CN109472983A - Intellectual traffic control method and system based on deep learning - Google Patents
Intellectual traffic control method and system based on deep learning Download PDFInfo
- Publication number
- CN109472983A CN109472983A CN201811613248.XA CN201811613248A CN109472983A CN 109472983 A CN109472983 A CN 109472983A CN 201811613248 A CN201811613248 A CN 201811613248A CN 109472983 A CN109472983 A CN 109472983A
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- China
- Prior art keywords
- road
- traffic
- traffic lights
- image information
- deep learning
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
Abstract
The present invention provides a kind of intellectual traffic control method and intelligent traffic control system based on deep learning, the intellectual traffic control method based on deep learning include: the real-time road conditions image information obtained at road cross；Discriminance analysis is carried out to the road conditions image information based on image recognition technology, to obtain showing the control parameter of duration for regulating and controlling traffic lights；Obtain the traffic lights history regulation data of the road cross；Regulate and control data according to the control parameter and the traffic lights history, the traffic lights for regulating and controlling the road cross show duration ratio.According to the technical solution of the present invention, intelligently neatly road traffic can be regulated and controled according to actual road conditions, improves the traffic efficiency of road, utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
Description
Technical field
The present invention relates to traffic management technology fields, in particular to a kind of intelligent transportation control based on deep learning
Method processed and a kind of intelligent traffic control system based on deep learning.
Background technique
With the rapid development of economy, the continuous quickening of Process of Urbanization Construction, various aspects are all facing to biggish pressure, especially
It is urban transportation, and with increasing for urban population and vehicle, congestion in road becomes common phenomenon, therefore how to improve people Che Tong
Line efficiency, being reduced as far as traffic congestion phenomenon is a major issue urgently to be solved.Currently, for city road
The control of road crossroads traffic light has 2 kinds of modes: timing controlled and not timing control, wherein timing controlled refers at any time
The all identical signal period controls traffic lights, does not account for the real-time change situation of the crossing traffic flow, it is easy to occur
Congestion, not timing control refer to the unlike signal period that Duan Xuan is set in different times to control traffic lights, such as peak period
Or non-peak period, this mode improves the traffic efficiency of road to a certain extent, but road conditions itself are more complicated changeable
, this control methods or inflexible, effect is not still it is obvious that therefore how flexibly intelligently regulation reason is red green
Lamp becomes technical problem urgently to be resolved with the traffic efficiency for improving road.
Summary of the invention
The present invention is based at least one above-mentioned technical problem, proposes a kind of new intelligence friendship based on deep learning
Logical control program intelligently neatly can regulate and control road traffic according to actual road conditions, improve the current effect of road
Rate, utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
In view of this, the invention proposes a kind of new intellectual traffic control methods based on deep learning, comprising: in real time
Obtain the road conditions image information at road cross；Discriminance analysis is carried out to the road conditions image information based on image recognition technology,
To obtain showing the control parameter of duration for regulating and controlling traffic lights；Obtain the traffic lights history regulation data of the road cross；
Regulate and control data according to the control parameter and the traffic lights history, the traffic lights for regulating and controlling the road cross show time length ratio
Example.
In the technical scheme, by obtaining the road conditions image information at road cross, and image recognition technology point is combined
Control parameter is precipitated, then the traffic lights history of road cross regulates and controls data, regulates and controls data based on control parameter and to history
Excavation and deep learning come regulate and control road cross traffic lights show duration ratio, entire regulation process can be based on actual
The deep learning of traffic information and history regulation letter data regulates and controls traffic lights, fully demonstrated regulation flexibility and
Accuracy effectively improves the traffic efficiency of road, and utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
In the above-mentioned technical solutions, it is preferable that the control parameter includes the two-way people's vehicle flowrate of road, people's vehicle queue length
And ground dry and wet degree.
In any of the above-described technical solution, it is preferable that the road cross is provided with image collecting device, described real-time
The step of obtaining the road conditions image information at road cross, specifically includes: obtaining the collected road conditions of described image acquisition device
Image information, wherein the road conditions image information includes people's vehicle picture or video, ground image or video.
According to the second aspect of the invention, a kind of intelligent traffic control system based on deep learning is proposed, comprising: the
One acquiring unit, for obtaining the road conditions image information at road cross in real time；Analytical unit, for being based on image recognition technology
Discriminance analysis is carried out to the road conditions image information, to obtain showing the control parameter of duration for regulating and controlling traffic lights；Second obtains
Unit is taken, the traffic lights history for obtaining the road cross regulates and controls data；Regulate and control unit, for according to the control parameter
And the traffic lights history regulates and controls data, the traffic lights for regulating and controlling the road cross show duration ratio.
