CN111179586A - Traffic guidance method, equipment and storage medium based on big data analysis - Google Patents
Traffic guidance method, equipment and storage medium based on big data analysis Download PDFInfo
- Publication number
- CN111179586A CN111179586A CN201911016866.0A CN201911016866A CN111179586A CN 111179586 A CN111179586 A CN 111179586A CN 201911016866 A CN201911016866 A CN 201911016866A CN 111179586 A CN111179586 A CN 111179586A
- Authority
- CN
- China
- Prior art keywords
- traffic
- road
- traffic flow
- data
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000007405 data analysis Methods 0.000 title claims abstract description 17
- 230000007613 environmental effect Effects 0.000 claims abstract description 23
- 230000006698 induction Effects 0.000 claims abstract description 14
- 230000008520 organization Effects 0.000 claims abstract description 13
- 239000006185 dispersion Substances 0.000 claims abstract description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 4
- 206010039203 Road traffic accident Diseases 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 3
- 239000013618 particulate matter Substances 0.000 claims description 2
- 239000007789 gas Substances 0.000 description 7
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013475 authorization Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002912 waste gas Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/095—Traffic lights
Landscapes
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Analytical Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Databases & Information Systems (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Remote Sensing (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a traffic guidance method, equipment and a storage medium based on big data analysis, wherein the traffic guidance method comprises the following steps: collecting traffic flow data to acquire real-time traffic conditions of all road sections in a road network; predicting traffic flow in future time periods of each road segment in a road network according to real-time traffic conditions to form a traffic prediction result; collecting road surface environment parameters, establishing a traffic control and induction cooperative model by combining traffic prediction results and the road surface environment parameters, and outputting a traffic flow organization and a dispersion plan; and respectively controlling the working state of the traffic signal lamp corresponding to each road section and the issuing of early warning information by the terminal equipment according to the traffic flow organization and the dispersion plan. The invention combines real-time environmental parameters of the road surface and traffic flow data, and reasonably induces and controls road signals, thereby improving the running smoothness of the motor vehicle, reducing the environmental discharge pressure of the motor vehicle, and solving the environmental problem of the existing traffic road.
Description
Technical Field
The present invention relates to traffic control, and more particularly, to a traffic guidance method, device, and storage medium based on big data analysis.
Background
At present, the traditional traffic control guidance system is generally based on real-time traffic flow data detection, and the traffic flow state is calculated and controlled by established control and guidance algorithms when detected. Associated with the transportation system is also the impact on environmental issues.
Besides normal running, road vehicles are influenced by traditional traffic control and traffic induction and can be in various running states such as deceleration, idling and acceleration, so that the emission concentration of tail gas of the motor vehicle is increased, and the fuel efficiency of the motor vehicle is reduced. Even more alarming is that in places with dense pedestrian flows, the pedestrians are exposed to places with serious vehicle exhaust pollution for a long time, and obvious discomfort is caused to the pedestrians.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a traffic guidance method based on big data analysis, which improves the smoothness of the running of a motor vehicle, reduces the environmental discharge pressure of the motor vehicle and relieves the environmental problem through reasonable guidance and control.
The second purpose of the invention is to provide a terminal device.
It is a further object of the present invention to provide a storage medium.
One of the purposes of the invention is realized by adopting the following technical scheme:
a traffic guidance method based on big data analysis comprises the following steps:
collecting traffic flow data to acquire real-time traffic conditions of all road sections in a road network;
predicting the traffic flow of each road section in the road network in the future time period according to the real-time traffic condition to form a traffic prediction result;
collecting road surface environment parameters, establishing a traffic control and induction cooperative model by combining traffic prediction results and the road surface environment parameters, and outputting a traffic flow organization and a dispersion plan;
and respectively controlling the working state of the traffic signal lamp corresponding to each road section and the issuing of early warning information by the terminal equipment according to the traffic flow organization and the dispersion plan.
