In cellular mobile networks due to the emerge of different application services, the datarntraffic generated by these application services is increasing. Moreover, the traffic consumptionrnpatternrnisrnchangingrninrntimernandrnspace.rnUnderstandingrnandrncharacterizingrnthernrntrafficrndemandsrnandrndynamicsrnassociatedrnwithrndifferentrnmobilernservicesrn(e.g.,rnknowingrnrnwhenrnandrnwherernusersrnuserndominantrnapplicationrnservices)rnisrninstrumentalrnforrnoperators.rnrnrnByrndoingrnso,rnoperatorsrncanrnimprovernapplicationrnservicesrnusability,rnoptimizingrnnetworkrnrnservicernqualityrnandrnusernitrnasrnanrninputrnforrntechnicalrnandrnbusinessrnstrategies.rnInrntherncasernofrnrnethiorntelecom,rnthernsolerntelecomrnservicernproviderrninrnEthiopia,rnknowledgernofrnthernapplicationrnrnservicesrndistributionrnisrnnot arnknownrnpractice.rnrnTherefore,rnrnthe main purpose of this thesis work is modeling the spatial and temporalrntraffic distribution of observed application services for Addis Ababa Universal MobilernTelecommunication System (UMTS) network. In this regard, to study a temporal andrnspatial distribution analysis of the traffic density (the traffic load per unit area) for selectedrnmobilernapplicationrnservices,rndatarntrafficrnisrncollectedrnfrom 739rnbasesrnStationsrn(BSs).rn rnTo model the temporal distribution of the selected application services, which are Streaming,rnSocialrnNetworksrnandrnWebrnbrowsing,rnrnfour candidate models: Normal, Lognormal,rnWeibull and Stable are used. Based on maximum log-likelihood and probability plot evaluationrnrncriteria, out of four models, a Stable distribution modeling best fit for thisrnparticular application services. Similarly, to model spatial distribution, out of the candidaternfourrnmodels,rnWeibullrndistributionrnmodelrnbestrnfitsrnforrnthesernparticularrnservices.