Passenger　travel　flows　of　urban　rail　transit　during　holidays　usually　show　distinct　characteristics　different　from　normal　days.　To　ensure　efficient　operation　management,　it　is　essential　to　accurately　predict　the　distribution　of　holiday　passenger　flow.　Based　on　Automatic　Fare　Collection　(AFC)　data,　this　paper　explores　the　passengers＇　destination　choice　differences　between　normal　days　and　holidays,　as　well　as　one-way　tickets　and　public　transportation　cards,　which　provides　support　for　variable　selection　in　modeling.　Then,　a　forecasting　model　of　holiday　travel　distribution　is　proposed,　in　which　the　destination　choice　model　is　established　for　representing　local　and　nonlocal　passengers.　Meanwhile,　explanatory　variables　such　as　land　matching　degree,　scenic　spot　dummy,　and　level　of　service　variables　are　introduced　to　deal　with　the　particularity　of　holiday　passengers＇　travel　behavior.　The　parameters　calibrated　by　the　improved　weighted　exogenous　sampling　maximum　likelihood　(WESML)　method　are　applied　to　predict　passenger　flow　distribution　in　different　holiday　cases　with　annual　changes　in　the　metro　network,　using　the　data　collected　from　Guangzhou　Metro,　China.　The　results　show　that　the　proposed　model　is　valid　and　performs　better　than　the　other　comparable　models　in　terms　of　forecasting　accuracy.　The　proposed　model　has　the　capability　to　provide　a　more　universal　and　accurate　passenger　flow　distribution　prediction　method　for　urban　rail　transit　in　different　holiday　scenarios　with　network　changes.