Traffic　violations　usually　caused　by　aggressive　driving　behavior　are　often　seen　as　a　primary　contributor　to　traffic　crashes.　Violations　are　either　caused　by　an　unintentional　or　deliberate　act　of　drivers　that　jeopardize　the　lives　of　fellow　drivers,　pedestrians,　and　property.　This　study　is　aimed　to　investigate　different　traffic　violations　(overspeeding,　wrong-way　driving,　illegal　parking,　non-compliance　traffic　control　devices,　etc.)　using　spatial　analysis　and　different　machine　learning　methods.　Georeferenced　violation　data　along　two　expressways　(S308　and　S219)　for　the　year　2016　was　obtained　from　the　traffic　police　department,　in　the　city　of　Luzhou,　China.　Detailed　descriptive　analysis　of　the　data　showed　that　wrong-way　driving　was　the　most　common　violation　type　observed.　Inverse　Distance　Weighted　(IDW)　interpolation　in　the　ArcMap　Geographic　Information　System　(GIS)　was　used　to　develop　violation　hotspots　zones　to　guide　on　efficient　use　of　limited　resources　during　the　treatment　of　high-risk　sites.　Lastly,　a　systematic　Machine　Learning　(ML)　framework,　such　as　K　Nearest　Neighbors　(KNN)　models　(using　k　=　3,　5,　7,　10,　and　12),　support　vector　machine　(SVM),　and　CN2　Rule　Inducer,　was　utilized　for　classification　and　prediction　of　each　violation　type　as　a　function　of　several　explanatory　variables.　The　predictive　performance　of　proposed　ML　models　was　examined　using　different　evaluation　metrics,　such　as　Area　Under　the　Curve　(AUC),　F-score,　precision,　recall,　specificity,　and　run　time.　The　results　also　showed　that　the　KNN　model　with　k　=　7　using　manhattan　evaluation　had　an　accuracy　of　99%　and　outperformed　the　SVM　and　CN2　Rule　Inducer.　The　outcome　of　this　study　could　provide　the　practitioners　and　decision-makers　with　essential　insights　for　appropriate　engineering　and　traffic　control　measures　to　improve　the　safety　of　road-users.