Further, after the traffic flow data is collected, the traffic flow data is preprocessed, and the preprocessing method comprises the following steps:
determining an uploading period: according to time information in traffic flow data, judging the uploading time interval between every two data one by one, if the time interval between certain two data is integral multiple of the uploading period and the multiple is more than 1, indicating that the data is missed and lost at the position between the two data;
and (3) missing data completion: the average value of the two data at the position is taken and the data is restored by the average value.
Further, the traffic flow data comprises traffic flow data of historical time periods and real-time periods, wherein the traffic flow data comprises traffic flow, vehicle speed, headway, traffic events, lane numbers and vehicle types.
Further, the accident data of the traffic incident is acquired by issuing and/or detecting a real-time road video image in a traffic center platform through an alarm platform.
Further, the traffic control and guidance cooperative model is connected with a plan library, and the plan library stores traffic control and guidance measures for different traffic flow sudden changes, traffic accidents and traffic control behaviors.
Further, the terminal device for issuing the early warning information comprises a vehicle-mounted navigation terminal, a mobile phone navigation terminal and a traffic police command terminal.
Further, the early warning information comprises recommendation of an optimal navigation route, air quality of a path road section in the navigation route, suggestion of vehicle running speed and suggestion of traffic police guidance.
Further, the road surface environmental parameters comprise the type and content of tail gas pollution gas, the type and content of fixed particulate matter, a noise value and meteorological parameters.
The second purpose of the invention is realized by adopting the following technical scheme:
a terminal device comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the traffic control method of the traffic signal platform.
The third purpose of the invention is realized by adopting the following technical scheme:
a storage medium having stored thereon a computer program which, when executed, implements the traffic control method of a traffic signal platform as described above.
Compared with the prior art, the invention has the beneficial effects that:
by combining real-time environmental parameters of the road surface and traffic flow data and reasonably inducing and controlling road signals, the running smoothness of the motor vehicle is improved, the environmental discharge pressure of the motor vehicle is reduced, and the environmental problem of the conventional traffic road is solved.
Drawings
FIG. 1 is a signal flow diagram according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a second embodiment of the present invention.
In the figure: 1. a traffic flow data acquisition device; 2. an environmental parameter receiving device; 3. a processor; 4. a traffic guidance issuing device; 5. traffic signal control device.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that, in the present invention, the embodiments or technical features described below may be arbitrarily combined to form a new embodiment without conflict.
Example one
A traffic induction method based on big data analysis is disclosed, as shown in FIG. 1, and comprises the following steps:
step S1: and acquiring traffic flow data to acquire the real-time traffic condition of each road section in the road network.
The traffic flow data acquisition device 1 in the traffic signal platform is connected with the existing traffic center platform, and historical traffic flow data and real-time traffic flow data in the traffic center platform can be acquired under the authorization of the traffic center platform, so that the historical traffic condition and the real-time traffic condition of each road section in a road network can be obtained. And the historical traffic flow data can be stored in a historical traffic database and can be used as a reference and basis for predicting the traffic jam condition at the later stage.
The existing traffic center platform can acquire a real-time road surface picture by shooting through a traffic camera, and the traffic center platform can intelligently identify traffic flow data such as traffic flow, speed, headway time, traffic events, lane numbers, vehicle types, public and private vehicle classification and the like of all road sections in a road network according to the shot road surface picture; in addition, the traffic center platform can be connected with an alarm platform, can receive 110 alarm information in real time, can also receive traffic alarm information of a police car vehicle-mounted terminal and a mobile police terminal, and can also acquire specific accident data of real-time traffic events of current road sections in real time in a mode of detecting real-time road surface video images in the traffic center platform.
In addition, the traffic flow data acquisition device 1 can also acquire the working states of intersection signal lamps of all road sections through the guided traffic signal platform, so that the working states of the intersection signal lamps can be conveniently switched according to a dredging plan in the later period.
