Individuals　＇daily　route　choice　results,　is　not　only　related　to　the　perceived　route　travel　time,　but　also　the　individuals＇　personal　preference,　travel　inertia　and　other　factors.　In　this　paper,　a　dynamic-updating　mechanism　for　route　preference　based　on　Dogit　model　is　introduced　into　the　traditional　day-to-day　route　choice　model.　Three　different　learning　strategies　on　perceived　route　travel　time　are　proposed,　and　the　effects　of　individual　route　preference　on　day-to-day　route　flow　evolution　track　are　analyzed　and　compared.　The　numerical　results　show　that,　in　contrast　with　the　static　Dogit　model,　the　Logit　model　always　overestimates　the　route　difference　owing　to　ignoring　individuals　＇route　preference,　to　be　specific,　always　overestimates　the　traffic　flow　on　dominant　routes　and　underestimates　the　traffic　flow　on　inferior　routes.　When　the　individuals＇　route　preference　is　dynamically　updated,　the　equilibrium　flow　always　stays　between　that　under　Logit　model　and　static　Dogit　model　with　same　initial　parameters.　Copyright　©　2016　by　Science　Press.