This　study　presents　a　detailed　analysis　on　the　characteristics　of　travel　mode　preference　of　working　residents　living　far　away　from　downtown　area　on　workdays,　using　GPS-based　activity　travel　diary　data　from　Shangdi　area　(Beijing).　A　hybrid　method　integrating　random　parameter　logit　model　with　systematic　heterogeneity　(RPL-SH)　and　Apriori　algorithm　is　put　forward　to　explore　the　influence　factors　and　interaction　effects　affecting　travel　mode　preference.　First,　the　RPL-SH　model　is　established　to　explore　significant　factors,　and　capture　the　unobserved　random　heterogeneity　and　systematic　heterogeneity　due　to　individual　characteristics　on　the　travel　mode　preference.　Then,　these　significant　factors　are　used　to　generate　association　rules　by　Apriori　algorithm　to　investigate　statistical　associations　between　the　specific　travel　mode　preference　and　these　significant　factors.　Ten　significant　factors　are　found　in　the　RPL-SH　model,　in　which　annual　household　income　is　normally　distributed.　The　results　of　the　Apriori　algorithm　indicate　that　some　factors　combined　with　other　factors　could　significantly　influence　working　residents＇　travel　mode　preference.　For　example,　the　combination　of　lower　annual　household　income　and　shorter　distance　between　workplace　and　the　nearest　bus　stop　is　highly　associated　with　green　travel　mode　preference.　Moreover,　the　results　show　that　the　proposed　hybrid　method　not　only　demonstrates　the　consistency　of　the　results　of　the　two　methods,　but　also　plays　a　complementary　role　in　exploring　more　information　on　travel　mode　preference.　This　research　hopes　to　give　regulators　a　better　understanding　on　how　working　residents　living　far　away　from　downtown　area　choose　their　travel　mode,　so　as　to　develop　more　effective　and　targeted　measures　for　reducing　private　car　use　and　alleviating　workday　traffic　congestion.