After traffic flow data are collected, preprocessing the traffic flow data, wherein the preprocessing method comprises the following steps:
and judging the uploading time interval between every two data one by taking the uploading data period as a reference, if the time interval is integral multiple of the uploading period and the multiple is more than 1, indicating that the data is missed and lost, and repairing the data by using the front data and the rear data of the position with an average value. For the data with inconsistent uploading time interval caused by the change of the signal period, if the uploading interval is larger than the uploading period, the flow value of the part is uniformly divided into the flow value close to the uploading period, and if the uploading interval is smaller than the uploading period, the flow value of the part is uniformly combined into the flow value close to the uploading period.
Step S2: and predicting the traffic flow of each road section in the road network in the future time period according to the real-time traffic condition to form a traffic prediction result.
The traffic jam condition of each current road section in a short period in the future is fused and predicted according to various information acquired by the traffic flow data acquisition device 1, and if the real-time traffic flow data shows that the traffic flow of a current road section is small and no traffic event occurs, the traffic prediction result of the road section in the short period can be predicted to be smooth; if the real-time traffic flow data show that the current traffic flow of a certain road section is large and a traffic event occurs, the traffic jam condition of the road section in a short period can be predicted according to the traffic flow distribution characteristics in the historical traffic database, and whether the grade of the traffic jam of the road section is slow or jammed and the corresponding traffic jam prediction time are analyzed.
Step S3: collecting road surface environment parameters, establishing a traffic control and induction cooperative model by combining traffic prediction results and the road surface environment parameters, and outputting a traffic flow organization and a dispersion plan.
The road surface environmental parameters of all road sections detected by a plurality of environmental monitors on the road surface are collected by an environmental parameter receiving device 2 in a traffic signal platform, and the road surface environmental parameters comprise but are not limited to the type and the content of tail gas pollution gas, the type and the content of fixed particulate matters, noise values and meteorological parameters.
And then, road surface environment parameters, historical traffic flow data, real-time traffic flow data, short-term traffic prediction results and accident data of traffic events are fused, a control and induction cooperative model is established together according to the existing modeling mode, empirical model fitting is carried out by utilizing the existing artificial neural network technology, and then traffic flow organization and evacuation plans related to environmental emission of the motor vehicles can be obtained, wherein the traffic flow organization and evacuation plans comprise switching suggestions, vehicle drainage guides, pedestrian flow drainage guides and the like for the working states of traffic lights of all road sections. In addition, the traffic control and induction cooperative model is connected with a plan library, and the plan library stores traffic control and induction measures which are made aiming at different traffic flow sudden changes, traffic accidents and traffic control behaviors, so that more modeling data are provided for the control and induction cooperative module, and the accuracy of traffic flow combination and plan dispersion is improved.
Step S4: respectively controlling the working state of a traffic signal lamp corresponding to each road section and releasing early warning information by terminal equipment according to a traffic flow organization and a dispersion plan, wherein the terminal equipment comprises but is not limited to a vehicle-mounted navigation terminal, a mobile phone navigation terminal and a traffic police command terminal; the early warning information includes but is not limited to optimal navigation route recommendation, air quality of road sections in the navigation route, vehicle driving speed recommendation and traffic police command recommendation.
In order to realize the cooperation of control, guidance and on-site traffic guidance personnel, the information communication and cooperation with the traffic guidance personnel are realized through a traffic guidance integrated system, a mobile police service system or a trunking intercom system, so that the cooperation of control, guidance and traffic police is realized, and the traffic guidance and control capacity is improved.
For example, when the traffic jam condition of a certain road section is slow, the traffic flow organization and evacuation plan can communicate information with traffic commanders, so that the traffic commanders on site assist the traffic evacuation work, and meanwhile, the red light waiting time of traffic signal lamps of the road section is controlled to be shortened, the evacuation of vehicles is accelerated, in addition, the current jam condition of the road section can be informed to drivers through a vehicle-mounted GPS navigation terminal, the driving speed is intelligently recommended according to the position and the signal timing of the vehicles, the stopping times and energy consumption in the low peak period are reduced, the occurrence of the conditions of sudden braking and the like is avoided, and the emission concentration of tail gas of motor vehicles is reduced. If the traffic jam condition of the road section is serious congestion, the driver can be informed of the predicted congestion time and be reminded of stopping the engine waiting for the driver on the serious congestion road section so as to reduce the tail gas emission of the vehicle; meanwhile, the map navigation software on the mobile phone terminal can inform the current air quality of the road section, and the route outside the road section is recommended to the pedestrian, so that the pedestrian is prevented from breathing excessive waste gas.
