公开(公告)号：US11657708B2

公开(公告)日：2023-05-23

申请号：US17211734

申请日：2021-03-24

Applicant: Harbin Engineering University

Inventor： Tong Wang ,  Azhar Hussain ,  Shan Gao ,  Jiahua Cao ,  Liwei Chen

IPC: G08G1/01 ,  G06T7/90 ,  G06T7/20

CPC classification number: G08G1/0133 ,  G06T7/20 ,  G06T7/90 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30236

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.

公开(公告)号：US20210209939A1

公开(公告)日：2021-07-08

申请号：US17211734

申请日：2021-03-24

Applicant: Harbin Engineering University

Inventor： Tong Wang ,  Azhar Hussain ,  Shan Gao ,  Jiahua Cao ,  Liwei Chen

IPC: G08G1/01 ,  G06T7/20 ,  G06T7/90

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.