CN115346376A - Big data urban traffic volume statistical method, system, storage medium and electronic equipment - Google Patents
Big data urban traffic volume statistical method, system, storage medium and electronic equipment Download PDFInfo
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G—TRAFFIC CONTROL SYSTEMS
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Abstract
The application discloses a big data urban traffic volume statistical method, a system, a storage medium and electronic equipment, and belongs to the technical field of traffic data processing systems. The method comprises the steps of acquiring overall length data of vehicles staying at a road intersection in a red light stage and synchronizing the data to a data storage module; the method comprises the steps of (1) calling integral length data of vehicles staying at a red light stage at a road junction, and constructing a road junction congestion level evaluation index; constructing a traffic volume analysis model based on the road junction congestion level evaluation index; the traffic analysis model evaluates the congestion level of the road junction according to the data of the whole length of the vehicles staying at the red light stage, which is obtained in real time at the road junction; and extracting congestion level information of the high road junction, uploading the congestion level information to a server, and automatically distributing and issuing the congestion level information of the high road junction to the branch management terminals of the corresponding road junctions by the server. The technical problems that actual data of the number of the traffic lights in the original specific time period cannot be accurately counted and the length of the traffic jam waiting for the red light cannot be obtained are solved.
Description
Technical Field
The application relates to the technical field of traffic data processing systems, in particular to a big data urban traffic volume statistical method and system, a storage medium and electronic equipment.
Background
At present, the regular statistics of the traffic flow is one of important means for timely relieving the municipal traffic pressure. In the prior art, a fully-automatic traffic flow observation instrument is generally used, namely, a traffic flow meter or a traffic flow meter is arranged on a suspension of a traffic light, the traffic flow of a traffic road and a traffic gate is monitored in real time, the number of vehicles entering and exiting at different time periods is automatically counted, and the traffic condition is analyzed, so that reference is provided for traffic scheduling optimization.
However, the traffic flow is calculated by monitoring the number of passing vehicles in a preset time period, and for vehicles which are blocked due to waiting for a red light, the vehicles pass through the intersection in a concentrated manner after the green light, the actual data of the number of passing vehicles in the original specific time period are difficult to accurately count, so that the existing traffic scheduling optimization is low in efficiency, the length of the vehicles which are blocked due to waiting for the red light cannot be obtained, and the traffic condition of the intersection is difficult to further analyze.
Disclosure of Invention
Therefore, the application provides a big data urban traffic volume statistical method, a big data urban traffic volume statistical system, a storage medium and electronic equipment, and aims to solve the technical problems that when traffic flow is regularly counted in the prior art, actual data of the number of traffic vehicles in an original specific time period is difficult to accurately count when vehicles which are blocked due to red light waiting pass through an intersection concentratedly after the green light, the effect of optimizing the existing traffic dispatching is low, the length of the vehicles which are blocked due to red light waiting cannot be known, and the traffic condition of the intersection is difficult to further analyze.
In order to achieve the above object, according to a first aspect of the present application, there is provided a big data urban traffic volume statistical method, comprising the steps of:
s101: acquiring the overall length data of vehicles staying at the road junction in a red light stage;
s102: sending the whole length data of the vehicles staying at the red light stage of the road junction to the main control module and synchronizing the whole length data to the data storage module;
s103: calling the data of the whole length of the vehicles staying at the road junction in the red light stage in the data storage module to construct a road junction congestion level evaluation index;
s104: constructing a traffic volume analysis model based on the road junction congestion level evaluation index;
s105: the traffic volume analysis model evaluates the congestion level of the road junction according to the whole length data of the vehicles staying at the red light stage, which is obtained in real time at the specific road junction;
s106: and extracting congestion level information of the high road intersections, uploading the congestion level information to a server, and automatically distributing and issuing the congestion level information of the high road intersections to the branch management terminals of the corresponding road intersections by the server.
In one embodiment, the obtaining data of the overall length of the vehicle staying in the red light stage at the intersection specifically includes:
the light control data module arranged at a specific road junction is matched with the infrared monitoring module, so that the overall length data of the vehicles staying at the red light stage is acquired in real time.
