This　paper　presents　a　novel　approach　to　one-step-forward　prediction　of　traffic　flow　based　on　fuzzy　reasoning.　The　successful　construction　of　a　competent　fuzzy　inference　system　of　Sugeno　type　largely　relies　on　proper　choice　of　input　dimension　and　accurate　estimation　of　structure　parameters　and　rules.　The　first　issue　is　addressed　with　a　proposed　method,　based　on　delta-test,　which　can　simultaneously　determine　input　dimension　and　reduce　noise　level.　In　response　to　the　second　issue,　two　clustering　techniques,　based　on　nearest-neighbor　clustering　and　Gaussian　mixture　models,　are　successively　employed　to　determine　the　antecedent　parameters　and　rules,　and　the　estimation　for　the　consequent　parameters　is　achieved　by　the　least　square　estimation　technique.　A　number　of　experiments　have　been　performed　on　the　one-week　data　of　traffic　flow　to　evaluate　the　proposed　approach　in　terms　of　denosing,　prediction　performances,　overfitting,　and　so　forth.　The　experimental　results　have　demonstrated　that　the　proposed　prediction　approach　is　effective　in　removing　noise　and　constructing　a　competent　and　compact　fuzzy　inference　system　without　significant　overfitting.