Example two
An electronic device comprising a memory, a processor 3 and a program stored in the memory, the program being configured to be executed by the processor 3, when executing the program, implementing the steps of the traffic inducing method as described above.
In this embodiment, the electronic device may be a traffic signal platform, as shown in fig. 2, the platform includes:
the traffic flow data acquisition device 1 is connected with a traffic center platform through a communication port, and is used for acquiring traffic flow data of the traffic center platform;
the environment parameter receiving device 2 is connected with a plurality of environment monitors through a wireless network and is used for receiving the road surface environment parameters detected by the environment monitors;
a signal input end of the processor 3 is connected with signal output ends of the traffic flow data acquisition device 1 and the environmental parameter receiving device 2 and is used for correspondingly generating an induction signal and a control signal by combining traffic flow data and road surface environmental parameters;
the signal input end of the traffic guidance issuing device 4 is connected with the signal output end of the processor 3 and used for controlling the traffic guidance issuing device 4 to issue early warning information according to the guidance signal;
and a signal input end of the traffic signal control device 5 is connected with a signal output end of the processor 3, and a signal output end of the traffic signal control device 5 is connected with the traffic lights of all road sections and is used for controlling the working state of the traffic lights of all road sections according to the control signal.
The specific working principle of the traffic guidance control platform is as follows:
firstly, the traffic flow data acquisition device 1 is connected with the existing traffic center platform, and historical traffic flow data and real-time traffic flow data in the traffic center platform can be acquired under the authorization of the traffic center platform, so that the historical traffic condition and the real-time traffic condition of each road section in a road network can be acquired. And the historical traffic flow data can be stored in a historical traffic database and can be used as a reference and basis for predicting the traffic jam condition at the later stage.
The existing traffic center platform can acquire a real-time road surface picture by shooting through a traffic camera, and the traffic center platform intelligently identifies traffic flow data of each road section in a road network according to the shot road surface picture; in addition, the traffic center platform can be connected with an alarm platform, can receive 110 alarm information in real time, can also receive traffic alarm information of a police car vehicle-mounted terminal and a mobile police terminal, and can also acquire specific accident data of real-time traffic events of current road sections in real time in a mode of detecting real-time road surface video images in the traffic center platform.
In addition, the traffic flow data acquisition device 1 can also acquire the working states of intersection signal lamps of all road sections through the guided traffic signal platform, so that the working states of the intersection signal lamps can be conveniently switched according to a dredging plan in the later period.
The traffic jam condition of each current road section in a short period in the future is fused and predicted according to various information acquired by the traffic flow data acquisition device 1, and if the real-time traffic flow data shows that the traffic flow of a current road section is small and no traffic event occurs, the traffic prediction result of the road section in the short period can be predicted to be smooth; if the real-time traffic flow data show that the current traffic flow of a certain road section is large and a traffic event occurs, the traffic jam condition of the road section in a short period can be predicted according to the traffic flow distribution characteristics in the historical traffic database, and whether the grade of the traffic jam of the road section is slow or jammed and the corresponding traffic jam prediction time are analyzed.
Secondly, the environmental parameter receiving device 2 collects the road surface environmental parameters of each road section detected by a plurality of environmental monitors on the road surface. The processor 3 integrates road surface environment parameters, historical traffic flow data, real-time traffic flow data, short-term traffic prediction results and accident data of traffic events, establishes a control and induction cooperative model together according to the existing modeling mode, and performs empirical model fitting by using the existing artificial neural network technology, so that a traffic flow organization and a dredging plan related to the environmental emission of the motor vehicle can be obtained, wherein the traffic flow organization and the dredging plan comprise induction signals and control signals such as switching suggestions of working states of traffic lamps of various road sections, vehicle drainage guidance, pedestrian flow guidance and the like.
