Riding　comfort　is　an　important　index　to　measure　the　quality　of　service　for　railways,　especially　for　congested　urban　rail　transit　systems　where　the　majority　of　passengers　cannot　find　a　seat.　Existing　studies　usually　employ　the　value　of　longitudinal　acceleration　as　the　key　indicator　to　evaluate　the　riding　comfort　of　vehicles,　while　there　is　no　validated　mathematical　models　to　evaluate　the　riding　comfort　of　urban　rail　trains　from　the　perspective　of　passengers.　This　paper　aims　to　employ　the　collected　longitudinal　acceleration　data　and　passengers＇　feedback　data　in　Beijing　subway　to　qualitatively　measure　and　validate　the　riding　comfort　of　transit　trains.　First,　we　develop　four　regular　fuzzy　sets　based　comfort　measurement　models,　where　the　parameters　of　the　fuzzy　sets　are　determined　by　experiences　of　domain　experts　and　the　field　data.　Then　a　combinational　model　is　given　by　averaging　the　four　regular　fuzzy　set　models　to　elaborate　a　comprehensive　measurement　for　the　riding　comfort.　In　order　to　verify　the　developed　models,　we　conducted　a　questionnaire　survey　in　Beijing　subway.　The　surveyed　riding　comfort　data　from　passengers　and　the　measured　acceleration　data　are　used　to　validate　and　optimize　the　proposed　models.　Two　key　parameters　are　deduced　to　describe　all　parameters　in　the　fuzzy　set　models　and　a　meta-heuristic　algorithm　is　applied　to　optimize　the　parameters　and　weight　coefficients　of　the　combinational　model.　Comparing　the　collected　comfort　data　with　the　comfort　levels　and　values　calculated　by　different　models　shows　that　the　averaging　model　is　better　than　any　regular　fuzzy　set　model.　Furthermore,　the　optimized　model　is　better　than　the　averaging　model　and　provides　the　best　accuracy　and　robustness　for　riding　comfort　measurement.　The　models　provided　in　this　paper　offer　an　optional　way　to　measure　the　riding　comfort　for　further　assessment　and　more　comprehensively　tuning　of　train　control　systems.　©　2020　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.