The　causes　of　traffic　violations　by　elderly　drivers　are　different　from　those　of　other　age　groups.　To　reduce　serious　traffic　violations　that　are　more　likely　to　cause　serious　traffic　crashes,　this　study　divided　the　severity　of　traffic　violations　into　three　levels　(i.e.,　slight,　ordinary,　severe)　based　on　point　deduction,　and　explore　the　patterns　of　serious　traffic　violations　(i.e.,　ordinary,　severe)　using　multi-source　data.　This　paper　designed　an　interpretable　machine　learning　framework,　in　which　four　popular　machine　learning　models　were　enhanced　and　compared.　Specifically,　adaptive　synthetic　sampling　method　was　applied　to　overcome　the　effects　of　imbalanced　data　and　improve　the　prediction　accuracy　of　minority　classes　(i.e.,　ordinary,　severe);　multi-objective　feature　selection　based　on　NSGA-II　was　used　to　remove　the　redundant　factors　to　increase　the　computational　efficiency　and　make　the　patterns　discovered　by　the　explainer　more　effective;　Bayesian　hyperparameter　optimization　aimed　to　obtain　more　effective　hyperparameters　combination　with　fewer　iterations　and　boost　the　model　adaptability.　Results　show　that　the　proposed　interpretable　machine　learning　framework　can　significantly　improve　and　distinguish　the　performance　of　four　popular　machine　learning　models　and　two　post-hoc　interpretation　methods.　It　is　found　that　six　of　the　top　ten　important　factors　belong　to　multi-scale　built　environment　attributes.　By　comparing　the　results　of　feature　contribution　and　interaction　effects,　some　findings　can　be　summarized:　ordinary　and　severe　traffic　violations　have　some　identical　influencing　factors　and　interactive　effects;　have　the　same　influencing　factors　or　the　same　combinations　of　influencing　factors,　but　the　values　of　the　factors　are　different;　have　some　unique　influencing　factors　and　unique　combinations　of　influencing　factors.