Data-driven early warning method for sudden large passenger flow in urban rail transit stations

An urban rail transit, data-driven technology, applied in data processing applications, neural learning methods, alarms, etc., to achieve the effects of stable detection results, strong implementability, and strong scene adaptability

Active Publication Date: 2021-08-17
BEIJING JIAOTONG UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, there is no early warning method for sudden large passenger flow based on the passenger flow characteristics of urban rail transit and adapting to the dynamic incremental data environment in the prior art

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Data-driven early warning method for sudden large passenger flow in urban rail transit stations
  • Data-driven early warning method for sudden large passenger flow in urban rail transit stations
  • Data-driven early warning method for sudden large passenger flow in urban rail transit stations

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0057] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a data-driven early warning method for sudden large passenger flow at urban rail transit stations. Including: extracting minute-level time-domain signals of station passenger flow from multi-source AFC data, using minute-level time-domain signals to count sliding time window feature parameters that conform to the time-varying characteristics of passenger flow signals; training signal classifiers based on deep belief networks , extract the high-order hidden features of the passenger flow signal, and divide the passenger flow signal into an adaptive mode; calculate the local anomaly factor value of the passenger flow signal in the sliding time window according to the sliding time window characteristic parameters and the adaptive mode division result of the passenger flow signal, when the local anomaly factor When the value is greater than the set large passenger flow detection limit, a large passenger flow risk warning will be issued. The invention is driven by multi-source automatic fare collection data, can adapt to a dynamic incremental data environment, has the technical advantages of strong implementability and stable detection results, and provides scientific method support for urban rail transit passenger flow early warning.

Description

technical field [0001] The invention relates to the technical field of urban rail transit. More specifically, it relates to a data-driven early warning method for sudden large passenger flow at urban rail transit stations. Background technique [0002] With the continuous expansion of urban rail transit networks in major cities in my country, rail transit has become the backbone of urban comprehensive transportation systems, undertaking the task of meeting the diverse travel needs of passengers. The passenger flow demand of the urban rail transit system is significantly affected by external factors of the city, especially when large-scale cultural and sports activities, trade exhibitions, ground traffic control, and severe weather occur. When a sudden large passenger flow occurs, a large number of passengers flood into the station in a short period of time. If the warning is not given in time, it will cause overcrowding in the payment area of ​​the station, a sudden increas...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06Q50/26G08B21/18
CPCG06N3/084G06Q50/26G08B21/182G06N3/045G06F2218/20G06F2218/08G06F18/2414
Inventor 姚恩建郇宁张金萌
Owner BEIJING JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap