CN116013092A - Road traffic management system based on cloud computing - Google Patents
Road traffic management system based on cloud computing Download PDFInfo
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Abstract
The invention discloses a road traffic management system based on cloud computing, which comprises: the system comprises a data acquisition module, a data processing and analyzing module, a storage module and a flow regulation and control module; the data acquisition module is used for acquiring initial man-vehicle data of a plurality of intersections in real time; the data processing and analyzing module processes and analyzes the initial man-vehicle data to obtain comprehensive flow information; the storage module is used for storing initial man-vehicle data and comprehensive flow information; the traffic flow regulation and control module is used for constructing a traffic optimization model and regulating and controlling traffic flow based on the comprehensive traffic flow information and the traffic optimization model. According to the invention, vehicle flow information is acquired through multiple channels, and non-motor vehicle flow information is established through a head identification model; according to the historical information, an initial model is obtained, iterative optimization is carried out on the model by combining flow information, instant guide information is obtained, and accuracy of traffic optimization management is improved; and a recommended driving route is obtained through path planning, so that vehicle diversion is realized, and traffic jam is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic management, and particularly relates to a road traffic management system based on cloud computing.
Background
The urban traffic network brings convenience to people's life, and the condition of multiple routes of vehicles also increases great pressure on traffic, and more difficulties are brought to traffic control and management. In traffic management, intersections are the most complex and of highest importance. Whether the traffic control design of the crossing is reasonable plays a very important role in the smoothness of urban traffic. The traffic control design of the crossing is generally completed by a traffic design institute, and needs numerous parameter support, on one hand, the actual measurement needs to be carried out on the crossing site, and on the other hand, the traffic flow OD (traffic starting point) information of different directions of the crossing needs to be obtained in a manual counting mode. The workload is large and the accuracy is difficult to guarantee. The current domestic intelligent traffic industry is biased to the application of vehicle-mounted positioning, violation detection and the like, and traffic flow analysis of intersections is rarely involved.
Currently, traffic management is mainly to manage urban traffic by managing traffic lights. The traffic light management is to manually configure the switching time of the traffic light corresponding to the traffic signal lamp in advance according to the cognitive experience of the staff on factors such as vehicles at road intersections, pedestrian flow, traffic busy time periods, main and auxiliary roads and the like, so as to realize the control of the traffic signal lamp. However, the control precision corresponding to the management of the traffic control equipment is not high, the switching time of the traffic lights corresponding to the traffic signals cannot be dynamically adjusted, and the resource allocation is unreasonable, so that unnecessary traffic jams are caused.
Disclosure of Invention
The invention aims to provide a road traffic management system based on cloud computing, which aims to solve the problems existing in the prior art.
In order to achieve the above object, the present invention provides a road traffic management system based on cloud computing, comprising:
the system comprises a data acquisition module, a data processing and analyzing module, a storage module and a flow regulation and control module;
the data acquisition module is used for acquiring initial man-vehicle data of a plurality of intersections in real time;
the data processing and analyzing module is used for processing and analyzing the initial man-vehicle data to obtain comprehensive flow information;
the storage module is used for storing initial man-vehicle data and comprehensive flow information; the integrated flow information includes multi-dimensional vehicle flow information and non-motor vehicle flow information.
The traffic flow regulation and control module regulates and controls traffic flow based on the comprehensive flow information to realize road traffic management.
Optionally, the data acquisition module comprises a vehicle information acquisition unit and a non-motor vehicle information acquisition unit;
the vehicle information acquisition unit is used for acquiring vehicle data of a running vehicle and comprises a video detection subunit and a geomagnetic detection subunit;
the non-motor vehicle information acquisition unit acquires non-motor vehicle data and personnel data through acquisition of video streams.
Optionally, the geomagnetic detection subunit is configured to obtain, in real time, first vehicle information of a vehicle running on a road, and transmit the first vehicle information to the data analysis processing module through the built-in communication module, where the first vehicle information includes a number of vehicles, a running speed, and a running direction;
the video detection subunit acquires vehicle video information in a fixed period through video monitoring.
Optionally, the data processing analysis module comprises a target detection unit, a data fusion unit and a video stream processing unit;
the target detection unit is used for carrying out framing treatment on the vehicle video information, determining a reference frame and a frame to be registered, matching the frame to be registered by using the reference frame to obtain a registered frame set, and carrying out target detection on the registered frame set by using a deep learning target detection model trained in advance to obtain second vehicle information;
the data fusion unit carries out fusion calculation on the first vehicle information and the second vehicle information to obtain multidimensional vehicle flow information;
the video stream processing unit comprises a head model subunit and a counting unit; and constructing a head recognition model through the head model subunit, intercepting the image by the counting unit according to the preset time by using the video stream, and recognizing and counting the heads of pedestrians in the intercepted image through the head recognition model to obtain the flow information of the non-motor vehicle.
