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.
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