The　behavior　of　waiting　area　choice　of　passengers　at　the　subway　platform　can　directly　affect　the　travel　efficiency,　thereby　affecting　the　service　level　of　the　station.　Combining　fuzzy　logic　theory　and　cellular　automata　model/social　force　model,　a　waiting　area　choice　model　for　passengers　at　the　subway　platform　is　proposed,　which　can　be　used　to　predict　the　distribution　of　passengers.　The　distance　from　passengers　to　waiting　areas,　the　number　of　passengers　in　the　waiting　area,　and　the　length　of　the　remaining　waiting　area　are　selected　as　the　main　influence　factors　after　the　analysis　of　field　investigation,　which　are　defined　as　the　input　variables　of　the　fuzzy　logic　system.　The　choice　probability　of　passengers　to　each　waiting　area　is　defined　as　the　output　variable.　The　waiting　area　with　the　highest　probability　is　taken　as　the　destination　of　passengers,　thereby　the　static　field　value　in　the　cellular　automata　model　and　the　desired　direction　in　the　social　force　model　can　be　determined.　The　total　error　of　the　established　method　can　be　as　low　as　10.13%,　which　shows　a　relatively　good　prediction　effect.　The　effects　of　passenger　attributes,　stair　attributes,　and　passable　area　size　on　the　waiting　area　choice　behavior　are　simulated.　The　results　show　that　the　influence　degree　of　passenger　companion　behavior　on　passenger　distribution　depends　much　on　the　proportion　of　passengers　in　the　company.　The　body　radius　and　the　desired　speed　of　passengers　have　a　great　impact　on　the　distribution　balance　of　waiting　passengers　based　on　the　analysis　of　the　Gini　coefficient　and　random　forest　method.　The　interval　of　multiple　stairs　should　be　the　same　as　far　as　possible.　The　setting　of　a　passable　area　is　conducive　to　the　flow　of　passengers　during　peak　hours.　The　number　of　passengers　is　most　pronounced　in　the　distribution　balance　of　passengers　by　comparing　five　different　influence　factors.　Guidance　strategies　can　be　released　through　the　LED　display　screen　on　the　basis　of　the　predicted　distribution　information.　This　paper　provides　a　new　idea　for　modeling　the　waiting　area　choice　behavior　of　passengers,　which　lays　a　theoretical　basis　for　the　design　and　transformation　of　platform　facilities.　©　2022　Elsevier　Ltd