Optionally, the head model subunit constructs an initial head recognition model, initially acquires a history monitoring video, extracts a head image in the history monitoring video as a positive sample set, uses a background image outside the head image as a negative sample set, and trains the initial head recognition model by using the positive sample set and the negative sample set to obtain the head recognition model.
Optionally, the flow regulation and control module comprises a model acquisition unit and a regulation and control optimization unit;
the model acquisition unit is used for constructing a traffic optimization model and performing initial training;
the regulation and control optimizing unit is used for carrying out iterative optimization on the traffic optimizing model after initial training, and generating instant guidance information by combining the comprehensive flow information.
Optionally, the road traffic management system further comprises a vehicle-mounted terminal module, wherein the vehicle-mounted terminal module comprises a safe driving unit and a route recommending unit, and the safe driving unit and the route recommending unit are both connected with the storage module, and the safe driving unit is used for guiding a driver to drive and actively intervening in an abnormal vehicle; the route recommending unit is used for acquiring the current position of the vehicle and carrying out real-time route planning according to the comprehensive flow information in the storage module.
Optionally, the safe driving unit is used for judging an abnormal vehicle when detecting that the vehicle does not reach the expected position or the running direction at the expected time is abnormal, starting the vehicle to search for the position information of the vehicle obtained by the positioning mode, obtaining the relevant duty personnel through the position information, and sending out a notification.
The invention has the technical effects that:
according to the invention, an Internet of things network is established through the built-in communication module of the geomagnetic detector and the monitoring device, and the vehicle-mounted terminal arranged in the vehicle acquires vehicle information in real time and transmits the vehicle information through the Internet of things network; acquiring vehicle flow information through multiple channels, and identifying intercepted images of intersection videos by establishing a head identification model to acquire non-motor vehicle flow information; initial training is carried out on the traffic optimization model according to the historical information, so that the accuracy of traffic optimization management is improved; performing iterative optimization on the model by combining the real-time information to obtain instant guide information, transmitting the instant guide information to the traffic control equipment, and performing traffic optimization management on the traffic control equipment; by means of path planning, a driving route is recommended, vehicle diversion is achieved, the rationality of resource allocation is improved, and traffic jam is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
fig. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
As shown in fig. 1, in this embodiment, a road traffic management system based on cloud computing is provided, including:
the system comprises a data acquisition module, a data processing and analyzing module, a storage module and a flow regulation and control module;
the data acquisition module is used for acquiring initial man-vehicle data of a plurality of intersections in real time;
the data processing and analyzing module processes and analyzes the initial man-vehicle data to obtain comprehensive flow information;
the storage module is used for storing initial man-vehicle data and comprehensive flow information; the integrated flow information includes multi-dimensional vehicle flow information and non-motor vehicle flow information.
The traffic flow regulation and control module regulates and controls traffic flow based on the comprehensive flow information, and road traffic management is achieved.
Cloud computing technology is a product of a fusion of multiple technologies, such as distributed computing, virtualization, network storage, network technology, and the like. The cloud computing technology has the characteristics of virtualization, high reliability, strong universality, high expandability and the like. The embodiment relates to a large amount of calculation analysis, adopts cloud calculation to perform distributed calculation, has extremely large number of intersections, has extremely large data volume to be stored, and is convenient for data acquisition and processing by adopting cloud storage.
Specifically, the data acquisition module comprises a vehicle information acquisition unit and a non-motor vehicle information acquisition unit;
the vehicle information acquisition unit is used for acquiring vehicle data of a running vehicle and comprises a video detection subunit and a geomagnetic detection subunit;
the non-motor vehicle information acquisition unit acquires non-motor vehicle data and personnel data through acquisition of video streams.
Specifically, the geomagnetic detection subunit is used for acquiring first vehicle information of a vehicle running on a road in real time, and transmitting the first vehicle information to the data analysis processing module through the built-in communication module, wherein the first vehicle information comprises the number of vehicles, the running speed and the running direction;
the geomagnetic detectors may be arranged in a scattered manner according to the lanes;
the method can be implemented, the video monitoring obtains the vehicle video information in a fixed period, the fixed period is determined based on the running speed of the vehicle passing through the intersection, and the vehicle video information is obtained in a segmented mode.
The video detection subunit acquires the vehicle video information in a fixed period through video monitoring.
