Traffic　flow　is　one　of　the　fundamental　parameters　for　traffic　analysis　and　planning.　With　the　rapid　development　of　intelligent　transportation　systems,　a　large　number　of　various　detectors　have　been　deployed　in　urban　roads　and,　consequently,　huge　amount　of　data　relating　to　the　traffic　flow　are　accumulatively　available　now.　However,　the　traffic　flow　data　detected　through　various　detectors　are　often　degraded　due　to　the　presence　of　a　number　of　missing　data,　which　can　even　lead　to　erroneous　analysis　and　decision　if　no　appropriate　process　is　carried　out.　To　remedy　this　issue,　great　research　efforts　have　been　made　and　subsequently　various　imputation　techniques　have　been　successively　proposed　in　recent　years,　among　which　the　k　nearest　neighbour　algorithm　(kNN)　has　received　a　great　popularity　as　it　is　easy　to　implement　and　impute　the　missing　data　effectively.　In　the　work　presented　in　this　paper,　we　firstly　analyse　the　stochastic　effect　of　traffic　flow,　to　which　the　suffering　of　the　kNN　algorithm　can　be　attributed.　This　motivates　us　to　make　an　improvement,　while　eliminating　the　requirement　to　predefine　parameters.　Such　a　parameter-free　algorithm　has　been　realized　by　introducing　a　new　similarity　metric　which　is　combined　with　the　conventional　metric　so　as　to　avoid　the　parameter　setting,　which　is　often　determined　with　the　requirement　of　adequate　domain　knowledge.　Unlike　the　conventional　version　of　the　kNN　algorithm,　the　proposed　algorithm　employs　the　multivariate　linear　regression　model　to　estimate　the　weights　for　the　final　output,　based　on　a　set　of　data,　which　is　smoothed　by　a　Wavelet　technique.　A　series　of　experiments　have　been　performed,　based　on　a　set　of　traffic　flow　data　reported　from　serval　different　countries,　to　examine　the　adaptive　determination　of　parameters　and　the　smoothing　effect.　Additional　experiments　have　been　conducted　to　evaluate　the　competent　performance　for　the　proposed　algorithm　by　comparing　to　a　number　of　widely-used　imputing　algorithms.　©　2020　Warsaw　University　of　Technology.　All　rights　reserved.