There　is　a　high　potential　of　severe　injury　outcomes　in　traffic　crashes　on　rural　interstate　highways　due　to　the　significant　amount　of　high　speed　traffic　on　these　corridors.　Hierarchical　Bayesian　models　are　capable　of　incorporating　between-crash　variance　and　within-crash　correlations　into　traffic　crash　data　analysis　and　are　increasingly　utilized　in　traffic　crash　severity　analysis.　This　paper　applies　a　hierarchical　Bayesian　logistic　model　to　examine　the　significant　factors　at　crash　and　vehicle/driver　levels　and　their　heterogeneous　impacts　on　driver　injury　severity　in　rural　interstate　highway　crashes.　Analysis　results　indicate　that　the　majority　of　the　total　variance　is　induced　by　the　between-crash　variance,　showing　the　appropriateness　of　the　utilized　hierarchical　modeling　approach.　Three　crash-level　variables　and　six　vehicle/driver-level　variables　are　found　significant　in　predicting　driver　injury　severities:　road　curve,　maximum　vehicle　damage　in　a　crash,　number　of　vehicles　in　a　crash,　wet　road　surface,　vehicle　type,　driver　age,　driver　gender,　driver　seatbelt　use　and　driver　alcohol　or　drug　involvement.　Among　these　variables,　road　curve,　functional　and　disabled　vehicle　damage　in　crash,　single-vehicle　crashes,　female　drivers,　senior　drivers,　motorcycles　and　driver　alcohol　or　drug　involvement　tend　to　increase　the　odds　of　drivers　being　incapably　injured　or　killed　in　rural　interstate　crashes,　while　wet　road　surface,　male　drivers　and　driver　seatbelt　use　are　more　likely　to　decrease　the　probability　of　severe　driver　injuries.　The　developed　methodology　and　estimation　results　provide　insightful　understanding　of　the　internal　mechanism　of　rural　interstate　crashes　and　beneficial　references　for　developing　effective　countermeasures　for　rural　interstate　crash　prevention.　(C)　2016　Elsevier　Ltd.　All　rights　reserved.