Large-scale real-time traffic flow prediction method based on fuzzy logic and deep LSTM
US11657708B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 24, 2021 |
| Grant date | May 23, 2023 |
| Priority date | — |
| Expiry date | Aug 26, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02T10/40
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.