Short-term　traffic　flow　prediction　with　high　accuracy　and　efficiency　plays　an　important　role　in　Intelligent　Transportation　Systems,　which　is　a　prerequisite　for　traffic　guidance,　management,　and　control.　Due　to　the　time-varying　and　non-stationary　characteristics　of　the　dynamic　change　of　traffic　flow,　it　is　difficult　to　predict　traffic　flow　with　high　accuracy,　which　needs　to　be　resolved　urgently　in　the　transportation　field.　In　order　to　improve　the　accuracy　and　efficiency　of　short-term　traffic　flow　prediction,　the　paper　develops　a　short-term　traffic　flow　predicting　algorithm　based　on　adaptive　time　slice　and　the　improved　KNN　model　(A-TS-KNN),　which　is　then　implemented　successfully　in　short-term　traffic　flow　predicting　experiments.　In　the　first,　the　Dynamic　Time　Warping　(DTW)　algorithm　is　used　to　dynamically　slice　the　daytime　sequence　of　traffic　flow　into　different　traffic　patterns.　Secondly,　the　mutual　information　method　is　used　to　solve　the　maximum　threshold　of　the　time　delays　of　traffic　flow　at　each　time　in　different　traffic　patterns.　Then　the　traffic　flow　state　vectors　of　different　time　delays　is　constructed,　which　generates　a　history　database　of　traffic　flow.　Thirdly,　the　method　of　ten　times　ten-fold　cross-validation　is　used　to　solve　the　orthogonal　error　distribution　of　different　time　delays　and　K　values　of　traffic　flow　at　each　time.　The　orthogonal　result　with　the　smallest　error　is　selected,　and　the　parameters　combination　of　adaptive　time　delay　and　K　value　are　obtained.　In　the　end,　the　weighted　value　of　the　reciprocal　Euclidean　distance　of　the　K　most　similar　neighbors　is　used　for　predicting　traffic　flow　of　next　time.　The　forecasting　accuracies　of　the　improved　A-TS-KNN　and　other　four　models　including　K-Nearest　Neighbors　(KNN)　model,　Support　Vector　Regression　(SVR)　model,　Long-Short　Term　Memory　(LSTM)　neural　networks,　and　Gate　Recurrent　Unit　(GRU)　neural　networks　are　compared.　The　experimental　results　indicate　that　the　improved　A-TS-KNN　model　is　more　appropriate　for　short-term　traffic　flow　forecasting　than　the　other　models.　In　addition,　the　A-TS-KNN　algorithm　is　used　for　short-term　traffic　flow　predicting　at　other　four　different　intersections　in　the　urban　road　network　of　Fuzhou,　which　has　been　shown　good　generalization　ability.　©　2022,　Science　Press.　All　right　reserved.