CN115188208A - Traffic control method based on big data and computer equipment - Google Patents
Traffic control method based on big data and computer equipment Download PDFInfo
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- CN115188208A CN115188208A CN202210809580.3A CN202210809580A CN115188208A CN 115188208 A CN115188208 A CN 115188208A CN 202210809580 A CN202210809580 A CN 202210809580A CN 115188208 A CN115188208 A CN 115188208A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012790 confirmation Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 2
- 238000012806 monitoring device Methods 0.000 description 16
- 230000002035 prolonged effect Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/085—Controlling traffic signals using a free-running cyclic timer
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- 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
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- 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
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- 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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- 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
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- 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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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Abstract
The invention is suitable for the technical field of data processing, and particularly relates to a traffic control method and computer equipment based on big data, wherein the method comprises the following steps: collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions; carrying out picture identification on the intersection traffic picture, and counting the number of vehicles on roads in all directions; acquiring traffic data of adjacent intersections, and determining traffic flow passing requirements of the current intersection according to the traffic data; and generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on the road of each direction of the current intersection, and executing. According to the traffic control method based on the big data, provided by the embodiment of the invention, the number of vehicles and the number of personnel on each direction of the road are determined by identifying the images of the intersections, and then the traffic information of the adjacent intersections is taken to judge the traffic pressure of the current intersection, so that the traffic time of each direction of the current intersection is actively adjusted, the congestion of each direction of the road is avoided, and the traffic efficiency is maximized.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a traffic control method and computer equipment based on big data.
Background
Big data, or mass data, refers to the data that is too large to be captured, managed, processed, and organized into information that helps enterprise business decisions to be more positive within a reasonable time through mainstream software tools.
With the gradual development of big data technology, the big data technology is applied to various aspects in life, including clothes and eating and housing of people, for example, in the aspect of travel, the big data is used for determining the co-traffic jam condition of each road, so that people can conveniently select a proper travel road.
In the traffic guiding process, guidance is usually performed through fixed traffic lights, and active adaptation cannot be performed according to the change of the travel flow, so that the guidance efficiency is low.
Disclosure of Invention
The embodiment of the invention aims to provide a traffic control method based on big data, and aims to solve the problem that the prior art cannot actively adapt to the change of travel flow.
The embodiment of the invention is realized in such a way that a traffic control method based on big data comprises the following steps:
collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions;
carrying out picture identification on the intersection traffic picture, and counting the number of vehicles on roads in all directions;
acquiring traffic data of adjacent intersections, and determining traffic flow passing requirements of the current intersection according to the traffic data;
and generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on the road of each direction of the current intersection, and executing.
Preferably, the step of performing image recognition on the intersection traffic image and counting the number of vehicles on each road specifically includes:
performing road identification according to the intersection traffic picture, and determining the number of roads contained in the intersection traffic picture;
identifying vehicles according to the intersection traffic pictures, and counting the number of the vehicles waiting to pass on each road;
and acquiring the positioning of the current intersection, and determining the passing direction of each road corresponding to the current intersection.
Preferably, the step of acquiring traffic data of an adjacent intersection and determining the traffic flow passing demand of the current intersection according to the traffic data specifically includes:
calling a corresponding regional map according to the positioning information of the current intersection;
determining an intersection adjacent to the intersection according to the regional map, and acquiring corresponding traffic data;
and judging the number of vehicles going to the adjacent intersection according to the traffic data to obtain the vehicle passing requirement.
Preferably, the generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on each road of the current intersection and executing steps specifically include:
calling the traffic history data of the current intersection, and determining the traffic speed of the current intersection in each direction according to the traffic history data;
determining the passing time on the roads in all directions according to the passing speed in all directions and the number of vehicles on the roads in all directions;
and generating a signal lamp adjusting scheme according to the traffic time on each road.
Preferably, the vehicles at the adjacent intersection can directly access the current intersection.
