Analysis of human errors in maritime accidents: A Bayesian spatial multinomial logistic model
Tianyi Sheng, Jinxian Weng, Kun Shi, Bing Han
Abstract
Considering the unobserved spatial heterogeneity, this study aims to build a Bayesian spatial multinomial logistic (BSMNL) model by utilizing the geographic information from historical maritime accidents. The proposed BSMNL model can be applied to investigate the determinants of human errors involved in maritime accidents. Compared to the traditional multinomial logistic (MNL) model, the proposed BSMNL model produces a more accurate estimate of the effects of environmental and accident factors on the occurrence likelihood of human errors in maritime accidents. Results show that accidents involving cargo and container ships; tankers carrying liquefied natural gas (LNG), liquefied petroleum gas (LPG), or oil; and fishing vessels are more likely to be associated with human errors. Further, one important finding is that the involvement of fishing vessels significantly increases the occurrence probability of both negligence errors and judgment or operational errors. In addition, the occurrence likelihood of human errors is generally higher in springtime, conditions of poor visibility, the absence of strong winds or waves, and the moored or docked status.