Data

Abstract:

Traffic incident duration is one of the most important indexes in accident management studies. The semi parametric proportional hazards model (Cox model) and parametric accelerated failure time model (AFT model) were employed for analysis of the association between multivariate incident durations and risk factors. With the three year traffic incidents data collected from some freeways in Zhejiang Province, the Cox model and AFT model with covariates selected to remain in the model and survival probability of sensitivity to common covariates were illustrated and compared. Based on the Cox and the Log logistic AFT models, the parameter estimates showed that six significant covariates were selected to remain in the estimated survival functions, including accident time, accident type, number of lanes occupied, number of the injured and accident of fatality. With regard to the most significant indicator variable (accident of fatality), the estimates of the survival probability curves for duration demonstrate that the AFT model give a higher sensitivity than the Cox model, and the Cox model is more appropriate for short time incident duration prediction. Moreover, survival analysis models can predict duration based on incident report and benefit rescue countermeasure and emergency aid decision after accidents.