Preferably, the intersection traffic picture is obtained from intersection monitoring.
Another object of an embodiment of the present invention is to provide a computer device for traffic control based on big data, wherein the computer device includes:
the system comprises an image acquisition module, a data processing module and a data processing module, wherein the image acquisition module is used for acquiring intersection traffic pictures, and the intersection traffic pictures comprise images corresponding to roads in all directions;
the picture identification module is used for carrying out picture identification on the traffic pictures at the intersections and counting the number of vehicles on roads in all directions;
the demand confirmation module is used for acquiring traffic data of adjacent intersections and determining traffic flow passing demands of the current intersection according to the traffic data;
and the signal lamp adjusting module is used for generating a signal lamp adjusting scheme according to the traffic flow passing demand and the quantity of vehicles on the road of each direction of the current intersection and executing the signal lamp adjusting scheme.
Preferably, the picture recognition module includes:
the road counting unit is used for identifying roads according to the intersection traffic pictures and determining the number of the roads contained in the intersection traffic pictures;
the vehicle counting unit is used for identifying vehicles according to the intersection traffic pictures and counting the number of the vehicles waiting to pass on each road;
and the road positioning unit is used for acquiring the positioning of the current intersection and determining the passing direction of each road corresponding to the current intersection.
Preferably, the requirement confirming module includes:
the map calling unit is used for calling a corresponding regional map according to the positioning information of the current intersection;
the data acquisition unit is used for determining an intersection adjacent to the intersection according to the regional map and acquiring corresponding traffic data;
and the data calculation unit is used for judging the number of the vehicles going to the intersection from the adjacent intersection according to the traffic data to obtain the vehicle passing requirement.
Preferably, the signal lamp adjusting module includes:
the historical data calling unit is used for calling the traffic historical data of the current intersection and determining the traffic speed of the current intersection in each direction according to the traffic historical data;
the traffic time calculation unit is used for determining the traffic time on each road according to the traffic speed and the number of vehicles on each road;
and the scheme generating unit is used for generating a signal lamp adjusting scheme according to the passing time on each road.
According to the traffic control method based on the big data, provided by the embodiment of the invention, the number of vehicles and the number of people on each direction of the road are determined by identifying the images of the intersections, and then the traffic information of the adjacent intersections is called to judge the traffic pressure of the current intersection, so that the traffic time of each direction of the current intersection is actively adjusted, the congestion of the road in each direction is avoided, and the traffic efficiency is maximized.
Drawings
Fig. 1 is a flowchart of a traffic control method based on big data according to an embodiment of the present invention;
fig. 2 is a flowchart of steps of performing image recognition on a road traffic image and counting the number of vehicles on each road according to an embodiment of the present invention;
fig. 3 is a flowchart of steps of acquiring traffic data of an adjacent intersection and determining a traffic flow passing demand of a current intersection according to the traffic data according to an embodiment of the present invention;
FIG. 4 is a flowchart of steps executed to generate a signal light adjustment scheme according to traffic demands and the number of vehicles on each road of a current intersection according to an embodiment of the present invention;
FIG. 5 is an architecture diagram of a big data based traffic control computer apparatus according to an embodiment of the present invention;
fig. 6 is an architecture diagram of a picture recognition module according to an embodiment of the present invention;
FIG. 7 is a block diagram of a demand validation module according to an embodiment of the present invention;
fig. 8 is an architecture diagram of a signal lamp adjusting module according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
With the gradual development of big data technology, the big data technology is applied to various aspects in life, including clothes and eating and living of people, for example, in the aspect of travel, the big data is used for determining the co-walking congestion condition of each road, so that people can conveniently select a proper travel road. In the traffic guidance process, guidance is usually performed through fixed traffic lights, and active adaptation cannot be performed according to the change of travel flow, so that the guidance efficiency is low.
