详情

原文传递 PREDICTING VEHICLE OCCUPANCIES FROM ACCIDENT DATA: AN ACCIDENT SEVERITY APPROACH.

题名：	PREDICTING VEHICLE OCCUPANCIES FROM ACCIDENT DATA: AN ACCIDENT SEVERITY APPROACH.
作者：	Chang-L-Y; Mannering-FL
关键词：	ACCIDENT-RECORDS; ESTIMATING-; VEHICLE-OCCUPANCY; OVERESTIMATION-; BIAS-; ACCIDENT-SEVERITY; CORRECTIONS-; LOGIT-MODELS; ACCURACY-
摘要：	Past studies have shown that using accident records to estimate vehicle occupancies (i.e., using the observed occupancies of vehicles involved in accidents) results in an overestimation of occupancy. There are a number of possible reasons for this, one of which is that multioccupant accidents are more likely to be reported (i.e., appear in an accident database) because, with more people, the possibility of an injury is greater. The interaction between vehicle occupancy and accident severity is used to develop a method to correct for the occupancy overestimation bias inherent in accident data. A nested logit model of occupancy and severity is estimated, and a correction technique is applied to eliminate biases. The results show that the proposed approach gives accurate predictions of vehicle occupancies using standard accident data.
总页数：	Transportation Research Record. 1998. (1635) pp93-104 (1 Fig., 6 Tab., 11 Ref.)
报告类型：	科技报告