Rail transit passenger flow prediction method and system based on passenger travel information
A travel information and rail transit technology, applied in prediction, digital data information retrieval, character and pattern recognition, etc., can solve problems such as data waste, passenger deep mining, and insufficient index system, and achieve the effect of improving accuracy
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[0122] Example: Taking the passengers in a subway station as the research object, the AFC data of the three working days of June 6, 7, and 8, 2018 were selected as the basic data to analyze the travel behavior characteristics of passengers in the station on weekdays. After data screening, the station had 197,328 inbound visitors in three working days.
[0123] Passengers are divided into 5 categories by K-means clustering method. Table 1 below shows the cluster center points of the five categories.
[0124]
[0125] Table 1
[0126] Clustering result analysis:
[0127] The proportion of passengers in the first category is 21.2%. The travel characteristics show that the number of trips within three days is 1.75, which is the highest travel intensity among the five categories. The travel distance is not very far, and it fits the time period of the morning rush hour. This type of passenger can be considered as a standard commuter passenger during the morning rush hour.
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