This　paper　investigates　the　latent　classes　of　parking　preference　for　drivers　and　the　economic　effects　after　implementing　Parking　Space　Comprehensive　Utilization　(PSC)　in　traditional　business　districts　(TBD),　with　a　particular　focus　on　the　parking　preferences　of　electric　vehicle　users　(EVU).　Firstly,　Exploratory　Factor　Analysis　(EFA)　is　used　to　reduce　dimensionality　and　determine　the　latent　structure.　Then,　based　on　the　Latent　Class　Model　(LCM),　the　customers　are　classified,　and　the　proportion　of　each　class　under　various　latent　variables　is　analyzed.　Finally,　the　paper　conducts　a　quantitative　analysis　of　economic　effects　by　considering　different　psychological　factors　across　different　customer　classes.　With　the　data　obtained　from　revealed　preference　(RP)　and　stated　preference　(SP)　surveys,　this　paper　identifies　the　customers＇　preferences　for　the　three　scenarios　presented.　The　results　show　that　(1)　customers　can　be　classified　into　four　classes:　core　customers　(CCS,　34%),　potential　customers　(PCS,　29%),　regular　customers　(RCS,　22%),　and　marginal　customers　(MCS,　15%),　among　which　EVU　do　not　show　a　significant　preference　for　parking　charging　facilities　in　TBD;　(2)　the　potential　economic　improvements　for　these　four　classes　are:　9%,　12%,　8%,　and　10%;　(3)　CCS　has　the　greatest　potential　to　increase　store　revenue　by　yen　7041,　while　PCS　has　the　greatest　potential　to　increase　store　customer　flow　by　31%.　These　findings　provide　a　valuable　reference　for　decision-making　by　TBD　store　managers.