民航客運(yùn)收益管理預(yù)測技術(shù)研究.pdf

1、分類號；——密級：——UDC：——學(xué)號：鯉8Q3Q!鯉中國民用航空學(xué)院碩士掌位論文民航客運(yùn)收益管理預(yù)測技術(shù)研究研究生姓名：醴導(dǎo)師姓名：壑些富熬撞申請學(xué)位級別管理堂亟學(xué)科專業(yè)名稱壘些鳘堡芏所在院系篷理堂醫(yī)論文答辯日期2Q盟生!旦)旦2005年2月21日中國民用航窄學(xué)院碩十學(xué)位論文AbstractForecasthasbeenoneofthehottopicsinstudyingtherevenuemanagementofairpassen

2、gertransportationItisthebaseofmakingpolicies，suchasfixingaprice，allottingseatsofdifferentclassesBeingoneimportantpartofrevenuemanagement，forecastdirectlyaffectsitseriectiveness，tosomeextentThethesisemphasizestheanalysisa

3、ndstudyoftheforecastmodelsandmethodsFirstitintroducesthehistoryandpresentconditionsofpredictingtechniquesontherevenuemanagement，andthenittalksabouttheforecastsubject，objectandbasisThesubjectiswhotopredict，thatisthepredic

4、tingstafffromairlinesonrevenuemanagementTheobjectisthepredictingobjectsofstudythatisthepassengerdemandsfordifferentflightsandtimetableThethesisanalyzesthecharactersandaffectingfactorsofpassengersdemands，moreoveritstudies

5、theservicesfromairlinesatreservation，aswellasthebehaviorcharactersofairpassengersandtherelationsbetweenultimatereservationsandcurrentreservationsThetheoreticalbasisofforecastisthereservationcurve，thatisthedataanalysisofr

6、eservationAccordingtothedetailedanalysisofdataonrevenuemanagement，itintroducesthebasicdatastructuresandsystemsettingsthatguaranteetheoperationofforecastmodelsOnpredictingtechnologyitstressesshort—termmacroquantitativeana

7、lysisItdescribesthetechniquessuchastimeseriesmodel，linearregressionmodel，pickupmodel，PROSforecastingmodels，thatispopularlyusedinairlinesSomeresearchresultshavebeenputinthethesis：Itdescribesallthestagesofforecastprocess，w

8、hichistheindispensableparttealizethepredictingpoliciesofrevenuemanagementItsystematicallydescribesthemethodstopredictthedemandsofrevenuemanagement，aswellasthecharactersandapplicationrequirementsofeachmethodsItmakeconclus

9、ionsandadvicetoadvancethemodelsItputsupsuggestionsthatrevenuemanagementbeforecastedbyclasses，andmakesomeadvicetoenhancethepredictingmodelskeywords：revenuemanagement；forecastingtechniques；demand；reservationdata；unconstrai