The traffic guidance issuing device 4 issues early warning information to inform a user of real-time traffic conditions after receiving the guidance signal; and the traffic signal control device 5 controls the working state of the traffic lights of each road section after receiving the control signal, thereby relieving the congestion condition of each road section in the road network.
In addition, the present invention also provides a storage medium storing a computer program which, when executed by the processor 3, implements the steps of the aforementioned traffic guidance method.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor 3 systems, microprocessor 3-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A traffic guidance method based on big data analysis is characterized by comprising the following steps:
collecting traffic flow data to acquire real-time traffic conditions of all road sections in a road network;
predicting the traffic flow of each road section in the road network in the future time period according to the real-time traffic condition to form a traffic prediction result;
collecting road surface environment parameters, establishing a traffic control and induction cooperative model by combining traffic prediction results and the road surface environment parameters, and outputting a traffic flow organization and a dispersion plan;
and respectively controlling the working state of the traffic signal lamp corresponding to each road section and the issuing of early warning information by the terminal equipment according to the traffic flow organization and the dispersion plan.
2. The big data analysis-based traffic guidance method according to claim 1, wherein the traffic flow data is collected and then preprocessed, and the preprocessing method comprises:
determining an uploading period: according to time information in traffic flow data, judging the uploading time interval between every two data one by one, if the time interval between certain two data is integral multiple of the uploading period and the integral multiple is more than 1, indicating that the data are missed and lost at the position between the two data;
and (3) missing data completion: the average value of the two data at the position is taken and the data is restored by the average value.
3. The big data analysis-based traffic guidance method according to claim 1, wherein the traffic flow data comprises traffic flow data of historical and real-time periods, wherein the traffic flow data comprises traffic flow, vehicle speed, headway, traffic events, lane numbers, vehicle types.
4. The big data analysis based traffic guidance method according to claim 3, wherein the accident data of the traffic incident is obtained by means of issuing and/or detecting real-time road video images in a traffic center platform by an alarm platform.
5. The big data analysis-based traffic guidance method according to claim 1, wherein the traffic control and guidance collaborative model is connected with a plan library, and the plan library stores traffic control and guidance measures for different traffic flow sudden changes, traffic accidents and traffic control behaviors.
6. The traffic guidance method based on big data analysis according to claim 1, wherein the terminal device issuing early warning information comprises a vehicle-mounted navigation terminal, a mobile phone navigation terminal, and a traffic police command terminal.
7. The big data analysis-based traffic guidance method according to claim 6, wherein the early warning information comprises an optimal navigation route recommendation, air quality of a road segment in a navigation route, a vehicle driving speed recommendation, and a traffic police guidance recommendation.
8. The big-data-analysis-based traffic induction method according to claim 1, wherein the road environmental parameters include tail gas pollution gas type and content, fixed particulate matter type and content, noise value and meteorological parameters.
9. A terminal device, comprising a processor, a memory and a computer program stored on the memory and operable on the processor, wherein the processor executes the computer program to implement the traffic guidance method based on big data analysis according to any one of claims 1 to 8.
