The　air　transport　industry　is　an　important　branch　of　the　intelligent　transportation　system　(ITS).　It　is　widely　admitted　that　modern　ITS　technologies　and　advanced　management　methods,　such　as　fleet　assignment,　aircraft　maintenance　routing,　and　crew　scheduling,　can　significantly　increase　an　airline＇s　market　share　and　profit,　and　also　improve　customer　satisfaction.　This　paper　studies　a　new　airline　stochastic　fleet　assignment　problem　with　random　passenger　demands　under　risk　aversion.　The　objective　is　to　maximize　the　expected　total　profit　at　a　certain　level　of　risk　avoidance　(i.e.,　conditional　value-at-risk).　To　solve　this　problem,　we　present　a　risk-averse　two-stage　stochastic　mixed-integer　programming　model.　The　first　stage　mainly　deals　with　tactic　level　decisions:　assigning　aircraft　families　(e.g.,　Airbus　A380　family)　to　flight　legs.　The　operational　level　decisions　are　made　in　the　second　stage　to　efficiently　assign　aircraft　types　(e.g.,　Airbus　A380-800　or　A380-800F)　to　flight　legs　while　meeting　the　family　assignment　plan　developed　in　the　first　stage.　Then,　a　sample　average　approximation　algorithm　is　proposed　to　solve　the　stochastic　programming　problem　considering　risk　aversion.　A　realistic　international　airline＇s　numerical　experiment　is　conducted　to　illustrate　the　efficiency　of　the　proposed　two-stage　stochastic　programming　model　and　algorithm.