In one embodiment, the sending the data of the overall length of the vehicle staying at the intersection in the red light stage to the main control module and synchronizing the data to the data storage module specifically includes:
and sending the whole length data of the vehicles staying at the red light stage at different time periods of a plurality of specific road junctions to the main control module, and synchronizing the whole length data to the data storage module.
In one embodiment, the invoking of the data storage module constructs an intersection congestion level assessment indicator based on overall length data of vehicles staying at the intersection in a red light stage, and specifically includes:
and the management end calls the data of the whole length of the vehicles staying in the red light stage in different time periods based on a plurality of specific road junctions in the data storage module through the main control module to construct road junction congestion level evaluation indexes.
In one embodiment, the constructing a traffic volume analysis model based on the intersection congestion level assessment index specifically includes:
and the management terminal further constructs a traffic analysis model based on the intersection congestion level evaluation index, and places the traffic analysis model in the data storage module.
In one embodiment, the traffic analysis model estimates the congestion level at a specific road junction according to the data of the overall length of the vehicle staying at the red light stage, which is obtained at the specific road junction in real time, and specifically includes:
the traffic analysis model evaluates the congestion level of the road junction according to the data of the whole length of the vehicle staying at the red light stage, which is obtained by the specific road junction in real time, and transmits the congestion level information of the road junction back to the management end through the main control module;
the extracted congestion level information of the high road junction is uploaded to a server, and the server automatically distributes the congestion level information of the high road junction correspondingly and issues the congestion level information to the branch management terminals of the corresponding road junctions, and the method specifically comprises the following steps:
the management terminal extracts high intersection congestion level information and uploads the high intersection congestion level information to the server through the main control module and the communication module, the server automatically distributes the high intersection congestion level information correspondingly and issues the high intersection congestion level information to the branch management terminals of the corresponding intersections, and the branch management terminals perform corresponding intersection scheduling optimization.
According to a second aspect of the application, a big data urban traffic volume statistical system is provided, comprising:
the data acquisition module is used for acquiring the overall length data of the vehicles staying at the road junction in the red light stage;
the data synchronization module is used for sending the whole length data of the vehicles staying at the road junction in the red light stage to the main control module and synchronizing the whole length data to the data storage module;
the index building module is used for calling the overall length data of the vehicles staying at the red light stage in the data storage module based on the road junction and building a road junction congestion level evaluation index;
the analysis model building module is used for building a traffic volume analysis model based on the road junction congestion level evaluation index;
the grade evaluation module is used for evaluating the congestion grade of the road junction by the traffic volume analysis model according to the whole length data of the vehicles staying at the red light stage, which is acquired by the specific road junction in real time;
and the information extraction and allocation module is used for extracting the congestion level information of the high road junction and uploading the congestion level information to the server, and the server automatically distributes and issues the congestion level information of the high road junction correspondingly to the branch management terminals of the corresponding road junction.
In one embodiment, the data acquisition module comprises a lamp control data module and an infrared monitoring module which are arranged at the road junction;
the lamp control data module is arranged on one side of the road junction;
the road junction is provided with a plurality of lanes, and a plurality of groups of infrared monitoring modules are uniformly arranged on the middle line of every two adjacent lanes correspondingly;
each group of infrared monitoring modules comprises two groups of monitoring probes which are arranged oppositely, the two groups of monitoring probes respectively correspond to two adjacent lanes on two sides of the infrared monitoring modules one by one, the detection angle range of the monitoring probes is 90-120 degrees, the two adjacent groups of infrared monitoring modules of each intermediate line are overlapped with each other in the detection area corresponding to the lanes, and the infrared monitoring modules positioned at two ends in the three adjacent groups of intermediate lines are mutually jointed in the detection area corresponding to the lanes.
According to a third aspect of the present application, there is provided an electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method.
According to a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of the method.