Specifically, the data processing and analyzing module comprises a target detection unit, a data fusion unit and a video stream processing unit;
the target detection unit is used for carrying out framing treatment on the vehicle video information, determining a reference frame and a frame to be registered, matching the frame to be registered by utilizing the reference frame to obtain a registered frame set, and carrying out target detection on the registered frame set by adopting a deep learning target detection model trained in advance to obtain second vehicle information;
the reference frame can be taken as the first frame of the vehicle video information;
the data fusion unit carries out fusion calculation on the first vehicle information and the second vehicle information to obtain multidimensional vehicle flow information;
the first vehicle information can be obtained directly by the geomagnetic detection subunit; the target detection unit is used for preprocessing the vehicle video information and then obtaining second vehicle information through target detection, wherein the second vehicle information comprises vehicle quantity information;
and comparing and fusing the first vehicle information and the second vehicle information to obtain a difference value between the first vehicle information and the second vehicle information, updating the target detection model based on the difference value, further reducing the resource investment of the detection device, and obtaining more accurate vehicle flow information.
The video stream processing unit comprises a head model subunit and a counting unit; and constructing a head recognition model through a head model subunit, intercepting the image by a counting unit according to the preset time, and recognizing and counting the heads of the pedestrians in the intercepted image through the head recognition model to obtain the flow information of the non-motor vehicle.
The preset time may be set to 1S.
Specifically, the head model subunit builds an initial head recognition model, initially acquires a historical monitoring video, extracts a head image in the historical monitoring video as a positive sample set, uses a background image outside the head image as a negative sample set, and trains the initial head recognition model by using the positive sample set and the negative sample set to obtain the head recognition model.
Specifically, the flow regulation and control module comprises a model acquisition unit and a regulation and control optimization unit;
the model acquisition unit is used for constructing a traffic optimization model and performing initial training;
the regulation and control optimizing unit is used for carrying out iterative optimization on the traffic optimizing model after initial training and generating instant guiding information by combining the comprehensive flow information.
The initial training process of the traffic optimization model comprises the following steps:
acquiring initial man-vehicle data and comprehensive flow information stored in the storage module as first historical data; and acquiring a historical monitoring video, acquiring second historical data based on the historical monitoring video, and performing cloud computing processing on the historical data to acquire a traffic optimization model.
The road traffic management system further comprises a vehicle-mounted terminal module, wherein the vehicle-mounted terminal module comprises a safe driving unit and a route recommending unit, the safe driving unit and the route recommending unit are connected with the storage module, and the safe driving unit is used for guiding a driver to drive and actively intervening in an abnormal vehicle; the route recommending unit is used for acquiring the current position of the vehicle and carrying out real-time route planning according to the comprehensive flow information in the storage module.
Specifically, the safe driving unit is used for judging an abnormal vehicle when detecting that the vehicle does not reach the expected position or the running direction at the expected time is abnormal, starting the vehicle to search for the position information of the vehicle obtained by the positioning mode, obtaining the relevant duty personnel through the position information, and sending out a notification.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A road traffic management system based on cloud computing, comprising:
the system comprises a data acquisition module, a data processing and analyzing module, a storage module and a flow regulation and control module;
the data acquisition module is used for acquiring initial man-vehicle data of a plurality of intersections in real time;
the data processing and analyzing module is used for processing and analyzing the initial man-vehicle data to obtain comprehensive flow information;
the storage module is used for storing initial man-vehicle data and comprehensive flow information; the comprehensive flow information comprises multidimensional vehicle flow information and non-motor vehicle flow information;
the traffic flow regulation and control module regulates and controls traffic flow based on the comprehensive flow information to realize road traffic management.
2. The cloud computing based road traffic management system of claim 1, wherein,
the data acquisition module comprises a vehicle information acquisition unit and a non-motor vehicle information acquisition unit;
the vehicle information acquisition unit is used for acquiring vehicle data of a running vehicle and comprises a video detection subunit and a geomagnetic detection subunit;
the non-motor vehicle information acquisition unit acquires non-motor vehicle data and personnel data through acquisition of video streams.
3. The cloud computing based road traffic management system of claim 2, wherein,
the geomagnetic detection subunit is used for acquiring first vehicle information of a vehicle running on a road in real time, and transmitting the first vehicle information to the data analysis processing module through the built-in communication module, wherein the first vehicle information comprises the number of vehicles, the running speed and the running direction;
the video detection subunit acquires vehicle video information in a fixed period through video monitoring.
