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Rail transit passenger flow prediction method and system based on passenger travel information

A travel information and rail transit technology, used in forecasting, digital data information retrieval, character and pattern recognition, etc., can solve problems such as data waste, insufficient index system, and deep passenger mining, and achieve the effect of improving accuracy

Active Publication Date: 2022-07-12
BEIJING JIAOTONG UNIV +1
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AI Technical Summary

Problems solved by technology

At present, the passenger travel information of rail transit still has the following deficiencies: the multi-source travel information of passengers has not been deeply excavated, resulting in a large waste of data; the index system of passenger travel information established through data analysis is not perfect, and further excavation is still needed

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  • Rail transit passenger flow prediction method and system based on passenger travel information
  • Rail transit passenger flow prediction method and system based on passenger travel information
  • Rail transit passenger flow prediction method and system based on passenger travel information

Examples

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example

[0122] Example: Take passengers in a subway station as the research object, select the AFC data of three working days on June 6, 7, and 8, 2018 as the basic data, and analyze the travel behavior characteristics of passengers in the station on working days. After data screening, the number of people entering the station for three working days was 197,328.

[0123] Passengers are divided into 5 categories by K-means clustering method. Table 1 below is the cluster center points of the five categories.

[0124]

[0125] Table 1

[0126] Clustering result analysis:

[0127] The proportion of the first category of passengers is 21.2%, and the travel characteristics are that the number of trips in three days is 1.75, which is the category with the highest travel intensity among the five categories. The first travel time is 08:22:13, and the average travel time is 27.7min. The travel distance is not very far, and it conforms to the time period of the morning rush hour. This type...

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Abstract

The invention relates to the technical field of rail transit passenger flow prediction, in particular to a rail transit passenger flow prediction method and system based on passenger travel information, wherein the method includes: acquiring passenger travel data, establishing a passenger travel information index system based on the passenger travel data, and calculating statistical Each index information in the passenger travel information index system; based on the partial index information calculated in the passenger travel information index system, the return passenger flow in different time periods in the station is estimated; the return passenger flow of passengers in the station is used as a covariate Add to the passenger flow prediction model to predict the inbound passenger flow of the station. According to the multi-source travel data of the passengers, the invention mines the travel rules of the passengers, establishes a three-level index information system of the travel information of the passengers on this basis, and realizes the statistical calculation of each index information. At the same time, a prediction method is proposed to identify the return passenger flow based on the passenger travel information, and effectively improve the accuracy of the station's incoming passenger flow according to the return passenger flow.

Description

technical field [0001] The invention relates to the technical field of rail transit passenger flow prediction, in particular to a rail transit passenger flow prediction method and system based on passenger travel information. Background technique [0002] Accurate forecasting of passenger flow demand at stations is critical to the operation of urban subway systems. The previous research mainly regards the passenger flow value of the past several moments as a time series to predict the passenger flow at a certain moment in the future. However, this method basically ignores the travel behavior rules of individual passengers. For example, if a passenger gets off at a subway station in the morning for work, he / she is likely to get back home at the same station in the evening. Existing research shows that it is very necessary to add travel behavior components to the passenger flow forecast time series. According to the concept of user travel information, easy-to-understand, re...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q10/04G06K9/62G06F16/9537G06F16/2458
Inventor 许心越张安忠蔡昌俊刘军
Owner BEIJING JIAOTONG UNIV
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