Rear-end　crash　is　one　of　the　most　common　types　of　traffic　crashes　in　the　U.S.　A　good　understanding　of　its　characteristics　and　contributing　factors　is　of　practical　importance.　Previously,　both　multinomial　Logit　models　and　Bayesian　network　methods　have　been　used　in　crash　modeling　and　analysis,　respectively,　although　each　of　them　has　its　own　application　restrictions　and　limitations.　In　this　study,　a　hybrid　approach　is　developed　to　combine　multinomial　logit　models　and　Bayesian　network　methods　for　comprehensively　analyzing　driver　injury　severities　in　rear-end　crashes　based　on　state-wide　crash　data　collected　in　New　Mexico　from　2010　to　2011.　A　multinomial　logit　model　is　developed　to　investigate　and　identify　significant　contributing　factors　for　rear-end　crash　driver　injury　severities　classified　into　three　categories:　no　injury,　injury,　and　fatality.　Then,　the　identified　significant　factors　are　utilized　to　establish　a　Bayesian　network　to　explicitly　formulate　statistical　associations　between　injury　severity　outcomes　and　explanatory　attributes,　including　driver　behavior,　demographic　features,　vehicle　factors,　geometric　and　environmental　characteristics,　etc.　The　test　results　demonstrate　that　the　proposed　hybrid　approach　performs　reasonably　well.　The　Bayesian　network　reference　analyses　indicate　that　the　factors　including　truck-involvement,　inferior　lighting　conditions,　windy　weather　conditions,　the　number　of　vehicles　involved,　etc.　could　significantly　increase　driver　injury　severities　in　rear-end　crashes.　The　developed　methodology　and　estimation　results　provide　insights　for　developing　effective　countermeasures　to　reduce　rear-end　crash　injury　severities　and　improve　traffic　system　safety　performance.　(C)　2015　Elsevier　Ltd.　All　rights　reserved.