Risky　and　aggressive　driving　maneuvers　are　considered　a　significant　indicator　for　traffic　accident　occurrence　as　well　as　they　aggravate　their　severity.　Traffic　violations　caused　by　such　uncivilized　driving　behavior　is　a　global　issue.　Studies　in　existing　literature　have　used　statistical　analysis　methods　to　explore　key　contributing　factors　toward　aggressive　driving　and　traffic　violations.　However,　such　methods　are　unable　to　capture　latent　correlations　among　predictor　variables,　and　they　also　suffer　from　low　prediction　accuracies.　This　study　aimed　to　comprehensively　investigate　different　traffic　violations　using　spatial　analysis　and　machine　learning　methods　in　the　city　of　Luzhou,　China.　Violations　committed　by　taxi　drivers　are　the　focus　of　the　current　study　since　they　constitute　a　significant　proportion　of　total　violations　reported　in　the　city.　Georeferenced　violation　data　for　the　year　2016　was　obtained　from　the　traffic　police　department.　Detailed　descriptive　analysis　is　presented　to　summarize　key　statistics　about　various　violation　types.　Results　revealed　that　over-speeding　was　the　most　prevalent　violation　type　observed　in　the　study　area.　Frequency-based　nearest　neighborhood　cluster　methods　in　Arc　map　Geographic　Information　System　(GIS)　were　used　to　develop　hotspot　maps　for　different　violation　types　that　are　vital　for　prioritizing　and　conducting　treatment　alternatives　efficiently.　Finally,　different　machine　learning　(ML)　methods,　including　decision　tree,　AdaBoost　with　a　base　estimator　decision　tree,　and　stack　model,　were　employed　to　predict　and　classify　each　violation　type.　The　proposed　methods　were　compared　based　on　different　evaluation　metrics　like　accuracy,　F-1　measure,　specificity,　and　log　loss.　Prediction　results　demonstrated　the　adequacy　and　robustness　of　proposed　machine　learning　(ML)　methods.　However,　a　detailed　comparative　analysis　showed　that　the　stack　model　outperformed　other　models　in　terms　of　proposed　evaluation　metrics.