In the invention, the number of vehicles and the number of personnel on each direction of the road are determined by identifying the images of the intersections, and then the traffic information of the adjacent intersections is taken to judge the traffic pressure of the current intersection, so that the traffic time of each direction of the current intersection is actively adjusted, the congestion of each direction of the road is avoided, and the traffic efficiency is maximized.
As shown in fig. 1, a flow chart of a traffic control method based on big data according to an embodiment of the present invention is provided, where the method includes:
s100, collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions.
In this step, a road traffic picture is collected, a corresponding monitoring device is arranged at each traffic intersection, each lane is recorded through the monitoring device, the traffic picture at the intersection is directly obtained from the monitoring device, and when the traffic picture at the intersection is obtained, the traffic picture is obtained according to a preset time interval, specifically, one frame of picture is extracted from a video collected by the monitoring device every 1 second.
S200, carrying out picture identification on the road traffic picture, and counting the number of vehicles on the road in each direction.
In this step, carry out the picture identification to intersection traffic picture, the supervisory equipment that every road set up all comes to the vehicle and shoots, utilizes picture identification technology to confirm the vehicle quantity that waits for the current in each lane, and is concrete, also can carry out the quantity statistics to the pedestrian that each direction needs to pass through the mode of portrait discernment to finally adjust the pedestrian traffic command lamp according to pedestrian's quantity, guarantee current efficiency.
S300, traffic data of adjacent intersections are obtained, and traffic flow passing requirements of the current intersection are determined according to the traffic data.
In this step, traffic data of adjacent intersections are obtained, similarly, a monitoring device is arranged at each intersection, and then a monitoring device is arranged at each adjacent intersection, so that vehicle conditions in each lane are also collected, because two intersections are adjacent, a traffic flow enters the current intersection at each adjacent intersection, and vehicles at adjacent intersections are constantly converged to avoid congestion at the current intersection, so that the time for allowing vehicles to pass through on each road at the current intersection can be adjusted, specifically, in order to determine the condition of entering the vehicles at the current intersection from the adjacent intersections, historical data of the current intersection is obtained, big data analysis is performed on the historical data of the current intersection, the proportion of the vehicles entering each lane at each passing interval time at each time period is judged, for example, between 9 and 10 am every day, from the adjacent intersection, 30% of the vehicles entering the current intersection have left turn, 40% of the vehicles enter the right turn, 30% of the vehicles enter the straight turn, in the big data analysis process, the data are selected to analyze the data in the preset time period, such as the time period of entering the right turn, the vehicles enter the right turn, and the total number of the vehicles is counted according to obtain the total number of the vehicles entering the current intersection, and the total number of the vehicles entering the left turn, and the vehicles in each day is divided into the total number of the 30, and the total number of the vehicles in each day, and the current intersection is determined in each time period, and the following time period, wherein the time period.
And S400, generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on the road of each direction of the current intersection, and executing.
In the step, a signal lamp adjusting scheme is generated according to traffic flow passing demands and the number of vehicles on each road of the current intersection, when adjustment is performed, specifically, if a direction and a direction are perpendicular to each other, a large number of vehicles will gather into the direction a road at the adjacent intersection, and the vehicle waiting to pass exists on the direction a road, the passing time of the direction a to the road is prolonged, namely the green light starting time of the direction a to the road is prolonged, for example, the distance is prolonged from 50 seconds to 60 seconds, for the direction B to the road, the green light starting time is appropriately shortened, namely more passing time is provided for the direction a to the road by shortening the passing time of the direction B to the road, and after the signal lamp adjusting scheme is generated, the signal lamp adjusting scheme is sent to a traffic lamp control device for execution.
As shown in fig. 2, as a preferred embodiment of the present invention, the step of performing image recognition on the intersection traffic image and counting the number of vehicles on roads in each direction specifically includes:
s201, performing road identification according to the intersection traffic picture, and determining the number of roads contained in the intersection traffic picture.
