CN114582126A - Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety - Google Patents
Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety Download PDFInfo
- Publication number
- CN114582126A CN114582126A CN202210213606.8A CN202210213606A CN114582126A CN 114582126 A CN114582126 A CN 114582126A CN 202210213606 A CN202210213606 A CN 202210213606A CN 114582126 A CN114582126 A CN 114582126A
- Authority
- CN
- China
- Prior art keywords
- traffic
- data
- abnormal state
- control
- tunnel
- 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 21
- 230000002159 abnormal effect Effects 0.000 claims abstract description 55
- 238000011156 evaluation Methods 0.000 claims abstract description 32
- 238000003062 neural network model Methods 0.000 claims abstract description 10
- 208000027418 Wounds and injury Diseases 0.000 claims description 18
- 230000006378 damage Effects 0.000 claims description 18
- 208000014674 injury Diseases 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 9
- 238000013178 mathematical model Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 2
- 238000011112 process operation Methods 0.000 abstract 1
- 239000003344 environmental pollutant Substances 0.000 description 8
- 231100000719 pollutant Toxicity 0.000 description 8
- 238000009423 ventilation Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- MGWGWNFMUOTEHG-UHFFFAOYSA-N 4-(3,5-dimethylphenyl)-1,3-thiazol-2-amine Chemical compound CC1=CC(C)=CC(C=2N=C(N)SC=2)=C1 MGWGWNFMUOTEHG-UHFFFAOYSA-N 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 229910002091 carbon monoxide Inorganic materials 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- JCXJVPUVTGWSNB-UHFFFAOYSA-N nitrogen dioxide Inorganic materials O=[N]=O JCXJVPUVTGWSNB-UHFFFAOYSA-N 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000001960 triggered effect Effects 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
-
- 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
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
-
- 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
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
-
- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
-
- 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
- G08G1/16—Anti-collision systems
- G08G1/164—Centralised systems, e.g. external to vehicles
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses an intelligent management and control method and system suitable for extra-long tunnel traffic and considering efficiency safety, and relates to the technical field of tunnel traffic management and control. The method comprises the following specific steps: acquiring traffic operation data and road surface condition data in a tunnel in real time; carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data; and implementing corresponding control measures according to different abnormal state evaluation results, and performing grading early warning according to the abnormal state evaluation results. The invention can accurately and timely make various management and control measures to be combined for tunnel traffic management and control, reduces accident risks, and a manager can pertinently implement the corresponding management and control measures according to different abnormal state evaluation results so as to effectively and timely process operation risks, reduce the probability of accident occurrence and improve the efficiency of tunnel operation.
Description
Technical Field
The invention relates to the technical field of tunnel traffic control, in particular to an intelligent control method and system suitable for ultra-long tunnel traffic with efficiency and safety.
Background
When a plurality of highways, particularly expressways are constructed, due to the landform, the construction of the highways is often completed by arranging tunnels in the mountains when the mountains block the highways, and the construction and later management of the tunnels are difficult. The bottleneck restricting further development of highway tunnels in China lies in intelligent tunnel control, the tunnel is difficult to manage and control due to the particularity of the tunnel, if the management and control are not timely or accurate, the whole traffic line is blocked, and the operation efficiency of the whole traffic line is seriously influenced. Moreover, sometimes a single management and control measure cannot achieve a better management and control effect, and therefore, for those skilled in the art, how to accurately make a tunnel traffic management and control scheme combining multiple management and control measures in time is a problem to be solved urgently.
Disclosure of Invention
In view of this, the present invention provides an intelligent management and control method and system suitable for both efficient and safe traffic in an extra-long tunnel, so as to solve the problems proposed in the background art.
In order to achieve the purpose, the invention adopts the following technical scheme: an intelligent management and control method suitable for ultra-long tunnel traffic and considering efficiency safety comprises the following specific steps:
acquiring traffic operation data and road surface condition data in a tunnel in real time;
carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and implementing corresponding control measures according to different abnormal state evaluation results, and performing grading early warning according to the abnormal state evaluation results.
