Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A road condition prediction method and device

A forecasting method and forecasting device technology, applied in forecasting, biological neural network models, data processing applications, etc., can solve problems such as difficult to realize road condition prediction, unable to effectively extract, etc., and achieve the effect of realizing road condition prediction and accurate road condition prediction

Active Publication Date: 2019-03-01
ALIBABA (CHINA) CO LTD
View PDF5 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Stationarity is one of the prerequisites for the application of CNN; therefore, the conventional LSTM-CNN network structure cannot effectively extract features that are valuable for road condition prediction, and thus it is difficult to achieve accurate road condition prediction

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
  • A road condition prediction method and device
  • A road condition prediction method and device
  • A road condition prediction method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0067] see Figure 7 , which is a frame diagram of an embodiment of the road condition prediction device provided by the present invention.

[0068] The road condition prediction device provided in this embodiment includes:

[0069] The road condition conversion module 701 is used to convert the road condition information into a topological structure diagram and input it into the prediction model, wherein the nodes of the topological structure diagram represent road sections, and the edges represent turns;

[0070] The input module 702 is used to use the topological structure graph as the first input of the prediction model, and the prediction model includes sequentially arranged: a convolution module 703, a pooling module 704 and a full connection module 705;

[0071] Wherein, the N-1th downsampling result output by the pooling module will be used as the Nth input of the convolution module until N reaches the set value, and N is a positive integer greater than or equal to 2;...

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 a road condition prediction method and device. The method comprises the following steps: converting road condition information of a road section into a topological structure diagram; nodes of the topological structure diagram represent road sections; edges represent connection relations of the road sections; taking the topology map as a first input of a prediction model, the prediction model comprising a convolution layer, a pooling layer and a full connection layer arranged in order; taking N-1 downsampling result output by the pooled layer output as the Nth inputof the volume layer until N reaches the set number of times, N is a positive integer greater than or equal to 2. The Nth downsampling result of the pooling layer is inputted into the whole connectionlayer to perform the whole convolution operation, and the road condition prediction result of the road section output by the prediction model is obtained. The technical proposal provided by the application can effectively extract valuable features for road condition prediction by performing convolution neural network on the topological structure diagram obtained by road condition conversion, thereby realizing accurate road condition prediction.

Description

technical field [0001] The invention relates to the field of real-time traffic technology, in particular to a method and device for predicting road conditions. Background technique [0002] With the continuous increase of vehicles, the roads are becoming more and more congested. Therefore, in large cities, how to achieve accurate road condition prediction has important guiding significance. Road condition prediction refers to the use of historical road condition information and static road network information to predict future road condition information. Future traffic information helps to improve the excellent rate of ETA (estimated travel time), and helps users avoid future congestion. [0003] A solution for road condition prediction in the prior art is to use a CNN network structure for road condition prediction. CNN is a convolutional neural network, which belongs to a transformation operation in deep learning, and is often used to extract the local area of ​​a regular...

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): G06N3/04G06Q10/04
CPCG06N3/04G06Q10/04G06N3/045
Inventor 冀晨光刘凯奎
Owner ALIBABA (CHINA) CO LTD
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
Eureka Blog
Learn More
PatSnap group products