In this step, road recognition is performed according to the intersection traffic pictures, line recognition is performed, and the positions of the lane lines are determined, so that lanes are divided, the number of roads included in each intersection traffic picture is counted, and specifically, non-motor lanes can also be recognized.
S202, vehicle identification is carried out according to the intersection traffic pictures, and the number of vehicles waiting to pass on each road is counted.
In the step, road identification is carried out according to the intersection traffic picture, when image identification is carried out, line drawing processing is carried out on the image firstly, and the image is converted into a line drawing, so that the outline and the vehicle characteristics of the vehicle are determined according to the lines, and the vehicle characteristics are the outline of a front windshield of the vehicle, the outline of wheels and the like, so that the number of the vehicles on each road is determined.
S203, obtaining the location of the current intersection and determining the passing direction of each road corresponding to the current intersection.
In this step, the location of the current intersection is obtained, and when the location is performed, the location can be obtained according to the location device, and the query can also be performed according to the number of the monitoring device, so that the installation position of each monitoring device is determined, that is, the location of the intersection can be determined.
As shown in fig. 3, as a preferred embodiment of the present invention, the step of acquiring traffic data of an adjacent intersection and determining a traffic flow passing demand of a current intersection according to the traffic data specifically includes:
s301, calling a corresponding regional map according to the positioning information of the current intersection.
In this step, according to the positioning information of the current intersection, a corresponding area map is called, and after the positioning is obtained, the map of the current position is called, so that the map of the corresponding position is intercepted from the global map to obtain the area map, and specifically, a circle is drawn by a preset radius with the positioning center of the current intersection to determine the area map.
S302, according to the regional map, the crossing adjacent to the crossing is determined, and corresponding traffic data are obtained.
In the step, the crossing adjacent to the crossing is determined according to the area map, the distribution condition of lanes on each road in the map is identified, so that the position of a traffic signal lamp on each road is determined, the adjacent crossing is determined according to the traffic signal lamp, and traffic data corresponding to the crossing is obtained, wherein the traffic data comprises the waiting number in each lane.
And S303, judging the number of the vehicles going to the adjacent intersection to the intersection according to the traffic data to obtain the vehicle passing requirement.
In the step, the number of the vehicles going to the intersection at the adjacent intersection is judged according to the traffic data, and corresponding vehicles waiting to pass exist in the lane of each intersection, so that the number of the vehicles going to the intersection can be determined according to the lane where the vehicles are located.
As shown in fig. 4, as a preferred embodiment of the present invention, the steps of generating a signal light adjustment scheme according to the traffic flow passing demand and the number of vehicles on each road of the current intersection, and executing the signal light adjustment scheme specifically include:
s401, obtaining the traffic history data of the current intersection, and determining the traffic speed of the current intersection in each direction according to the traffic history data.
In the step, the traffic history data of the current intersection is retrieved, the traffic speed in each traffic direction is counted in the history data, and the weather data including the temperature, the date and the weather of the area where the current intersection is located is obtained during counting, so that the traffic speed matched with the weather data is retrieved when the traffic speed is retrieved, and the traffic speed of the current intersection is obtained in each direction.
S402, determining the passing time on the roads in each direction according to the passing speed in each direction and the number of vehicles on the roads in each direction.
And S403, generating a signal lamp adjusting scheme according to the traffic time on the roads in each direction.
In the step, the passing time on each road is determined according to the passing speed and the number of vehicles on each road, when the passing time is calculated, the calculation is carried out on the basis of the sum of the number of vehicles waiting to pass through at the current intersection and the number of vehicles going to the current road, so that whether the lighting time of the current traffic light meets the requirement or not is judged, if the lighting time cannot meet the requirement, the adjustment is carried out according to the calculated passing time to generate a signal light adjusting scheme, and the signal light adjusting scheme is sent to traffic light control equipment at the current intersection for execution.
As shown in fig. 5, a big data based traffic control computer device provided in an embodiment of the present invention includes:
the image acquisition module 100 is configured to acquire intersection traffic pictures, where the intersection traffic pictures include images corresponding to roads in all directions.