By adopting the technical scheme, the method has the following beneficial technical effects: the abnormal state evaluation is carried out on the traffic running states triggered in the tunnel under various traffic running scenes, so that a manager can specifically make a management and control scheme according to different abnormal state evaluation results, and the running efficiency of the tunnel is improved.
Optionally, the traffic operation data includes vehicle information, vehicle speed information, and road hour traffic volume information.
Optionally, the road surface condition information includes weather information, driving delay information, queuing length, and road occupancy.
Optionally, the step of constructing the neural network model includes:
acquiring data samples, wherein the data samples comprise traffic operation data, road surface condition data and abnormal state data; wherein the traffic operation data and the road surface condition data are used as input data, and the abnormal state data are used as output data;
carrying out normalization processing on the input data, and dividing the data sample into 80% of training set and 20% of testing set;
inputting the training set into a neural network for training to obtain a mathematical model for predicting abnormal states;
and inputting the traffic operation data and the road surface condition data of the prediction area into the abnormal prediction state mathematical model to obtain the abnormal prediction state.
By adopting the technical scheme, the method has the following beneficial technical effects: the neural network is used for carrying out abnormal state assessment, the neural network is combined with the tunnel traffic control technology, assessment results are fast and accurate, and manpower is saved.
Optionally, the control measure is acquired from a traffic control plan library, and the traffic control plan library stores the abnormal state and the corresponding control measure.
Optionally, the abnormal state includes at least one of a fire, a light injury accident, a heavy injury accident, a death accident, and other accidents except for a fire, a light injury accident, a heavy injury accident, and a death accident.
On the other hand, the intelligent management and control system suitable for the ultra-long tunnel traffic and giving consideration to efficiency and safety comprises a data acquisition module, an abnormal state evaluation module and an intelligent processing module which are sequentially connected; wherein, the first and the second end of the pipe are connected with each other,
the data acquisition module is used for acquiring traffic operation data and road surface condition data in a tunnel in real time;
the abnormal state evaluation module is used for carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and the intelligent processing module is used for implementing corresponding control measures according to different abnormal state evaluation results and carrying out graded early warning according to the abnormal state evaluation results.
Optionally, the traffic operation data is acquired by a radar, a traffic camera, and a microwave detector.
Optionally, the system further comprises a regional collaborative cloud control platform connected to the intelligent processing module and used for sending the alarm information to the traffic management terminal.
According to the technical scheme, compared with the prior art, the invention discloses the intelligent management and control method and the intelligent management and control system which are suitable for the ultra-long tunnel traffic and have efficiency and safety, and various management and control measures are timely and accurately formulated to be combined for tunnel traffic management and control, so that the accident risk is reduced; the tunnel condition is monitored in real time, a man-vehicle-road cooperative comprehensive sensing system is constructed, real-time monitoring and early warning under the comprehensive operation of a road network are achieved, and the purpose of improving the travel safety is achieved; the manager can implement corresponding management and control measures according to different abnormal state evaluation results in a targeted manner, so that the operation risk is effectively processed in time, the probability of accident occurrence is reduced, and the efficiency of tunnel operation is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a system configuration diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment 1 of the invention discloses an intelligent management and control method suitable for traffic of an extra-long tunnel and considering efficiency safety, which comprises the following specific steps as shown in fig. 1:
s1, acquiring traffic operation data and road surface condition data in the tunnel in real time;
s2, carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and S3, implementing corresponding control measures according to different abnormal state evaluation results, and performing grading early warning according to the abnormal state evaluation results.
Specifically, the traffic operation data comprises vehicle information, vehicle speed information and road hour traffic volume information; the road surface condition information comprises weather information, driving delay information, queuing length and road occupancy.
Further, the step of constructing the neural network model comprises:
s21, acquiring data samples, wherein the data samples comprise traffic operation data, road surface condition data and abnormal state data; wherein, the traffic operation data and the road surface condition data are used as input data, and the abnormal state data are used as output data;
s22, carrying out normalization processing on the input data, and dividing the data sample into 80% of training set and 20% of testing set;
s23, inputting the training set into a neural network for training to obtain a mathematical model for predicting abnormal states;
and S24, inputting the traffic operation data and the road surface condition data of the prediction area into the abnormal prediction state mathematical model to obtain the abnormal prediction state.
