Human　motion　modelling　has　attracted　more　and　more　attentions　in　various　industrial　fields　with　the　event　of　information　technology.　Previous　studies　focus　on　capturing,　animating,　understanding　and　modelling　human　gestures　or　physical　activities.　However,　in　many　applications　such　as　Intelligent　Transportation　Systems　(ITS),　the　traffic　data　quality　(TDQ)　is　becoming　a　critical　issue　which　can　has　great　influence　on　the　efficiency　of　the　modelling.　In　this　paper,　we　focus　on　evaluating　the　traffic　data　quality　(TDQ)　from　the　large　amount　of　detectors　and　traffic　flow　data　in　the　modelling　of　Intelligent　Transportation　Systems　(ITS).　We　first　introduce　four　error　indices　of　an　occupancy　speed　model　and　an　occupancy　flow　model　as　model　evaluation　indices,　and　two　indices　from　experts　as　non-model　evaluation　indices.　Then,　we　propose　a　comprehensive　evaluation　model　(CEM)　for　TDQ.　Furthermore,　we　develop　two　algorithms　for　training　the　parameters　in　CEM　based　on　the　least　square　method　(LSM)　and　the　adaptive　network　based　fuzzy　inference　system　(ANFIS).　We　compare　the　proposed　algorithms　with　the　real-world　traffic　flow　data　which　has　been　collected　on　Beijing　ring-roads　and　connected　lines.　The　experimental　results　show　that　the　ANFIS-based　learning　method　outperforms　in　most　scenarios　and　ensures　the　evaluation　error　less　than　10　%,　which　can　significantly　improve　the　efficiency　of　identifying　traffic　flow　detectors　with　low　data　quality.