Method for constructing prediction model of auto trips quantity and prediction method and system
US11995982B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 10, 2020 |
| Grant date | May 28, 2024 |
| Priority date | — |
| Expiry date | Jan 10, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08G1/0104
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for constructing a prediction model of an auto trips quantity and a prediction method and system are disclosed. The prediction model construction method designs a deep neural network Multitask GCN-LSTM based on GCN and LSTM for predicting the auto trips quantity. The deep neural network comprises three modules, wherein the three modules are respectively used for extracting a spatial correlation, a temporal correlation and a feature fusion. The prediction method and system predict the auto trips quantity based on a model constructed. By considering a road segment local relationship and a road segment global relationship and taking an auto arrival quantity as a related task in constructing the model, the prediction model construction method uses a multi-task learning method to avoid overfitting of the deep neural network and reduce a prediction error of the auto trips quantity effectively.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.