چكيده به لاتين
Analyses of vehicle crashes demonstrate that injuries severity is influenced by many factors that can be classified into four main categories of person, vehicle, road and contributing Factor.
In this study, the effect of different array of factors on three vehicle-crash injuries severity are investigated by using classification trees and artificial neural network over data derived from GES data center (2013 to 2015), similarly by using multi-vehicle crashes data of Tehran province in 1390, obtained results are compared with the results from GES crash data.
The results obtained from USA demonstrate that most significant factors influencing three-vehicle-crash injuries severity in more severe injuries are drinking, pre-crash movement, critical event pre-crash, number of occupants, initial contact point, safety belt usage and pre-impact location, moreover in less severe injuries, safety belt usage, pre-crash movement, critical event pre-crash, initial impact point, body type, first harmful event and sitting position are considered as the most significant factors.
The obtained results from Tehran province crash data shows that the most significant factors influencing multi-vehicle crashes are body type, Manner of collision, safety belt usage, road deficiencies and area usage