10. A storage medium having stored thereon a computer program which, when executed, implements the big data analysis-based traffic guidance method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911016866.0A CN111179586A (en) | 2019-10-24 | 2019-10-24 | Traffic guidance method, equipment and storage medium based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911016866.0A CN111179586A (en) | 2019-10-24 | 2019-10-24 | Traffic guidance method, equipment and storage medium based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111179586A true CN111179586A (en) | 2020-05-19 |
Family
ID=70651871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911016866.0A Pending CN111179586A (en) | 2019-10-24 | 2019-10-24 | Traffic guidance method, equipment and storage medium based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111179586A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739313A (en) * | 2020-07-17 | 2020-10-02 | 安徽达尔智能控制系统股份有限公司 | Signal lamp management and control system based on road traffic flow big data |
| CN111915907A (en) * | 2020-08-18 | 2020-11-10 | 河南中天高新智能科技股份有限公司 | Multi-scale traffic information publishing system and method based on vehicle-road cooperation |
| CN111951557A (en) * | 2020-08-21 | 2020-11-17 | 成都工业学院 | Regional short-term traffic flow prediction method and system based on Internet of vehicles big data |
| CN112885123A (en) * | 2021-01-18 | 2021-06-01 | 湖南省交通规划勘察设计院有限公司 | Urban road condition information management system combining computer information and communication technology |
| CN113178069A (en) * | 2021-04-20 | 2021-07-27 | 宁波德尔菲信息科技有限公司 | Traffic early warning system based on big data machine learning technology |
| CN113256968A (en) * | 2021-04-30 | 2021-08-13 | 济南金宇公路产业发展有限公司 | Traffic state prediction method, equipment and medium based on mobile phone activity data |
| CN113393677A (en) * | 2021-05-27 | 2021-09-14 | 广州国交润万交通信息有限公司 | Traffic guidance object control method, device and system |
| CN113689721A (en) * | 2021-07-30 | 2021-11-23 | 深圳先进技术研究院 | Automatic driving vehicle speed control method, system, terminal and storage medium |
| CN114512001A (en) * | 2022-01-14 | 2022-05-17 | 阿里巴巴新加坡控股有限公司 | Regional traffic monitoring method, device, electronic apparatus, medium, and program product |
| CN114758495A (en) * | 2022-03-29 | 2022-07-15 | 北京百度网讯科技有限公司 | Traffic signal lamp adjusting method and device and electronic equipment |
| CN114973702A (en) * | 2022-04-21 | 2022-08-30 | 安徽皖通科技股份有限公司 | Traffic cooperative command system based on big data |
| CN116504076A (en) * | 2023-06-19 | 2023-07-28 | 贵州宏信达高新科技有限责任公司 | Expressway traffic flow prediction method based on ETC portal data |
-
2019
- 2019-10-24 CN CN201911016866.0A patent/CN111179586A/en active Pending
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739313A (en) * | 2020-07-17 | 2020-10-02 | 安徽达尔智能控制系统股份有限公司 | Signal lamp management and control system based on road traffic flow big data |
| CN111915907A (en) * | 2020-08-18 | 2020-11-10 | 河南中天高新智能科技股份有限公司 | Multi-scale traffic information publishing system and method based on vehicle-road cooperation |
| CN111951557A (en) * | 2020-08-21 | 2020-11-17 | 成都工业学院 | Regional short-term traffic flow prediction method and system based on Internet of vehicles big data |
| CN111951557B (en) * | 2020-08-21 | 2022-05-24 | 成都工业学院 | Regional short-term traffic flow prediction method and system based on Internet of vehicles big data |
| CN112885123B (en) * | 2021-01-18 | 2022-03-11 | 湖南省交通规划勘察设计院有限公司 | Urban road condition information management system combining computer information and communication technology |
| CN112885123A (en) * | 2021-01-18 | 2021-06-01 | 湖南省交通规划勘察设计院有限公司 | Urban road condition information management system combining computer information and communication technology |
| CN113178069A (en) * | 2021-04-20 | 2021-07-27 | 宁波德尔菲信息科技有限公司 | Traffic early warning system based on big data machine learning technology |
| CN113256968A (en) * | 2021-04-30 | 2021-08-13 | 济南金宇公路产业发展有限公司 | Traffic state prediction method, equipment and medium based on mobile phone activity data |