4. The road traffic management system based on cloud computing as recited in claim 3, wherein,
the data processing and analyzing module comprises a target detection unit, a data fusion unit and a video stream processing unit;
the target detection unit is used for carrying out framing treatment on the vehicle video information, determining a reference frame and a frame to be registered, matching the frame to be registered by using the reference frame to obtain a registered frame set, and carrying out target detection on the registered frame set by using a deep learning target detection model trained in advance to obtain second vehicle information;
the data fusion unit carries out fusion calculation on the first vehicle information and the second vehicle information to obtain multidimensional vehicle flow information;
the video stream processing unit comprises a head model subunit and a counting unit; and constructing a head recognition model through the head model subunit, intercepting the image by the counting unit according to the preset time by using the video stream, and recognizing and counting the heads of pedestrians in the intercepted image through the head recognition model to obtain the flow information of the non-motor vehicle.
5. The cloud computing based road traffic management system as claimed in claim 4, wherein,
the head model subunit builds an initial head recognition model, initially acquires a historical monitoring video, extracts head images in the historical monitoring video to serve as a positive sample set, and uses background images outside the head images as a negative sample set, and trains the initial head recognition model by utilizing the positive sample set and the negative sample set to obtain a head recognition model.
6. The cloud computing based road traffic management system of claim 1, wherein,
the flow regulation and control module comprises a model acquisition unit and a regulation and control optimization unit;
the model acquisition unit is used for constructing a traffic optimization model and performing initial training;
the regulation and control optimizing unit is used for carrying out iterative optimization on the traffic optimizing model after initial training, and generating instant guidance information by combining the comprehensive flow information.
7. The road traffic management system based on cloud computing as recited in claim 1, further comprising a vehicle-mounted terminal module, wherein the vehicle-mounted terminal module comprises a safe driving unit and a route recommending unit, the safe driving unit and the route recommending unit are both connected with the storage module, and the safe driving unit is used for guiding a driver to drive and actively intervening in an abnormal vehicle; the route recommending unit is used for acquiring the current position of the vehicle and carrying out real-time route planning according to the comprehensive flow information in the storage module.
8. The cloud computing-based road traffic management system according to claim 7, wherein said safe driving unit is configured to determine an abnormal vehicle when it is detected that the vehicle does not reach an expected position or a traveling direction abnormality at an expected time, and to start the vehicle to find and locate a mode to acquire position information of the vehicle, obtain a relevant duty person through the position information, and issue a notification.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101464946A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | Detection method based on head identification and tracking characteristics |
| CN103824070A (en) * | 2014-03-24 | 2014-05-28 | 重庆邮电大学 | Rapid pedestrian detection method based on computer vision |
| CN105635696A (en) * | 2016-03-22 | 2016-06-01 | 南阳理工学院 | Statistical method and device |
| CN107085953A (en) * | 2017-06-06 | 2017-08-22 | 郑州云海信息技术有限公司 | A kind of Intelligent traffic management systems and method based on cloud computing |
| 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 |
| CN109637137A (en) * | 2018-12-29 | 2019-04-16 | 浙江方大智控科技有限公司 | Traffic control system based on bus or train route collaboration |
| CN111860390A (en) * | 2020-07-27 | 2020-10-30 | 西安建筑科技大学 | Elevator waiting number detection and statistics method, device, equipment and medium |
| WO2022156520A1 (en) * | 2021-01-25 | 2022-07-28 | 国汽智控(北京)科技有限公司 | Cloud-road collaborative automatic driving model training method and system, and cloud-road collaborative automatic driving model calling method and system |
-
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- 2022-12-07 CN CN202211561623.7A patent/CN116013092A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101464946A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | Detection method based on head identification and tracking characteristics |
| CN103824070A (en) * | 2014-03-24 | 2014-05-28 | 重庆邮电大学 | Rapid pedestrian detection method based on computer vision |
| CN105635696A (en) * | 2016-03-22 | 2016-06-01 | 南阳理工学院 | Statistical method and device |
| 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 |
| CN107085953A (en) * | 2017-06-06 | 2017-08-22 | 郑州云海信息技术有限公司 | A kind of Intelligent traffic management systems and method based on cloud computing |
| CN109637137A (en) * | 2018-12-29 | 2019-04-16 | 浙江方大智控科技有限公司 | Traffic control system based on bus or train route collaboration |
| CN111860390A (en) * | 2020-07-27 | 2020-10-30 | 西安建筑科技大学 | Elevator waiting number detection and statistics method, device, equipment and medium |
| WO2022156520A1 (en) * | 2021-01-25 | 2022-07-28 | 国汽智控(北京)科技有限公司 | Cloud-road collaborative automatic driving model training method and system, and cloud-road collaborative automatic driving model calling method and system |
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