With　the　increasing　proliferation　of　position　technologies,　there　comes　huge　volumes　of　trajectory　data,　which　are　used　in　many　modern　applications　such　as　path　planning　and　location　based　services.　Accurate　road　network　matching　can　improve　the　service　quality　of　these　new　applications.　However,　the　low　sampling　trajectories　bring　a　major　challenge　for　map-matching.　This　paper　studies　the　problem　of　matching　individual　law-sampling　trajectory　to　a　dynamic　multi-criteria　road　network　based　on　user＇s　driving　preferences.　First,　a　driving　preference　model　in　the　dynamic　road　traffic　network　is　proposed.　Based　on　this　model,　a　two-stage　map-matching　algorithm　is　developed.　While　local　matching　searches　multiple　local　likely　Skyline　paths,　a　global　matching　dynamic　programming　algorithm　is　designed　and　the　most　probable　k　global　paths　are　selected　as　the　matching　result.　Experiments　show　that　the　proposed　method　is　effective　and　efficient.　©　Copyright　2018,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.