The beneficial effect of this application lies in: the traffic jam length of different road junctions in the red light stage can be monitored in real time, more accurate traffic flow data of a specific road junction can be obtained, the traffic condition of the junction can be analyzed in a targeted manner, the traffic scheduling optimization efficiency can be improved, and the functional practicability can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and the description of the exemplary embodiments of the present application are provided for explaining the present application and do not constitute an undue limitation on the present application. In the drawings:
fig. 1 is a schematic flow chart of a big data urban traffic volume statistical method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a big data urban traffic volume statistics system according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a data acquisition module in a big data urban traffic volume statistical system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
the reference numbers in the figures are as follows:
a road intersection 1; a lane 11; a lamp control data module 2; an infrared monitoring module 3; a main control module 4; and a data storage module 5.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as the case may be.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as the case may be.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Currently, traffic flow refers to the number of traffic entities that pass through a road at a certain location, section or lane over a period of time. With the development of traffic infrastructure construction and intelligent transportation systems, traffic planning and traffic route guidance have become hot spots for research in the field of municipal transportation. The regular statistics of the traffic flow is one of important means for timely relieving the municipal traffic pressure, and the accurate traffic flow prediction data is the premise and the key for realizing the traffic flow prediction data for traffic planning and traffic route guidance.
The traditional traffic flow statistical devices such as video analysis, traffic dispatching stations, overspeed enforcement systems, high-speed toll collection systems and the like are generally used as full-automatic traffic flow observation instruments, namely, a traffic flow meter or a traffic flow meter is arranged on a suspension of a traffic light, the traffic flow of a traffic road and a traffic gate is monitored in real time, the quantity of vehicles entering and exiting at different time periods is automatically counted, and the traffic condition is analyzed, so that a reference is provided for traffic dispatching optimization.
But it is only used to monitor the number of passing vehicles within a predetermined period of time to calculate the traffic flow. However, the physical states such as the length of a road section, the number of lanes and the like are different, so that the process of acquiring accurate data by the acquisition scheme of the acquisition method is excessively complex, particularly for vehicles which are blocked by waiting for a red light, the vehicles intensively pass through the intersection after the red light, the actual data of the number of the vehicles which pass through the intersection within the original specific time duration is difficult to accurately count, the effect of the existing traffic scheduling optimization is low, the length of the vehicles which are blocked by waiting for the red light cannot be known, and the traffic condition of the intersection is difficult to further analyze. Therefore, the above-mentioned practice has certain drawbacks.
Therefore, the concept of the application is that aiming at the problem encountered when intersection traffic conditions are further analyzed, a big-data urban traffic volume statistical method is provided, the method is used for monitoring the vehicle congestion length of different intersections 1 in the red light stage in real time, so that more accurate traffic flow data of specific intersections 1 can be obtained, the intersection traffic conditions can be analyzed in a targeted manner, and the traffic scheduling optimization efficiency can be improved.
Specifically, referring to fig. 1, fig. 1 is a schematic flow chart of a big data urban traffic volume statistical method provided in this embodiment, where the method specifically includes the following steps:
s101: the method comprises the steps that through the cooperation of a lamp control data module 2 and an infrared monitoring module 3 which are arranged at a specific road junction 1, the whole length data of vehicles staying at a red light stage are obtained in real time;
s102: sending the data of the whole lengths of the vehicles staying at the red light stage at different time periods at a plurality of specific road junctions 1 to a main control module 4, and synchronizing the data into a data storage module 5;
s103: the management end calls the data of the whole length of the vehicles staying at the red light stage in different time periods at a plurality of specific road junctions 1 in the data storage module 5 through the main control module 4 to construct a congestion level evaluation index of the road junctions 1;
s104: the management terminal further constructs a traffic analysis model based on the congestion level evaluation index of the road intersection 1, and the traffic analysis model is arranged in the data storage module 5;
s105: the traffic volume analysis model evaluates the congestion level of the road junction 1 according to the data of the whole length of the vehicle staying at the red light stage acquired by the specific road junction 1 in real time, and transmits the congestion level information of the road junction 1 back to the management end through the main control module 4;
s106: the management terminal extracts the congestion level information of the high road intersection 1 and uploads the congestion level information to the server through the main control module 4 and the communication module, the server automatically distributes the congestion level information of the high road intersection 1 correspondingly and issues the congestion level information to the branch management terminals of the corresponding road intersection 1, and the branch management terminals perform scheduling optimization on the corresponding road intersection 1.