In the technical scheme, by obtaining the road conditions image information at road cross, and image recognition technology point is combined
Control parameter is precipitated, then the traffic lights history of road cross regulates and controls data, regulates and controls data based on control parameter and to history
Excavation and deep learning come regulate and control road cross traffic lights show duration ratio, entire regulation process can be based on actual
The deep learning of traffic information and history regulation letter data regulates and controls traffic lights, fully demonstrated regulation flexibility and
Accuracy effectively improves the traffic efficiency of road, and utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
In the above-mentioned technical solutions, it is preferable that the control parameter includes the two-way people's vehicle flowrate of road, people's vehicle queue length
And ground dry and wet degree.
In any of the above-described technical solution, it is preferable that the road cross is provided with image collecting device, and described first
Acquiring unit is specifically used for: obtaining the collected road conditions image information of described image acquisition device, wherein the road conditions image letter
Breath includes people's vehicle picture or video, ground image or video.
By above technical scheme, can the deep learning based on actual traffic information and history regulation letter data come pair
Traffic lights are regulated and controled, and have been fully demonstrated the flexibility and accuracy of regulation, have been effectively improved the traffic efficiency of road, maximum
The generation of degree Shangdi reduction traffic congestion phenomenon.
Detailed description of the invention
Fig. 1 shows the exemplary flow of the intellectual traffic control method based on deep learning of embodiment according to the present invention
Figure；
Fig. 2 shows the schematic block diagrams of the intelligent traffic control system based on deep learning of embodiment according to the present invention.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying mode, the present invention is further described in detail.It should be noted that in the absence of conflict, the implementation of the application
Feature in example and embodiment can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, still, the present invention may be used also
To be implemented using other than the one described here other modes, therefore, protection scope of the present invention is not by described below
Specific embodiment limitation.
Below in conjunction with Fig. 1 and Fig. 2, technical scheme is described further:
As shown in Figure 1, the intellectual traffic control method based on deep learning of embodiment according to the present invention, comprising:
Step 102, the road conditions image information at road cross is obtained in real time.
Specifically, image collecting device is set at road cross, and to acquire road conditions image information, road conditions image information is
Picture or video, picture or video including people's vehicle and ground.
Step 104, discriminance analysis is carried out to road conditions image information based on image recognition technology, it is red green for regulating and controlling to obtain
Lamp shows the control parameter of duration.
Specifically, people's vehicle picture or video are split, feature extraction, the image recognition processes such as comparison, and combined pre-
Setting analysis algorithm analyzes vehicle flowrate, vehicle queue length, flow of the people, people's queue length etc., according to image recognition technology to ground
Picture or video are analyzed the humidity to identify ground, and generally the wet and slippery degree in ground is bigger, need to be put to pedestrian
The row time is longer.
Step 106, the traffic lights history regulation data of road cross are obtained.
Step 108, data are regulated and controled according to control parameter and traffic lights history, when regulating and controlling the traffic lights display of road cross
Long ratio.Specifically, data mining and deep learning are carried out to traffic lights history regulation data, dynamic adjusts pre-defined algorithm model
Parameter, so that model algorithm is constantly evolved, by control parameter bring into pre-defined algorithm model calculate traffic lights show duration.Pass through
It the road conditions image information at road cross is obtained, and analyzes control parameter in conjunction with image recognition technology, then road cross
Traffic lights history regulates and controls data, regulates and controls excavation and the deep learning of data based on control parameter and to history to regulate and control Road
The traffic lights of mouth show duration ratio, and entire regulation process being capable of the depth based on actual traffic information and history regulation letter data
Degree study has fully demonstrated the flexibility and accuracy of regulation to regulate and control to traffic lights, effectively improves the logical of road
Line efficiency, utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
If he is shown in 2, the intelligent traffic control system 200 based on deep learning of embodiment according to the present invention, comprising:
First acquisition unit 202, analytical unit 204, second acquisition unit 206 and regulation unit 208.
Wherein, first acquisition unit 202 for obtaining the road conditions image information at road cross in real time；Analytical unit 204
For carrying out discriminance analysis to road conditions image information based on image recognition technology, to obtain showing duration for regulating and controlling traffic lights
Control parameter；Second acquisition unit 206 is used to obtain the traffic lights history regulation data of road cross；Regulation unit 208 is used for
Regulate and control data according to control parameter and traffic lights history, the traffic lights for regulating and controlling road cross show duration ratio.Wherein, it controls
Parameter includes the two-way people's vehicle flowrate of road, the queue length of people's vehicle and ground dry and wet degree.