Further, the management and control measures are acquired from a traffic management and control plan library, and the abnormal state and the corresponding management and control measures are stored in the traffic management and control plan library.
The abnormal state includes at least one of a fire, a light injury accident, a heavy injury accident, a death accident, and other accidents except for the fire, the light injury accident, the heavy injury accident, and the death accident.
Furthermore, corresponding management and control measures are implemented according to different abnormal state evaluation results, and grading early warning is carried out according to the abnormal state evaluation results, and the specific implementation steps are as follows:
the early warning grades are divided into red early warning, orange early warning and blue early warning; when a fire disaster or a death accident occurs, red early warning is started; starting orange early warning when a light injury accident or a heavy injury accident occurs; starting blue early warning when other accidents happen;
the red early warning is started, the traffic control plan library responds to a first control measure, the first control measure is traffic tunnel ventilation control, and the method comprises the following specific steps:
s31, detecting the concentration of pollutants in the tunnel and traffic information, wherein the pollutants comprise smoke dust, carbon monoxide and nitrogen dioxide; the traffic information comprises traffic volume and vehicle speed;
s32, judging whether the current tunnel is a single tunnel or a tunnel group, if so, judging whether the concentration of pollutants in the tunnel exceeds the standard, if so, entering the step S33; if not, keeping the working state of the conventional ventilation fan in the tunnel, if the tunnel group is adopted, determining the pollutant suction amount of the human body in the tunnel according to the concentration of pollutants, including the smoke suction amount, the carbon monoxide suction amount and the nitrogen dioxide suction amount, judging whether the pollutant suction amount is larger than a set threshold value, if not, keeping the working state of the conventional ventilation fan in the tunnel, and if so, entering the step S33;
s33, predicting the traffic volume in the tunnel in the next time period according to the historical traffic volume data in the tunnel, and determining the number of fans in the working state required under different pollutants in the next time period;
s34, sequencing the number of fans in working state required under different pollutants in the next period, selecting the maximum value of the number of fans in working state as the number of fans required by the ventilation of the tunnel in the next period, and controlling the fans to work according to the required number of fans in the next period.
The orange early warning is started, the traffic control plan library responds to a second control measure, and the second control measure comprises the following specific steps: the acquired light injury accident and heavy injury accident information is uploaded to a regional cooperative cloud control platform, the cloud control platform issues the accident information in time by adopting a multi-level information issuing mode such as vehicle-road cooperative equipment, tunnel traffic broadcasting, signal control lamps, variable information boards, digital signs and the like according to the result, and emergency rescue is carried out through linkage of an emergency command scheduling and handling system and multiple departments.
Blue early warning starts, and the traffic control plan library responds to a third control measure, and the third control measure comprises the following specific steps: except for accidents such as fire disasters, light injury accidents, heavy injury accidents and death accidents, when personnel and equipment in the tunnel cannot work normally, the information in the tunnel is timely transmitted back to the regional cooperative control platform through the unmanned equipment, so that the monitoring personnel and managers can conveniently make reliable decisions to rescue.
On the other hand, an intelligent management and control system suitable for the ultra-long tunnel traffic with efficiency and safety is provided, as shown in fig. 2, and includes a data acquisition module, an abnormal state evaluation module, and an intelligent processing module, which are connected in sequence; wherein the content of the first and second substances,
the data acquisition module is used for acquiring traffic operation data and road surface condition data in the tunnel in real time;
the abnormal state evaluation module is used for carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and the intelligent processing module is used for implementing corresponding control measures according to different abnormal state evaluation results and carrying out graded early warning according to the abnormal state evaluation results.
Further, traffic operation data is acquired through a radar, a traffic camera and a microwave detector.