In an embodiment, the present application further provides a big data urban traffic volume statistics system, please refer to fig. 2, where fig. 2 is a schematic diagram of a principle of the big data urban traffic volume statistics system provided in this embodiment, and the system includes:
the data acquisition module is used for acquiring the overall length data of the vehicles staying at the road junction 11 in the red light stage;
the data synchronization module is used for sending the whole length data of the vehicles staying at the red light stage at the road junction 11 to the main control module 4 and synchronizing the whole length data to the data storage module 5;
the index construction module is used for calling the data of the whole length of the vehicles staying at the red light stage at the road junction 1 in the data storage module 5 and constructing the congestion level evaluation index of the road junction 1;
the analysis model building module is used for building a traffic volume analysis model based on the road junction 1 congestion level evaluation index;
the grade evaluation module is used for evaluating the congestion grade of the road junction 1 by the traffic volume analysis model according to the whole length data of the vehicles staying at the red light stage, which is acquired by the specific road junction 1 in real time;
and the information extraction and allocation module is used for extracting the congestion level information of the high road junction 1 and uploading the congestion level information to the server, and the server automatically distributes the congestion level information of the high road junction 1 correspondingly and issues the congestion level information to the branch management terminals of the corresponding road junction 1.
Specifically, please refer to fig. 3, fig. 3 is a schematic structural diagram of a data acquisition module in the big data urban traffic volume statistical system provided in this embodiment, where the data acquisition module includes a lamp control data module and an infrared monitoring module, which are disposed at a road junction; the lamp control data module is arranged on one side of the road junction.
The road junction is provided with a plurality of lanes, and a plurality of groups of infrared monitoring modules are correspondingly and uniformly arranged on the middle lines of every two adjacent lanes.
As a preferred scheme of this embodiment, each group of the infrared monitoring modules includes two groups of monitoring probes arranged in opposite directions, the two groups of the monitoring probes respectively correspond to two adjacent lanes on two sides of the infrared monitoring module one by one, and a detection angle range of the monitoring probes is 90-120 °.
Further preferably, the two adjacent groups of infrared monitoring modules of each middle line are overlapped with each other in the detection areas corresponding to the lanes, and the infrared monitoring modules at two ends of the three adjacent groups of middle lines are mutually jointed in the detection areas corresponding to the lanes.
In an implementation manner, the present application further provides an electronic device, please refer to fig. 4, where fig. 4 is a schematic structural diagram of the electronic device provided in this embodiment, and the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring the data of the whole length of the vehicles staying at the road junction in the red light stage; sending the whole length data of the vehicles staying at the red light stage of the road junction to the main control module and synchronizing the whole length data to the data storage module; calling the data of the whole length of the vehicles staying at the road junction in the red light stage in the data storage module to construct a road junction congestion level evaluation index; constructing a traffic volume analysis model based on the road junction congestion level evaluation index; the traffic volume analysis model evaluates the congestion level of the road junction according to the whole length data of the vehicles staying at the red light stage, which is obtained in real time at the specific road junction; and extracting congestion level information of the high road intersections, uploading the congestion level information to a server, and automatically distributing and issuing the congestion level information of the high road intersections to the branch management terminals of the corresponding road intersections by the server.
