Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Rail transit space-time short-time passenger flow prediction method, device and equipment and storage medium

A technology of rail transit and forecasting methods, applied in forecasting, biological neural network models, resources, etc., can solve problems such as inability to describe spatial correlation, and achieve the effect of eliminating the impact of dimensions and data value ranges

Pending Publication Date: 2020-10-02
BEIJING JIAOTONG UNIV
View PDF2 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But convolutional neural networks cannot describe the spatial correlation of topologies like subway networks

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
  • Rail transit space-time short-time passenger flow prediction method, device and equipment and storage medium
  • Rail transit space-time short-time passenger flow prediction method, device and equipment and storage medium
  • Rail transit space-time short-time passenger flow prediction method, device and equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0092] Embodiment 1 This embodiment provides a rail transit space-time short-term passenger flow prediction method, such as figure 1 As shown, the method includes:

[0093] S1 obtains the inbound data and train schedule data in the historical time period;

[0094] S2 constructs an adjacency matrix based on travel time according to the train schedule data;

[0095] S3 standardizes the inbound data and the adjacency matrix to obtain a standardized inbound matrix and adjacency matrix;

[0096] S4 adopts the graph convolutional neural network to extract the spatial feature matrix of the inbound matrix and the adjacency matrix after the normalization process;

[0097] S5 uses a sequence-to-sequence model based on a gated recurrent unit and an attention mechanism to extract the time features of the spatial feature matrix to predict the outbound volume at the current moment.

[0098] A sequence-to-sequence model based on gated recurrent units is used to extract temporal features, ...

Embodiment 2

[0099] Embodiment 2 This embodiment provides a rail transit space-time short-term passenger flow prediction method, such as figure 1 As shown, the method includes:

[0100] S1 obtains the inbound data and train schedule data in the historical time period;

[0101] S2 constructs an adjacency matrix based on travel time according to the train schedule data;

[0102] S3 standardizes the inbound data and the adjacency matrix to obtain a standardized inbound matrix and adjacency matrix;

[0103] S4 adopts the graph convolutional neural network to extract the spatial feature matrix of the inbound matrix and the adjacency matrix after the normalization process;

[0104] S5 uses a sequence-to-sequence model based on a gated recurrent unit and an attention mechanism to extract the time features of the spatial feature matrix to predict the outbound volume at the current moment.

[0105] A sequence-to-sequence model based on gated recurrent units is used to extract temporal features, ...

Embodiment 3

[0166] Embodiment 3 This embodiment provides a method for predicting short-term passenger flow in rail transit space-time

[0167] The method includes the following steps:

[0168] S1: Obtain subway outbound passenger flow data at a granularity of 15 minutes according to the AFC system; construct an adjacency matrix based on train schedule data. Standardize the subway outbound passenger flow data and adjacency matrix to obtain the processed outbound matrix and adjacency matrix;

[0169] S2: Input the standardized subway outbound matrix and adjacency matrix data into the spatial feature extraction unit to extract the spatial features of passenger flow;

[0170] S3: Input the data after extracting the spatial features into the time feature extraction unit to extract the time features of the passenger flow;

[0171] S4: Combine the established spatial feature extraction unit and time feature extraction unit to predict, and obtain outbound volume and corresponding evaluation ind...

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 relates to the technical field of passenger flow prediction, and discloses a rail transit space-time short-time passenger flow prediction method, device and equipment and a storage medium. The method comprises the steps of acquiring pull-in data and train timetable data of a historical time period, constructing an adjacency matrix according to the train timetable data; standardizingthe pull-in data and the adjacency matrix; adopting a graph convolutional neural network to extract spatial feature matrixes of the standardized pull-in data and the adjacency matrix; and extracting time features of the spatial feature matrix by adopting a sequence-to-sequence model based on a gating cycle unit and an attention mechanism so as to predict an outbound amount at the current moment. According to the method, the space-time relationship of large-scale passenger flow can be captured, high precision and high interpretability are achieved, the passenger flow distribution situation canbe mastered conveniently, and a basis is provided for passenger flow state analysis and early warning. Meanwhile, passenger flow organization is facilitated, transport capacity resources are reasonably allocated, congestion is relieved, and the service quality is improved.

Description

technical field [0001] The invention relates to the technical field of passenger flow forecasting, in particular to a method, device, equipment and storage medium for rail transit spatiotemporal short-term passenger flow forecasting. Background technique [0002] In recent years, the construction of urban rail transit has continued to increase, and the ever-expanding subway network has stimulated a sharp increase in the number of passengers. As a result, the current subway construction capacity cannot meet the rapidly growing passenger demand, and subway congestion has occurred. In subway related research, short-term passenger flow prediction plays a vital role in improving the operating efficiency of the subway system. On the one hand, short-term passenger flow prediction can enable subway managers to grasp the distribution of passenger flow in the subway network, understand which stations will experience a surge in passenger flow in the future, and provide a basis for anal...

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 Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30G06K9/62G06N3/04
CPCG06Q10/04G06Q10/06393G06N3/049G06N3/045G06F18/213G06Q50/40
Inventor 许心越王雪琴刘军张可糜子越
Owner BEIJING JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com