Civil aviation travel prediction method and system based on passenger image collaborative supply and demand mechanism

By constructing a collaborative supply and demand mechanism based on passenger profiles, and utilizing a dual-branch LSTM module and an attention mechanism feature fusion module, the problems of unexplored population differences and lack of fusion of multi-source data in existing technologies are solved, enabling accurate prediction of air travel volume and dynamic support for segmented populations.

CN121563098BActive Publication Date: 2026-07-07CHINA ACAD OF CIVIL AVIATION SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA ACAD OF CIVIL AVIATION SCI & TECH
Filing Date
2025-11-24
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing air travel prediction technologies fail to fully exploit population differences and effectively integrate multi-source user data, resulting in predictions that cannot be tailored to the needs of specific population segments. Furthermore, they pose risks of privacy breaches and data misalignment, making accurate and interpretable predictions impossible.

Method used

A collaborative supply and demand mechanism based on passenger profiles is adopted. A collaborative prediction LSTM model is constructed through a dual-branch LSTM module and an attention mechanism feature fusion module. Passenger profile labels are configured using multi-source data, and collaborative prediction is performed by combining passenger demand and civil aviation supply characteristics to ensure user privacy and security.

Benefits of technology

It enables dynamic and accurate prediction of passenger travel volume, improves the interpretability of prediction results in terms of population dimensions, supports differentiated operations of airlines, and provides fine-grained decision support.

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Abstract

The application discloses a civil aviation travel prediction method and system based on a passenger portrait collaborative supply and demand mechanism, and the method comprises the following steps: obtaining a historical travel booking date sequence data set of passengers, a civil aviation flight supply date sequence data set and an alternative travel date sequence data set; performing data analysis on the historical travel booking date sequence data set in combination with holiday information according to passengers, and configuring a passenger portrait label as a passenger attribute; an attention mechanism feature fusion module performs collaborative time sequence training on the feature time sequence data of a passenger demand branch LSTM module and an travel supply branch LSTM module according to time sequence alignment, and a collaborative prediction LSTM model predicts the civil aviation travel volume in the future for several days. Through the technical fusion of multi-source data fusion, portrait accurate construction, model collaborative prediction and result visual output, the application realizes dynamic and accurate prediction of passenger travel volume, and can provide fine-grained decision support for airline capacity allocation, route optimization and service upgrading.
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Citation Information

Patent Citations

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