In the computer device, an image acquisition module 100 acquires a road traffic picture, a corresponding monitoring device is arranged at each traffic intersection, each lane is recorded by the monitoring device, the traffic picture at the intersection is directly acquired from the monitoring device, and when the traffic picture is acquired, the traffic picture is acquired according to a preset time interval, specifically, 1 second can be adopted, that is, one frame of picture is extracted from a video acquired by the monitoring device every one second.
And the picture identification module 200 is used for carrying out picture identification on the traffic pictures at the intersections and counting the number of vehicles on roads in all directions.
In this computer equipment, picture identification module 200 carries out the picture identification to the intersection traffic picture, and the supervisory equipment that every road set up all is to coming to the vehicle and shoot, utilizes picture identification technique to confirm the vehicle quantity that waits for the current in each lane, and is concrete, also can carry out the quantity statistics to the pedestrian that each direction needs to pass through the mode of portrait discernment to finally come to adjust the current command lamp of pedestrian according to the quantity of pedestrian, guarantee current efficiency.
The demand confirmation module 300 is configured to obtain traffic data of an adjacent intersection, and determine a traffic demand of a current intersection according to the traffic data.
In the computer device, the demand confirmation module 300 obtains traffic data of adjacent intersections, similarly, each intersection is provided with a monitoring device, and then the adjacent intersection is also provided with a monitoring device, so that the vehicle condition in each lane is also collected, and because two adjacent intersections are adjacent, each adjacent intersection has a vehicle flow to converge into the current intersection, and in order to avoid the congestion of the current intersection, the vehicles at the adjacent intersections are continuously converged, so that the time for allowing the vehicles to pass through each road at the current intersection can be adjusted.
And the signal lamp adjusting module 400 is used for generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on the road of each direction of the current intersection and executing the signal lamp adjusting scheme.
In the present computer device, the signal light adjusting module 400 generates a signal light adjusting scheme according to the traffic demand of the traffic stream and the number of vehicles on each road of the current intersection, and when adjusting, specifically, if a direction and B directions are two roads, the two roads are perpendicular to each other, a large number of vehicles will gather into the a direction road at the adjacent intersection, and there is a waiting vehicle on the current a direction road, the time for the vehicle to pass to the road is prolonged, that is, the time for the green light to light on the direction road is prolonged, for example, the time is prolonged from 50 seconds to 60 seconds, and for the B direction road, the time for the green light to light is appropriately shortened, that is, the time for the vehicle to pass to the road is further prolonged by shortening the time for the vehicle to pass to the road, and after the signal light adjusting scheme is generated, the signal light adjusting module is sent to the traffic light control device for the traffic light control device to execute.
As shown in fig. 6, as a preferred embodiment of the present invention, the picture recognition module 200 includes:
the road counting unit 201 is configured to perform road identification according to the intersection traffic picture, and determine the number of roads included in the intersection traffic picture.
In this module, the road statistics unit 201 performs road recognition according to the intersection traffic pictures, performs line recognition, and determines the positions of the lane lines, thereby dividing the lanes, and performing statistics on the number of roads included in each intersection traffic picture, specifically, it is also possible to recognize non-motor lanes.
And the vehicle counting unit 202 is used for identifying vehicles according to the intersection traffic pictures and counting the number of the vehicles waiting to pass on each road.
In this module, the vehicle counting unit 202 performs road recognition according to the intersection traffic picture, and when performing image recognition, performs line drawing processing on the image first, and converts the image into a line drawing, so as to determine the profile of the vehicle and the vehicle characteristics according to the lines, where the vehicle characteristics are the profile of the front windshield of the vehicle, the profile of the wheels, and the like, and thus determine the number of vehicles on each road.
The road positioning unit 203 is configured to obtain a position of the current intersection, and determine a passing direction of each road corresponding to the current intersection.
