Short-term　traffic　state　prediction　has　become　an　integral　component　of　an　advanced　traveler　information　system　(ATIS)　in　intelligent　transportation　systems　(ITS).　Accurate　modeling　and　short-term　traffic　prediction　are　quite　challenging　due　to　its　intricate　characteristics,　stochastic,　and　dynamic　traffic　processes.　Existing　works　in　this　area　follow　different　modeling　approaches　that　are　focused　to　fit　speed,　density,　or　the　volume　data.　However,　the　accuracy　of　such　modeling　approaches　has　been　frequently　questioned,　thereby　traffic　state　prediction　over　the　short-term　from　such　methods　inflicts　an　overfitting　issue.　We　address　this　issue　to　accurately　model　short-term　future　traffic　state　prediction　using　state-of-the-art　models　via　hyperparameter　optimization.　To　do　so,　we　focused　on　different　machine　learning　classifiers　such　as　local　deep　support　vector　machine　(LD-SVM),　decision　jungles,　multi-layers　perceptron　(MLP),　and　CN2　rule　induction.　Moreover,　traffic　states　are　evaluated　using　traffic　attributes　such　as　level　of　service　(LOS)　horizons　and　simple　if-then　rules　at　different　time　intervals.　Our　findings　show　that　hyperparameter　optimization　via　random　sweep　yielded　superior　results.　The　overall　prediction　performances　obtained　an　average　improvement　by　over　95%,　such　that　the　decision　jungle　and　LD-SVM　achieved　an　accuracy　of　0.982　and　0.975,　respectively.　The　experimental　results　show　the　robustness　and　superior　performances　of　decision　jungles　(DJ)　over　other　methods.