The intelligent traffic management system further comprises a regional collaborative cloud control platform which is connected with the intelligent processing module and used for sending the alarm information to the traffic management terminal.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. An intelligent management and control method suitable for ultra-long tunnel traffic and giving consideration to efficiency and safety is characterized by comprising the following specific steps of:
acquiring traffic operation data and pavement condition data in a tunnel in real time;
carrying out abnormal state evaluation by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and implementing corresponding control measures according to different abnormal state evaluation results, and performing grading early warning according to the abnormal state evaluation results.
2. The intelligent management and control method for efficiency and safety in ultra-long tunnel traffic as claimed in claim 1, wherein the traffic operation data includes vehicle information, vehicle speed information, and road hour traffic volume information.
3. The intelligent management and control method for efficiency and safety of ultra-long tunnel traffic according to claim 1, wherein the road condition information includes weather information, driving delay information, queuing length and road occupancy.
4. The intelligent management and control method applicable to both ultra-long tunnel traffic and efficiency safety as claimed in claim 1, wherein the step of constructing the neural network model is as follows:
acquiring data samples, wherein the data samples comprise traffic operation data, road surface condition data and abnormal state data; wherein the traffic operation data and the road surface condition data are used as input data, and the abnormal state data are used as output data;
carrying out normalization processing on the input data, and dividing the data sample into 80% of training set and 20% of testing set;
inputting the training set into a neural network for training to obtain a mathematical model for predicting abnormal states;
and inputting the traffic operation data and the road surface condition data of the prediction area into the abnormal prediction state mathematical model to obtain the abnormal prediction state.
5. The method according to claim 1, wherein the control measures are obtained from a traffic control plan library, and the traffic control plan library stores abnormal conditions and corresponding control measures.
6. The intelligent management and control method applicable to efficiency and safety of ultra-long tunnel traffic according to claim 1, wherein the abnormal condition includes at least one of a fire, a light injury accident, a heavy injury accident, a death accident and other accidents, and the other accidents are accidents except for the fire, the light injury accident, the heavy injury accident and the death accident.
7. An intelligent management and control system suitable for ultra-long tunnel traffic and giving consideration to efficiency and safety is characterized by comprising a data acquisition module, an abnormal state evaluation module and an intelligent processing module which are sequentially connected; wherein the content of the first and second substances,
the data acquisition module is used for acquiring traffic operation data and road surface condition data in a tunnel in real time;
the abnormal state evaluation module is used for evaluating the abnormal state by utilizing a neural network model according to the traffic operation data and the road surface condition data;
and the intelligent processing module is used for implementing corresponding control measures according to different abnormal state evaluation results and carrying out grading early warning according to the abnormal state evaluation results.
8. The intelligent management and control system applicable to both ultra-long tunnel traffic and efficiency safety as claimed in claim 7, wherein the traffic operation data is obtained by a radar, a traffic camera and a microwave detector.
9. The intelligent management and control system applicable to both efficiency and safety of ultra-long tunnel traffic as claimed in claim 7, further comprising a regional collaborative cloud control platform connected to the intelligent processing module for sending alarm information to a traffic management terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210213606.8A CN114582126A (en) | 2022-03-04 | 2022-03-04 | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210213606.8A CN114582126A (en) | 2022-03-04 | 2022-03-04 | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114582126A true CN114582126A (en) | 2022-06-03 |
Family
ID=81774020