In this module, the road positioning unit 203 obtains the positioning of the current intersection, and when positioning, the positioning can be obtained according to the positioning device, and the inquiry can also be performed according to the number of the monitoring device, so as to determine the installation position of each monitoring device, that is, the position of the intersection.
As shown in fig. 7, as a preferred embodiment of the present invention, the requirement verification module 300 includes:
the map retrieving unit 301 is configured to retrieve a corresponding area map according to the positioning information of the current intersection.
In this module, the map retrieving unit 301 retrieves a corresponding area map according to the positioning information of the current intersection, and after the positioning is obtained, retrieves the map of the current position, so as to capture the map of the corresponding position from the global map, and obtain the area map, specifically, draw a circle with a preset radius by using the positioning center of the current intersection, so as to determine the area map.
The data obtaining unit 302 is configured to determine an intersection adjacent to the intersection according to the area map, and obtain corresponding traffic data.
In this module, the data obtaining unit 302 determines an intersection adjacent to the intersection according to the area map, identifies the lane distribution on each road in the map, thereby determining the position of a traffic light on each road, determines the adjacent intersection according to the traffic light, and obtains traffic data corresponding to the intersection, where the traffic data includes the number of waiting vehicles in each lane.
And the data calculation unit 303 is configured to determine, according to the traffic data, the number of vehicles approaching to the intersection at the adjacent intersection, so as to obtain a vehicle passing demand.
In this module, the data calculating unit 303 determines the number of vehicles approaching to the intersection at the adjacent intersection according to the traffic data, and a corresponding vehicle waiting to pass exists in the lane of each intersection, so that the number of vehicles approaching to the intersection can be determined according to the lane where the vehicle is located.
As shown in fig. 8, the signal lamp adjusting module 400, as a preferred embodiment of the present invention, includes:
a history data retrieving unit 401, configured to retrieve traffic history data of the current intersection, and determine each direction traffic speed of the current intersection according to the traffic history data.
In this module, a history data retrieving unit 401 retrieves traffic history data of a current intersection, counts the traffic speed in each traffic direction in the history data, and acquires weather data including temperature, date and weather of an area where the current intersection is located when counting, so that when retrieving the traffic speed, a traffic speed matched with the weather data is retrieved, and the current intersection can perform traffic speed in all directions.
And a traffic time calculating unit 402, configured to determine the traffic time on each road according to the traffic speed and the number of vehicles on each road.
A scheme generating unit 403, configured to generate a signal light adjustment scheme according to the traffic time on each road.
In the module, the passing time on each road is determined according to the passing speed and the number of vehicles on each road, when the passing time is calculated, the calculation is carried out on the basis of the sum of the number of vehicles waiting to pass through at the current intersection and the number of vehicles going to the current road, so that whether the lighting time of the current traffic light meets the requirement or not is judged, if the lighting time cannot meet the requirement, the adjustment is carried out according to the calculated passing time to generate a signal light adjusting scheme, and the signal light adjusting scheme is sent to traffic light control equipment at the current intersection for execution.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions;
carrying out picture identification on the intersection traffic picture, and counting the number of vehicles on roads in all directions;
acquiring traffic data of adjacent intersections, and determining traffic flow passing requirements of the current intersection according to the traffic data;
and generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on each road of the current intersection, and executing.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of:
collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions;
carrying out picture identification on the intersection traffic picture, and counting the number of vehicles on roads in all directions;
acquiring traffic data of adjacent intersections, and determining traffic flow passing requirements of the current intersection according to the traffic data;
and generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on the road of each direction of the current intersection, and executing.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (10)
1. A big data based traffic control method, the method comprising:
collecting intersection traffic pictures, wherein the intersection traffic pictures comprise images corresponding to roads in all directions;
carrying out picture identification on the intersection traffic picture, and counting the number of vehicles on roads in all directions;
acquiring traffic data of adjacent intersections, and determining traffic flow passing requirements of the current intersection according to the traffic data;
and generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on each road of the current intersection, and executing.
