It　is　assumed　that　travelers　adjust　departure　time　and　route　choices　according　to　updating　historical　information.　With　considering　traveler＇s　bounded　rationality,　the　day-to-day　dynamic　models　of　network　traffic　flow　are　developed　based　on　the　cumulative　prospect　theory.　The　proposed　models　reveal　how　travelers　make　departure　time　and　route　choices,　update　the　time　interval　weight,　and　the　route　travel　time　distribution　function.　Numerical　experiments　on　a　grid　network　show　that　the　reliance　on　historical　perceived　travel　time　have　no　significant　effect　on　the　stable　flow　distribution　of　each　departure　time　interval,　but　have　significant　effect　on　network　traffic　flow　evolution.　When　the　reliance　on　historical　perceived　travel　time　is　greater　than　or　equal　to　0.90,　the　network　traffic　flow　considering　departure　time　choice　will　eventually　reach　a　steady　state.　From　the　perspective　of　the　overall　efficiency　of　the　network　system,　when　the　reliance　on　historical　perceived　travel　time　is　within　[0.90,　0.95],　the　boundedly　rational　traffic　assignment　will　be　better　than　the　perfectly　rational　one.　Copyright　©　2017　by　Science　Press.