A congestion phenomenon in a transit station could lead to low transfer efficiency as well as a hidden danger. Effective management of congestion phenomenon shall help to reduce the efficiency decline and danger risk. However, due to the difficulty in acquiring microcosmic pedestrian density, existing researches lack quantitative indicators to reflect congestion degree. This paper aims to solve this problem. Firstly, platform, stair, transfer tunnel, auto fare collection (AFC) machine, and security check machine were chosen as key traffic facilities through large amounts of field investigation. Key facilities could be used to reflect the passenger density of a whole station. Secondly, the pedestrian density change law of each key traffic facility was analyzed using pedestrian simulation, and the load degree calculating method of each facility was defined, respectively, afterwards. Taking pedestrian density as basic data and gray clustering evaluation as algorithm, an index called Transit Station Congestion Index (TSCI) was constructed to reflect the congestion degree of transit stations. Finally, an evaluation demonstration was carried out with five typical transit transfer stations in Beijing, and the evaluation results show that TSCI can objectively reflect the congestion degree of transit stations. 1. Introduction Urban rail transit is one of the most important public transport modes. Transit stations, especially transfer stations, suffer from large passenger flow pressure during peak hours, and congestion phenomenon often occurs. It is easy to understand that congestion phenomenon in transit transfer stations could lead to low transfer efficiency, as well as hidden danger of passenger security. Thus, effective management of congestion phenomenon shall help to reduce the efficiency decline and danger risk. However, due to the difficulty in acquiring microcosmic pedestrian density, most of the existing researches lack quantitative indicators to reflect congestion degree. In order to quantify congestion degree, this paper puts forward a concept of Transit Station Congestion Index (TSCI) according to Global Port Congestion Index (GPCI). GPCI is published weekly to detailedly and timely reflect the retention situation of coal, ore, and other dry bulk fleets in major ports around the world, for the purpose of analyzing the influence of port congestion on the supply and demand of dry bulk market. Up to now, GPCI has covered 80 major ports from different countries, including Australia, Brazil, China, India, and South Africa. Just like GPCI, TSCI could help
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