Further, road cross is provided with image collecting device, and first acquisition unit 202 is specifically used for: obtaining image
The collected road conditions image information of acquisition device, wherein road conditions image information includes people's vehicle picture or video, ground image or view
Frequently.
The technical scheme of the present invention has been explained in detail above with reference to the attached drawings, and technical solution of the present invention proposes a kind of new
Intellectual traffic control scheme based on deep learning, being capable of the depth based on actual traffic information and history regulation letter data
The flexibility and accuracy for having fully demonstrated regulation to regulate and control to traffic lights are practised, the current effect of road is effectively improved
Rate, utmostly the generation of traffic congestion phenomenon is reduced in Shangdi.
It is merely a preferred embodiment of the present invention, is not intended to restrict the invention, for the technology of this field described in upper
For personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (6)
1. a kind of intellectual traffic control method based on deep learning characterized by comprising
The road conditions image information at road cross is obtained in real time；
Discriminance analysis is carried out to the road conditions image information based on image recognition technology, when obtaining for regulating and controlling traffic lights display
Long control parameter；
Obtain the traffic lights history regulation data of the road cross；
Regulate and control data according to the control parameter and the traffic lights history, when regulating and controlling the traffic lights display of the road cross
Long ratio.
2. the intellectual traffic control method according to claim 1 based on deep learning, which is characterized in that the control ginseng
Number includes the two-way people's vehicle flowrate of road, the queue length of people's vehicle and ground dry and wet degree.
3. the intellectual traffic control method according to claim 1 based on deep learning, which is characterized in that the Road
The step of mouth is provided with image collecting device, the real-time road conditions image information obtained at road cross, specifically includes:
Obtain the collected road conditions image information of described image acquisition device, wherein the road conditions image information includes people Che Tu
Piece or video, ground image or video.
4. a kind of intelligent traffic control system based on deep learning characterized by comprising
First acquisition unit, for obtaining the road conditions image information at road cross in real time；
Analytical unit, for carrying out discriminance analysis to the road conditions image information based on image recognition technology, to obtain for adjusting
Control the control parameter that traffic lights show duration；
Second acquisition unit, the traffic lights history for obtaining the road cross regulate and control data；
Regulate and control unit, for regulating and controlling data according to the control parameter and the traffic lights history, regulates and controls the road cross
Traffic lights show duration ratio.
5. the intelligent traffic control system according to claim 4 based on deep learning, which is characterized in that the control ginseng
Number includes the two-way people's vehicle flowrate of road, the queue length of people's vehicle and ground dry and wet degree.
6. the intelligent traffic control system according to claim 4 based on deep learning, which is characterized in that the Road
Mouth is provided with image collecting device, and the first acquisition unit is specifically used for:
Obtain the collected road conditions image information of described image acquisition device, wherein the road conditions image information includes people Che Tu
Piece or video, ground image or video.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811613248.XA CN109472983A (en) | 2018-12-27 | 2018-12-27 | Intellectual traffic control method and system based on deep learning |
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| CN201811613248.XA CN109472983A (en) | 2018-12-27 | 2018-12-27 | Intellectual traffic control method and system based on deep learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991782A (en) * | 2021-04-08 | 2021-06-18 | 河北工业大学 | Control method, system, terminal, equipment, medium and application of traffic signal lamp |
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| CN104933872A (en) * | 2014-03-19 | 2015-09-23 | 北京航天长峰科技工业集团有限公司 | Single-intersection traffic signal optimization control method |
| US20170372602A1 (en) * | 2016-06-24 | 2017-12-28 | Continental Advanced Lidar Solutions Us, Llc | Ladar enabled traffic control |
| CN108932855A (en) * | 2017-05-22 | 2018-12-04 | 阿里巴巴集团控股有限公司 | Road traffic control system, method and electronic equipment |
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2018
- 2018-12-27 CN CN201811613248.XA patent/CN109472983A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933872A (en) * | 2014-03-19 | 2015-09-23 | 北京航天长峰科技工业集团有限公司 | Single-intersection traffic signal optimization control method |
| US20170372602A1 (en) * | 2016-06-24 | 2017-12-28 | Continental Advanced Lidar Solutions Us, Llc | Ladar enabled traffic control |
| CN108932855A (en) * | 2017-05-22 | 2018-12-04 | 阿里巴巴集团控股有限公司 | Road traffic control system, method and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112991782A (en) * | 2021-04-08 | 2021-06-18 | 河北工业大学 | Control method, system, terminal, equipment, medium and application of traffic signal lamp |
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Application publication date: 20190315 |