title: A multiple discriminant analysis of vessel accidents.
abstract: A large sample of 936 vessel accident cases occurring between 1979 and 1987 on the lower Mississippi River were cluster analyzed to generate four groups relatively unique in their respective attribute values. The attributes used to cluster the accidents included participation in the U.S. Coast Guard's New Orleans Vessel Traffic Service (NOLA-VTS), type of accident, river stage traffic level, system utilization, accident location, weather conditions, and time of accident. The four-group cluster solution resulted in logical groupings, given the realities of navigating the lower Mississippi River. The four groups resulting from the cluster analysis were characterized as Group 1: Danger Zone (224 cases), 100% NOLA-VTS participants whose accidents occurred primarily on the most dangerous part of the river; Group 2: Bad Conditions for Good Navigators (230 cases), characterized by a high rate of participation and unserious accidents occurring in treacherous navigating conditions; Group 3: Probably Preventable (134 cases), characterized by a low participation rates and serious accidents occurring in not the worst navigating conditions; and Group 4: Accidents That Should Not Have Happened (345 cases), characterized by zero participation and serious accidents occurring in reasonable navigating conditions. Significant marginal participation rates for the marine tracking technology across the four accident clusters (100% for Group 1, 67% for Group 2, 37% for Group 3, and 0% for Group 4) effectively distinguishes between casualty groups. In the subsequent discriminant analysis, three discriminant functions correctly classified 96% of the total accidents, including 100% of Group 1 and Group 4, 90% of Group 2, and 88% of Group 3. The variables contributing most to overall group differentiation were participation in the system, overall system utilization, river stage, traffic level, time and location of accidents. The three discriminant functions were statistically significant, with each individual function accounting for a large relative percentage of the variance between the groups. In order of decreasing discriminating power, the functions could be characterized as (1) System Participation and Utilization, (2) Navigating Conditions, and (3) Time and Place.
authors.affiliation: Department of Management, University of Arkansas at Little Rock, South University 72204-1099, USA.
authors.forename: L A
authors.initials: LA
authors.lastname: Le Blanc
authors.forename: C T
authors.initials: CT
authors.lastname: Rucks
mesh_terms: D000059:Accidents / Q000145:classification / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003195:Computer Communication Networks; D016208:Databases, Factual; D016002:Discriminant Analysis; D005618:Fresh Water; D006801:Humans; D008922:Mississippi / Q000453:epidemiology; D011159:Population Surveillance; D012307:Risk Factors; D012767:Ships; D013997:Time Factors