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210213606.8A Pending CN114582126A (en) | 2022-03-04 | 2022-03-04 | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114582126A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014035639A (en) * | 2012-08-08 | 2014-02-24 | Toshiba Corp | Traffic accident occurrence prediction device, method and program |
| CN108665093A (en) * | 2018-04-19 | 2018-10-16 | 东南大学 | Traffic accidents Severity forecasting method based on deep learning |
| CN109034264A (en) * | 2018-08-15 | 2018-12-18 | 云南大学 | Traffic accident seriousness predicts CSP-CNN model and its modeling method |
| CN110992688A (en) * | 2019-11-26 | 2020-04-10 | 杭州普乐科技有限公司 | Intelligent traffic guidance system |
| CN111005890A (en) * | 2019-11-12 | 2020-04-14 | 招商局重庆交通科研设计院有限公司 | Traffic tunnel ventilation control method |
| CN111179601A (en) * | 2020-02-25 | 2020-05-19 | 青岛国信城市信息科技有限公司 | Tunnel traffic operation control method |
| CN113160593A (en) * | 2021-01-18 | 2021-07-23 | 重庆交通大学 | Mountain road driving safety early warning method based on edge cloud cooperation |
| CN113990018A (en) * | 2021-09-15 | 2022-01-28 | 上海腾盛智能安全科技股份有限公司 | Safety risk prediction system |
-
2022
- 2022-03-04 CN CN202210213606.8A patent/CN114582126A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014035639A (en) * | 2012-08-08 | 2014-02-24 | Toshiba Corp | Traffic accident occurrence prediction device, method and program |
| CN108665093A (en) * | 2018-04-19 | 2018-10-16 | 东南大学 | Traffic accidents Severity forecasting method based on deep learning |
| CN109034264A (en) * | 2018-08-15 | 2018-12-18 | 云南大学 | Traffic accident seriousness predicts CSP-CNN model and its modeling method |
| CN111005890A (en) * | 2019-11-12 | 2020-04-14 | 招商局重庆交通科研设计院有限公司 | Traffic tunnel ventilation control method |
| CN110992688A (en) * | 2019-11-26 | 2020-04-10 | 杭州普乐科技有限公司 | Intelligent traffic guidance system |
| CN111179601A (en) * | 2020-02-25 | 2020-05-19 | 青岛国信城市信息科技有限公司 | Tunnel traffic operation control method |
| CN113160593A (en) * | 2021-01-18 | 2021-07-23 | 重庆交通大学 | Mountain road driving safety early warning method based on edge cloud cooperation |
| CN113990018A (en) * | 2021-09-15 | 2022-01-28 | 上海腾盛智能安全科技股份有限公司 | Safety risk prediction system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10226982B2 (en) | Automatic vehicle climate control based on predicted air quality | |
| CN110164130B (en) | Traffic incident detection method, device, equipment and storage medium | |
| CN107798876B (en) | Road traffic abnormal jam judging method based on event | |
| CN109255955B (en) | Highway accident monitoring method and system based on foundation and empty foundation cooperative sensing | |
| CN110085029B (en) | Highway inspection system and method based on rail type inspection robot | |
| CN104464295B (en) | A kind of overhead Entrance ramp intelligence restricted driving method based on video | |
| CN112885100A (en) | Highway tunnel operation management and control system | |
| CN111721209A (en) | Road tunnel engineering safety real-time monitoring management system based on big data | |
| CN107067730B (en) | Network appointment vehicle-man-vehicle inconsistency monitoring method based on bayonet equipment | |
| CN113888873B (en) | Expressway accident detection and early warning system and method based on short-time traffic flow | |
| CN109993098A (en) | City vehicle black smoke intelligent recognition big data analysis system and method | |
| CN109785627A (en) | A kind of crossroad access flux monitoring system | |
| CN111754767B (en) | Highway traffic early warning method, device and system based on intelligent lamp pole | |
| CN114863675A (en) | Method for predicting position of key vehicle and alarming abnormity based on road police data fusion | |
| CN116597642A (en) | Traffic jam condition prediction method and system | |
| CN114582126A (en) | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety | |
| CN110060370B (en) | Equivalent statistical method for times of rapid acceleration and rapid deceleration of vehicle | |
| CN116935659A (en) | High-speed service area bayonet vehicle auditing system and method thereof | |
| CN214475530U (en) | Highway tunnel operation management and control system | |
| CN114582083B (en) | Tunnel monitoring multi-sensor data fusion fire disaster early warning method | |
| CN113628461B (en) | Traffic supervision device and method | |
| CN115941746A (en) | AI-based intelligent early warning system for high-risk area of oil and gas pipeline | |
| CN104809869A (en) | Viaduct entrance ramp traffic state identifying method based on lifting height limiting rack | |
| CN114170800A (en) | Method and system for predicting and disposing traffic cloud control platform active event | |
| CN112800844B (en) | Video monitoring intelligent control system |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220603 |