Short-term　traffic　speed　prediction　is　vital　for　proactive　traffic　control,　and　is　one　of　the　integral　components　of　an　intelligent　transportation　system　(ITS).　Accurate　prediction　of　short-term　travel　speed　has　numerous　applications　for　traffic　monitoring,　route　planning,　as　well　as　helping　to　relieve　traffic　congestion.　Previous　studies　have　attempted　to　approach　this　problem　using　statistical　and　conventional　artificial　intelligence　(AI)　methods　without　accounting　for　influence　of　data　collection　time-horizons.　However,　statistical　methods　have　received　widespread　criticism　concerning　prediction　accuracy　performance,　while　traditional　AI　approaches　have　too　shallow　architecture　to　capture　non-linear　stochastics　variations　in　traffic　flow.　Hence,　this　study　aims　to　explore　prediction　of　short-term　traffic　speed　at　multiple　time-ahead　intervals　using　data　collected　from　loop　detectors.　A　fast　forest　quantile　regression　(FFQR)　via　hyperparameters　optimization　was　introduced　for　predicting　short-term　traffic　speed　prediction.　FFQR　is　an　ensemble　machine　learning　model　that　combines　several　regression　trees　to　improve　speed　prediction　accuracy.　The　accuracy　of　short-term　traffic　speed　prediction　was　compared　using　the　FFQR　model　at　different　data　collection　time-horizons.　Prediction　results　demonstrated　the　adequacy　and　robustness　of　the　proposed　approach　under　different　scenarios.　It　was　concluded　that　prediction　performance　of　FFQR　was　significantly　enhanced　and　robust,　particularly　at　time　intervals　larger　than　5　min.　The　findings　also　revealed　that　speed　prediction　error　(in　terms　of　quantiles　loss)　ranged　between　0.58　and　1.18.