Rail transit new line access passenger flow prediction method based on data driving

A technology for rail transit and passenger flow, applied in forecasting, data processing applications, instruments, etc., which can solve the problems of high survey cost and low forecasting accuracy.

Pending Publication Date: 2021-07-06
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] Based on this, it is necessary to address the above-mentioned technical problems and provide a data-driven method for pred

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  • Rail transit new line access passenger flow prediction method based on data driving
  • Rail transit new line access passenger flow prediction method based on data driving
  • Rail transit new line access passenger flow prediction method based on data driving

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Embodiment Construction

[0050]In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0051] In one embodiment, such as figure 1 As shown, a data-driven method for predicting the passenger flow of new rail transit lines is provided, including the following steps:

[0052] Step S220, acquiring rail transit station characteristic data and rail transit station passenger flow data of the target rail station.

[0053] Wherein, the target rail station is the predicted rail transit station. The characteristic data of rail transit stations include land use-related data, station connection design-related data, and station accessibility-related data. Land use-relat...

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Abstract

The invention relates to a rail transit new line access passenger flow prediction method based on data driving. The method comprises the following steps: acquiring rail transit station characteristic data and rail transit station passenger flow data of a target rail station; performing quantitative processing on the rail transit station characteristic data to obtain a station characteristic quantitative index of the target rail station; performing statistical analysis on the rail transit station passenger flow data to obtain a station passenger flow characteristic index of a target rail station; taking the station characteristic quantitative index as an independent variable set, taking the station passenger flow characteristic index as a dependent variable set, and constructing a passenger flow total quantity prediction model and a passenger flow fluctuation form prediction model; performing parameter calibration on the model by adopting a regression analysis method, and determining calibrated parameters of the prediction model; and enabling the passenger flow total quantity prediction model and the passenger flow fluctuation form prediction model to perform prediction according to parameters calibrated by the prediction model to obtain the passenger flow total quantity and the fluctuation form of the target rail station, so that the problems of high investigation cost and low prediction precision are solved.

Description

technical field [0001] The present application relates to the field of road traffic technology, and in particular to a data-driven method for predicting passenger flow on new rail transit lines. Background technique [0002] The construction cost of urban rail transit is high and the construction period is long. It is difficult for a city to build a large-scale rail transit network at one time. Therefore, the development of rail transit in each city is accompanied by the gradual expansion of the road network. Whenever a new line (new line refers to a newly opened rail transit line) is connected to the network and put into operation, the connection of the new line will change the topology of the original urban rail transit network, and the increase in station density will make travelers There are various choices of adjacent stations, which affect the travel choice behavior of travelers, and the distribution of passenger flow online also changes accordingly. In order to bette...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/28
CPCG06Q10/04G06Q50/28
Inventor 张宁李嘉雯何铁军
Owner SOUTHEAST UNIV
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