SCATS system road traffic flow prediction method based on spatial domain graph convolutional neural network

A convolutional neural network and road traffic technology, applied in the field of SCATS system road traffic flow prediction, can solve the problem of inability to accurately predict traffic flow status

Active Publication Date: 2020-03-31
ZHEJIANG UNIV OF TECH
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiency that the existing technology cannot accurately predict the traffic flow state, the present invention provides a SCATS system road traffic flow prediction method based on the spatia

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SCATS system road traffic flow prediction method based on spatial domain graph convolutional neural network
  • SCATS system road traffic flow prediction method based on spatial domain graph convolutional neural network
  • SCATS system road traffic flow prediction method based on spatial domain graph convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0085] Embodiment 2: Data in the actual experiment

[0086] (1) Select experimental data

[0087] The source of the experimental data set is the coil detection system in Hangzhou Jianggan District. The experiment selects the flow data of 74 lanes. The data sampling period is 15 minutes. The data collection time range is from June 1 to June 30, 2017. The sampling interval T is 15min.

[0088] Each lane acts as a node, which inputs historical traffic to predict future traffic. The first 70% of the 2880 traffic state matrices are used as the training set data for model parameter training, and the remaining 30% of the traffic state matrices are used as the test data set for algorithm verification.

[0089] (2) Parameter determination

[0090] The experimental results of the present invention are realized based on the Tensorflow environment, and the framework of the entire experimental model is built based on Keras. The activation function selects the ReLU function, the number ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an SCATS system road traffic flow prediction method based on a spatial domain graph convolutional neural network. First, using lanes as nodes, obtaining nodes connected with each other according to the traffic adjacency matrix; sorting the correlations of the connected nodes from high to low, then finding a neighborhood node of each target node, constructing a high-order neighborhood traffic state matrix by using the target nodes and the traffic flow state data of the neighborhood nodes of the target nodes as the input of the CNN, and finally obtaining a traffic flow state prediction result of the predicted lane. According to the method, the correlation between the traffic flow state time and the space is fully mined, the accuracy is improved, and the anti-interference performance of a random result is enhanced.

Description

technical field [0001] The invention belongs to the field of intelligent traffic forecasting, and relates to a road traffic flow forecasting method of a SCATS system. Background technique [0002] Nowadays, the improvement of living standards and the progress of automobile manufacturing technology have led to a significant increase in the number of cars owned. When cars bring convenience to our lives, they have also brought many problems. Among them, the problem of urban road congestion is closely related to life. To alleviate the congestion problem, the first thing to do is to allocate urban road resources rationally, and road traffic flow state prediction can help the rational allocation of urban space to a large extent. [0003] Currently widely used forecasting models include: historical average model, time series model, autoregressive moving average model, non-parametric regression model, etc. However, these models are not sufficiently correlated in time and space to f...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G08G1/01G08G1/065G06N3/04
CPCG08G1/0125G08G1/065G06N3/045
Inventor 徐东伟周磊林臻谦魏臣臣戴宏伟彭鹏朱钟华
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap