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Urban road traffic flow prediction method and device based on space-time deep learning model

A technology of road traffic and deep learning, applied in the field of deep learning and intelligent transportation systems, can solve problems such as poor forecasting effect, and achieve the effect of improving forecasting effect

Active Publication Date: 2020-04-14
WUHAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In view of this, the present invention provides a method and device for predicting urban road traffic flow based on a spatio-temporal deep learning model to solve or at least partially solve the technical problem of poor prediction effect existing in the methods in the prior art

Method used

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  • Urban road traffic flow prediction method and device based on space-time deep learning model
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  • Urban road traffic flow prediction method and device based on space-time deep learning model

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Embodiment 1

[0057] This embodiment provides a method for predicting urban road traffic flow based on a spatiotemporal deep learning model, the method comprising:

[0058] S1: Using the adaptive image expression method of vector road network, the vector road network of historical road traffic flow data is converted into a two-dimensional image, and a traffic flow image sequence is constructed. Each pixel of the image represents a road segment in the vector road network.

[0059] The road network is actually a data set with a hierarchical structure. Mathematically, a planar road network can be expressed as G(V,E), where V is a collection of road nodes and E is a collection of road segments. Road traffic flow can be defined as the number of vehicles on the road at a certain moment, with figure 1 It shows the traffic flow distribution of the road network at a certain moment, and the statistical information of the traffic flow is shown in the subtable. If there is no vehicle on the road, the ...

Embodiment 2

[0139] Based on the same inventive concept, this embodiment provides a device for predicting urban road traffic flow based on a spatio-temporal deep learning model, please refer to Figure 11 , the device consists of:

[0140] The road network conversion module 201 is used to adopt the adaptive image expression method of the vector road network to convert the vector road network of the historical road traffic flow data into a two-dimensional image, and construct a communication image sequence, and each pixel of the image represents the vector road network in the vector road network. a section of road;

[0141] The STNN model construction module 202 is used to construct the spatio-temporal deep learning model STNN for urban road traffic flow prediction. STNN includes a short-term traffic flow prediction module, a medium-term traffic flow prediction module, a long-term traffic flow prediction module and an external information module;

[0142] The STNN model training module 203...

Embodiment 3

[0146] See Figure 12 , based on the same inventive concept, the present application also provides a computer-readable storage medium 300, on which a computer program 311 is stored. When the program is executed, the method as described in the first embodiment is implemented.

[0147] Since the computer-readable storage medium introduced in the third embodiment of the present invention is the computer-readable storage medium used to implement the urban road traffic flow prediction method based on the spatio-temporal deep learning model in the first embodiment of the present invention, it is based on the first embodiment of the present invention For the method introduced, those skilled in the art can understand the specific structure and deformation of the computer-readable storage medium, so details are not repeated here. All computer-readable storage media used in the method in Embodiment 1 of the present invention fall within the scope of protection intended by the present in...

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Abstract

The invention aims to solve the problems of time-space dependence, space sparsity and the like in urban road traffic flow prediction modeling. An end-to-end deep learning model STNN (Spatial TemporaryNeural Neural Neural Networks) is constructed to carry out modeling on a road traffic flow mode; traffic flow distribution of all road sections in the whole city range at the future moment can be predicted with high precision according to historical flow data. The model can effectively extract the space-time mode in the road traffic flow, can effectively solve the problem of space sparsity of theroad traffic flow, and provides an effective solution for predicting the urban global road traffic flow.

Description

technical field [0001] The invention relates to the technical field of deep learning and intelligent transportation systems, in particular to a method and device for predicting urban road traffic flow based on a spatio-temporal deep learning model. Background technique [0002] Traffic flow prediction is a long-standing topic in transportation research, and it is an indispensable component in intelligent transportation systems. The study of traffic flow forecasting can help improve the efficiency of people's travel, reduce traffic congestion and pollutant emissions, and enable governments to manage urban traffic efficiently. The main task of traffic flow forecasting is to predict the number of vehicles in a certain area or on a specific road in the future based on historical traffic flow data. [0003] Advances in information and communication technologies have resulted in a large amount of traffic data, which has been accumulated and constitutes a huge database, which enab...

Claims

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Application Information

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IPC IPC(8): G08G1/01G06N3/08
CPCG06N3/08G08G1/0129G08G1/0137
Inventor 贾涛鄢鹏高
Owner WUHAN UNIV
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