With　the　rise　of　bicycle　sharing　network,　＂shared-bicycle　+　subway＂　and　＂shared-bicycle　+　bus＂　have　become　the　main　mode　of　urban　commuting,　but　the　＂tidal　effect＂　of　shared-bicycle　makes　it　difficult　to　manage　and　deploy　resources.　Therefore,　exploring　the　＂tidal　law＂　of　shared-　bicycle　and　accurately　predicting　the　demand　for　borrowing　and　returning　bicycles　at　parking　areas　(electronic　fences)　are　important　for　the　orderly　and　standardized　development　of　shared-bicycle　and　the　optimization　of　the　riding　experience　and　environment.　Based　on　the　spatial　data　of　shared-bicycle　orders　and　electronic　fences,　our　research　proposes　a　spatial-temporal　model　for　identifying　tidal　shared-bicycle　stops　and　analyzing　their　tidal　spatial-temporal　characteristics.　Our　model　defines　the　tidal　shared-bicycle　stops　as　electric　fences　with　lacking-bike/lacking-parking　due　to　a　large　number　of　shared-bicycles　borrowed/returned　for　a　short　time.　The　electric　fences　are　then　classified　according　to　their　status　at　a　certain　period　and　assigned　different　lacking-bike/lacking-parking　indexes.　The　results　show　that　our　spatial-　temporal　model　can　accurately　identify　the　tidal　shared-　bicycle　stops　at　a　specific　period.　Moreover,　based　on　the　spatial-temporal　data　such　as　shared　bicycle　orders,　city　information　points　(POI),　road,　population,　land-use　type,　temperature,　and　wind　speed,　and　considering　the　correlation　of　electronic　fences　at　the　local　area,　we　propose　a　K　Nearest　Neighbors　(KNN)-　LightGBM　model　to　predict　the　sharing　demand　of　shared　bicycles,　which　includes:　(1)　Principal　Component　Analysis　(PCA)　is　used　to　extract　characteristics;　(2)　The　KNN　algorithm　is　used　to　calculate　the　correlation　information　of　electronic　fences　at　the　local　area;　(3)　We　integrate　the　characteristic　vectors　extracted　by　PCA　and　the　correlation　information　of　electronic　fences　as　input,　and　use　the　LightGBM　model　to　predict　the　sharing　demand　of　bicycles;　(4)　We　evaluate　the　importance　of　the　characteristics　that　affect　the　sharing　demand.　The　results　show　that　the　proposed　KNN-　LightGBM　is　better　than　the　common　machine　learning　methods　in　demand　prediction　at　different　time　scales.　The　mean　values　of　RMSE　and　MAE　using　our　proposed　model　are　the　smallest　and　the　mean　values　of　R2　and　r　are　the　largest.　We　use　the　KNN　algorithm　to　calculate　the　correlation　of　electronic　fences,　which　can　effectively　improve　the　prediction　accuracy.　Compared　with　LightGBM,　the　RMSE　and　MAE　of　KNN-　LightGBM　are　reduced　by　10%　and　11%,　respectively,　and　R2　and　r　are　improved　by　3%　and　4%,　respectively.　Based　on　the　importance　assessment　of　characteristics,　the　historical　data　of　shared-　bicycle　orders　are　the　most　important　for　the　demand　prediction,　followed　by　the　distance　to　the　nearest　public　transportation　stations.　Our　study　demonstrates　the　potential　of　model.　©　2023　Research　Institute　of　Beijing.　All　rights　reserved.