| CN113393677A (en) * | 2021-05-27 | 2021-09-14 | 广州国交润万交通信息有限公司 | Traffic guidance object control method, device and system |
| CN113393677B (en) * | 2021-05-27 | 2024-04-26 | 广州国交润万交通信息有限公司 | Traffic guidance object management and control method, device and system |
| CN113689721A (en) * | 2021-07-30 | 2021-11-23 | 深圳先进技术研究院 | Automatic driving vehicle speed control method, system, terminal and storage medium |
| CN113689721B (en) * | 2021-07-30 | 2022-09-20 | 深圳先进技术研究院 | Automatic driving vehicle speed control method, system, terminal and storage medium |
| CN114512001A (en) * | 2022-01-14 | 2022-05-17 | 阿里巴巴新加坡控股有限公司 | Regional traffic monitoring method, device, electronic apparatus, medium, and program product |
| CN114512001B (en) * | 2022-01-14 | 2024-04-26 | 阿里巴巴创新公司 | Regional traffic monitoring method, device, electronic equipment, medium and program product |
| CN114758495A (en) * | 2022-03-29 | 2022-07-15 | 北京百度网讯科技有限公司 | Traffic signal lamp adjusting method and device and electronic equipment |
| CN114758495B (en) * | 2022-03-29 | 2024-02-06 | 北京百度网讯科技有限公司 | Traffic signal lamp adjusting method and device and electronic equipment |
| CN114973702A (en) * | 2022-04-21 | 2022-08-30 | 安徽皖通科技股份有限公司 | Traffic cooperative command system based on big data |
| CN114973702B (en) * | 2022-04-21 | 2023-08-11 | 安徽皖通科技股份有限公司 | Traffic cooperative command system based on big data |
| CN116504076A (en) * | 2023-06-19 | 2023-07-28 | 贵州宏信达高新科技有限责任公司 | Expressway traffic flow prediction method based on ETC portal data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111179586A (en) | Traffic guidance method, equipment and storage medium based on big data analysis | |
| CN107608388B (en) | Autonomous police vehicle | |
| CN106846863B (en) | Accident black spot warning system and method based on augmented reality and cloud intelligent decision | |
| KR100986279B1 (en) | Method for control intelligent traffic signal and system thereof | |
| US20100070128A1 (en) | vehicle operation by leveraging traffic related data | |
| CN110341594B (en) | Passenger safety situation monitoring system and method for passenger car | |
| CN111882894A (en) | Intelligent traffic signal lamp control method and system | |
| CN204990721U (en) | But intelligent traffic monitoring system of automatic brake car | |
| CN109120890A (en) | A kind of vehicle monitoring and early warning system | |
| CN110992712A (en) | Traffic signal lamp light control system based on cloud computing | |
| CN110648533A (en) | Traffic control method, equipment, system and storage medium | |
| CN205506410U (en) | Lane auxiliary system's demarcation and test system | |
| CN113470407B (en) | Vehicle speed guiding method for multi-intersection passing, server and readable storage medium | |
| CN110930651A (en) | Disaster early warning-based road vehicle management and control method, system and readable storage medium | |
| CN105139678A (en) | Intelligent traffic monitoring system capable of automatically braking automobile | |
| CN110796857B (en) | Big data analysis system and method based on smart city | |
| CN113808418B (en) | Road condition information display system, method, vehicle, computer device and storage medium | |
| CN108960044A (en) | Infrastructure lamp state is determined using vehicle | |
| CN114120623A (en) | Road right distribution and supervision method and device | |
| CN108417068A (en) | Active-mode intelligent Traffic flow guidance method | |
| JP2017167795A (en) | Annoying drive detection device and annoying drive detection method | |
| CN114341962A (en) | Dangerous vehicle display system, dangerous vehicle display device, dangerous vehicle display program, computer-readable recording medium, and apparatus having recorded the program | |
| CN110444019A (en) | Vehicle abnormality parking origin cause of formation detection method and system based on vehicle-mounted parking warning sign | |
| CN211264452U (en) | Traffic guidance control platform based on big data | |
| CN103247178A (en) | Intelligent vehicle license plate recognition system for parking lot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |