Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks Oña López, Juan José De Mujalli, Randa Oqab Calvo Poyo, Francisco Javier Bayesian networks Injury severity Traffic accidents Classification Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference. 2013-04-08T09:10:30Z 2013-04-08T09:10:30Z 2011 info:eu-repo/semantics/article de Oña, J.; Mujalli, R.O.; Calvo, F.J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis and Prevention 43(1): 402–411 (2011). [http://hdl.handle.net/10481/24401] 0001-4575 doi: 10.1016/j.aap.2010.09.010 http://hdl.handle.net/10481/24401 eng http://dx.doi.org/10.1016/j.aap.2010.09.010 info:eu-repo/semantics/openAccess Elsevier