Subway short-time passenger flow prediction method based on machine learning

A technology of machine learning and prediction methods, applied in the field of transportation, can solve the problems of complex models, low accuracy of prediction results, and high data quality requirements, and achieve the effect of high prediction accuracy

Active Publication Date: 2017-10-24
CENT SOUTH UNIV
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AI Technical Summary

Problems solved by technology

[0004] 1. Traditional forecasting methods, the forecasting results are not accurate
[0005] 2. The existing models with high predic

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  • Subway short-time passenger flow prediction method based on machine learning
  • Subway short-time passenger flow prediction method based on machine learning
  • Subway short-time passenger flow prediction method based on machine learning

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specific Embodiment approach

[0034] like figure 1As shown, the research area of ​​this embodiment, that is, the location interval to be counted, is selected as Shenzhen City, and the records that do not meet the conditions are cleaned out by filtering the Shenzhen metro card data, and assuming that each pair of ODs travels according to the shortest path, and finds out that each pair of OD travels according to the shortest path Daily OD distribution, according to the statistics of the passenger flow of the subway section in the unit time window and the passenger flow of the subway in and out of the station, the Shenzhen subway passenger flow network is generated. The start and end times of the records are October 1, 2014 and December 31, 2014, respectively. In 2014, there were 118 subway stations and 252 sections in Shenzhen Metro. Select all passenger flows in October as historical data, select features through recursive feature elimination algorithm, and establish a regression prediction model. In Octob...

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Abstract

The invention discloses a subway short-time passenger flow prediction method based on machine learning. On the basis of subway card shooting data, all passengers are assumed to travel according to the shortest route, and the flow of all intervals and in all stations is counted in a unit time window; subway station passenger flow in the unit time window serves as nodes, subway interval passenger flow in the unit time window serves as the weight of the edge, and a subway passenger flow network is built; features whose influences are most important to a single target interval are selected out to be brought into a follow-up regression prediction model. The recursive feature elimination algorithm is used for completing feature selection, and important features of the target interval in a target time window are selected out. The regression prediction model is built through the gradient boosted regression tree method, and subway short-time passenger flow prediction is achieved. High prediction precision can be achieved through the method under the condition that a data source is simplex. The regression prediction model is built through historical data and combined with real-time data to predict the subway short-time passenger flow, and help is provided for design optimization of urban rail transit operation marshalling.

Description

technical field [0001] The invention belongs to the technical field of transportation, and in particular relates to a method for predicting short-term passenger flow of subways based on machine learning. Background technique [0002] With the acceleration of urbanization in our country, urban traffic problems are becoming more and more prominent, and the urban rail transit system is the fundamental way to solve the public transportation in large and medium-sized cities, which is relatively closed and has a large number of people. Real-time and accurate rail transit passenger flow prediction is crucial to the optimization of urban rail transit operation formation design. This study proposes a method of combining the historical passenger flow data of the subway with real-time information, and predicting the short-term burst passenger flow of the subway with the help of machine learning. The study found that the prediction based on the combination of historical passenger flow ...

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Application Information

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IPC IPC(8): G06F17/18G06N99/00G06Q10/04
CPCG06F17/18G06N20/00G06Q10/04
Inventor 王璞凌溪蔓
Owner CENT SOUTH UNIV
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