Urban rail transit passenger flow short-term OD passenger flow prediction method

By constructing a multi-scene OD passenger flow prediction model based on deep learning in the urban rail transit system, using sparse graph convolution network and cross-attention mechanism, the problem of low OD passenger flow prediction in the existing technology is solved, and more accurate short-term OD passenger flow prediction is achieved, and scientific transportation organization and resource allocation are supported.

CN119647680AActive Publication Date: 2025-03-18BEIJING WUZI UNIVERSITY
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Patent Information

Application Number
CN202411725196.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-18
Estimated Expiration
2044-11-28

AI Technical Summary

Technical Problem

The prior art is difficult to accurately predict the OD passenger flow in the middle road network in urban rail transit systems, especially when the road network is large and the passenger flow dynamics are complex, traditional prediction models cannot fully utilize the nonlinear relationships between OD data, and machine learning methods are based on shallow network structures, making it difficult to extract complex nonlinear spatio-temporal relationships.

Method used

Based on the deep learning framework, a feature fusion prediction model is constructed in multiple scenarios. By constructing scene feature labels, sparse graph convolution networks and cross attention mechanisms, and combining Transformer networks, a combined model is constructed for predicting OD passenger flow.

Benefits of technology

It improves the correlation and feature extraction quality of space-time features, enhances the interaction between the external environment characteristics of multiple scenarios and OD passenger flow information, and can more accurately predict the short-term OD passenger flow of multiple scenarios, meeting the needs of operation management and path planning and design.

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Abstract

The invention relates to a short-term OD passenger flow prediction method for urban rail transit passenger flow, and the method comprises the steps: 1) constructing a label of a scene feature based on an influence factor set which influences the short-term OD passenger flow prediction of multiple scenes, and embedding the scene feature into a time sequence of historical OD passenger flow; 2) constructing a subway network space-time diagram according to the time sequence of the historical OD passenger flow embedded with the scene features, and constructing a sparse graph convolutional network for capturing spatial features; 3) according to the time sequence of the historical OD passenger flow embedded with the scene features, constructing a cross attention mechanism for capturing the time features; and 4) based on the sparse graph convolutional network and a cross attention mechanism, constructing a Transform-based multi-scene short-term passenger flow prediction combination model, and outputting a final predicted OD passenger flow matrix.
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