It　is　difficult　to　monitor　crowded　passengers　in　comprehensive　transportation　hubs　due　to　complex　facility　layout　and　high　passenger　flow.　Artificial　identification　is　still　the　common　method　to　find　out　passenger　congestion　and　other　unusual　event.　Thus,　in　order　to　improve　the　efficiency　of　passenger　monitoring,　it　is　necessary　to　identify　key　nodes　which　are　need　to　be　monitored　to　support　the　decision　making　of　monitoring　equipments　configuration　and　operation.　An　operational　strategy　for　key　monitoring　nodes　identification　using　Grey　Relational　Analysis　(GRA)　was　presented.　Passenger　facilities　were　divided　into　four　types　to　create　potential　monitoring　nodes　diagram.　And　then,　based　on　the　pedestrian　simulation　tool,　evaluation　indicators　system　of　monitoring　node　importance　was　established.　GRA　algorithm　with　variable　weight　was　used　to　calculate　importance　of　different　potential　monitoring　nodes.　At　last,　the　identification　method　was　illustrated　with　a　case　study　of　a　designing　comprehensive　transportation　hub　in　Beijing.　©　2011　IEEE.