2. The traffic control method based on big data according to claim 1, wherein the step of performing picture recognition on the intersection traffic picture and counting the number of vehicles on each road specifically comprises:
carrying out road identification according to the intersection traffic picture, and determining the number of roads contained in the intersection traffic picture;
carrying out vehicle identification according to the intersection traffic picture, and counting the number of vehicles waiting to pass on each road;
and acquiring the positioning of the current intersection, and determining the passing direction of each road corresponding to the current intersection.
3. The traffic control method based on big data according to claim 1, wherein the step of obtaining traffic data of an adjacent intersection and determining the traffic flow passing demand of the current intersection according to the traffic data specifically comprises:
calling a corresponding area map according to the positioning information of the current intersection;
determining an intersection adjacent to the intersection according to the regional map, and acquiring corresponding traffic data;
and judging the quantity of the vehicles going to go to the intersection of the adjacent intersection according to the traffic data to obtain the vehicle passing demand.
4. The traffic control method based on big data according to claim 1, wherein the steps of generating a signal light adjustment scheme according to the traffic demand and the number of vehicles on each road of the current intersection and executing the signal light adjustment scheme specifically comprise:
calling the traffic history data of the current intersection, and determining the traffic speed of the current intersection in each direction according to the traffic history data;
determining the passing time on the roads in all directions according to the passing speed in all directions and the number of vehicles on the roads in all directions;
and generating a signal lamp adjusting scheme according to the passing time on each road.
5. The big-data based traffic control method according to claim 1, wherein the vehicles at the adjacent intersection can directly go to the current intersection.
6. The big-data-based traffic control method according to claim 1, wherein the intersection traffic picture is obtained from intersection monitoring.
7. A big-data based traffic control computer device, the computer device comprising:
the system comprises an image acquisition module, a data processing module and a data processing module, wherein the image acquisition module is used for acquiring intersection traffic pictures, and the intersection traffic pictures comprise images corresponding to roads in all directions;
the picture identification module is used for carrying out picture identification on the intersection traffic picture and counting the number of vehicles on roads in all directions;
the demand confirmation module is used for acquiring traffic data of adjacent intersections and determining traffic flow passing demands of the current intersection according to the traffic data;
and the signal lamp adjusting module is used for generating a signal lamp adjusting scheme according to the traffic flow passing demand and the number of vehicles on each road of the current intersection and executing the signal lamp adjusting scheme.
8. The big-data based traffic control computer device according to claim 7, wherein the picture recognition module comprises:
the road counting unit is used for identifying roads according to the intersection traffic pictures and determining the number of the roads contained in the intersection traffic pictures;
the vehicle counting unit is used for identifying vehicles according to the intersection traffic pictures and counting the number of the vehicles waiting to pass on each road;
and the road positioning unit is used for acquiring the positioning of the current intersection and determining the passing direction of each road corresponding to the current intersection.
9. The big-data based traffic control computer device of claim 7, wherein the demand confirmation module comprises:
the map calling unit is used for calling a corresponding regional map according to the positioning information of the current intersection;
the data acquisition unit is used for determining an intersection adjacent to the intersection according to the regional map and acquiring corresponding traffic data;
and the data calculation unit is used for judging the quantity of the vehicles going to go to the intersection from the adjacent intersection according to the traffic data to obtain the vehicle passing demand.
10. The big-data based traffic control computer apparatus of claim 1, wherein the signal light adjustment module comprises:
the historical data calling unit is used for calling the traffic historical data of the current intersection and determining the traffic speed of the current intersection in each direction according to the traffic historical data;
the traffic time calculation unit is used for determining the traffic time on each road according to the traffic speed and the number of vehicles on each road;
and the scheme generating unit is used for generating a signal lamp adjusting scheme according to the passing time on each road.
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Application publication date: 20221014 |