Rail transit large passenger flow track analysis method based on elastic cloud, terminal and storage medium

Abstract

The embodiment of the invention belongs to the technical field of data analysis, and particularly relates to a rail transit large passenger flow track analysis method based on elastic cloud, a terminal and a storage medium, the rail transit large passenger flow track analysis method based on the elastic cloud comprises the following steps of: acquiring real-time multi-source passenger flow data, fitting large passenger flow track information on the basis, subjecting the multi-source passenger flow data to correlation fusion through correlation, comparison and crossing methods, verifying the fitted large passenger flow track information, thus improving the integrity and accuracy of overall data analysis, and performing real-time analysis processing after verification, thus improving the accuracy and precision of generating real-time rail transit large passenger flow track data covering flow density, flow direction and flow velocity. The bottleneck that analysis result errors are too large when single data source analysis is used is broken through, and the human resource investment cost is reduced.

Description

technical field [0001] The embodiments of the present invention belong to the technical field of data analysis, and in particular relate to a method, a terminal and a storage medium based on an elastic cloud-based track analysis method for mass passenger flow in rail transit. Background technique [0002] With the continuous development of my country's urban rail transit network, the network passenger flow is showing a rapid growth trend, and the number and scale of stations are also expanding. Compared with other modes of transportation, the subway passenger flow has the characteristics of small carrying space, high density, complex streamlines, and numerous interweaving points. Especially in large transfer stations, due to the large number of connecting lines and entrances and exits, passenger flow is superimposed and crossed. And other characteristics, leading to increased risks of operating organizations, which puts forward higher requirements for the level of passenger ...

AI Technical Summary

Problems solved by technology

[0003] At present, the passenger flow management of existing urban rail transit stations is mainly through manual monitoring and video-assisted monitoring, and based on the comparison between operating experience and objective reality, to judge the passenger flow situation and take corresponding control measures. These measures are not only accurate and accurate Insufficient in the degree, but also lead to a huge workload of personnel

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