The big data city traffic volume statistical method disclosed in the embodiment of fig. 1 of the present application can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to perform the big data city traffic volume statistics method in the embodiment shown in fig. 1, and are specifically configured to perform:
acquiring the overall length data of vehicles staying at the road junction in a red light stage; sending the whole length data of the vehicles staying at the red light stage of the road junction to the main control module and synchronizing the whole length data to the data storage module; the method comprises the steps of calling integral length data of vehicles staying at a red light stage at a road junction in a data storage module, and constructing a road junction congestion level evaluation index; constructing a traffic volume analysis model based on the road junction congestion level evaluation index; the traffic analysis model evaluates the congestion level of the road junction according to the data of the whole length of the vehicles staying at the red light stage, which is acquired by the specific road junction in real time; and extracting the congestion level information of the high road junctions and uploading the congestion level information to the server, and automatically distributing and issuing the congestion level information of the high road junctions to the branch management terminals of the corresponding road junctions by the server.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. A big data urban traffic volume statistical method is characterized by comprising the following steps:
s101: acquiring the data of the whole length of the vehicles staying at the road junction in the red light stage;
s102: sending the whole length data of the vehicles staying at the red light stage of the road junction to the main control module and synchronizing the whole length data to the data storage module;
s103: the method comprises the steps of calling integral length data of vehicles staying at a red light stage at a road junction in a data storage module, and constructing a road junction congestion level evaluation index;
s104: constructing a traffic volume analysis model based on the road junction congestion level evaluation index;
s105: the traffic analysis model evaluates the congestion level of the road junction according to the data of the whole length of the vehicles staying at the red light stage, which is acquired by the specific road junction in real time;
s106: and extracting congestion level information of the high road intersections, uploading the congestion level information to a server, and automatically distributing and issuing the congestion level information of the high road intersections to the branch management terminals of the corresponding road intersections by the server.
2. The big data urban traffic statistical method according to claim 1, wherein the obtaining of the overall length data of the vehicles staying at the intersection in the red light stage specifically comprises:
the light control data module arranged at a specific road junction is matched with the infrared monitoring module, so that the data of the whole length of the vehicle staying at the red light stage can be acquired in real time.
3. The big data urban traffic volume statistical method according to claim 2, wherein the sending of the data of the overall length of the vehicles staying at the road junction at the red light stage to the main control module and the synchronization to the data storage module specifically comprises:
and sending the data of the whole length of the vehicles staying at the red light stage at different time periods at a plurality of specific road junctions to the main control module, and synchronizing the data into the data storage module.
4. The big-data urban traffic volume statistical method according to claim 3, wherein the invoking data storage module constructs an intersection congestion level evaluation index based on overall length data of vehicles staying at the intersection in a red light stage, and specifically comprises:
and the management end calls the data of the whole length of the vehicles staying in the red light stage in different time periods based on a plurality of specific road junctions in the data storage module through the main control module to construct road junction congestion level evaluation indexes.
5. The big data urban traffic volume statistical method according to claim 4, wherein the traffic volume analysis model is constructed based on the road junction congestion level evaluation index, and specifically comprises:
and the management terminal further constructs a traffic analysis model based on the intersection congestion level evaluation index, and places the traffic analysis model in the data storage module.
6. The big-data urban traffic statistical method according to claim 5, wherein the traffic analysis model estimates the road intersection congestion level according to the overall length data of vehicles staying at the red light stage acquired in real time at a specific road intersection, and specifically comprises:
the traffic volume analysis model evaluates the congestion level of the road junction according to the whole length data of the vehicles staying at the red light stage acquired by the specific road junction in real time, and transmits the congestion level information of the road junction back to the management end through the main control module;
the extracted congestion level information of the high road junction is uploaded to a server, and the server automatically distributes the congestion level information of the high road junction correspondingly and issues the congestion level information to the branch management terminals of the corresponding road junctions, and the method specifically comprises the following steps:
the management terminal extracts high intersection congestion level information and uploads the high intersection congestion level information to the server through the main control module and the communication module, the server automatically distributes the high intersection congestion level information correspondingly and issues the high intersection congestion level information to the branch management terminals of the corresponding intersections, and the branch management terminals perform corresponding intersection scheduling optimization.
7. A big data urban traffic volume statistical system, characterized by comprising:
the data acquisition module is used for acquiring the overall length data of the vehicles staying at the road junction in the red light stage;
the data synchronization module is used for sending the whole length data of the vehicles staying at the red light stage at the road junction to the main control module and synchronizing the whole length data to the data storage module;
the index building module is used for calling the overall length data of the vehicles staying at the red light stage in the data storage module based on the road junction and building a road junction congestion level evaluation index;
the analysis model building module is used for building a traffic volume analysis model based on the road junction congestion level evaluation index;
the grade evaluation module is used for evaluating the congestion grade of the road junction by the traffic volume analysis model according to the whole length data of the vehicles staying at the red light stage, which is acquired by the specific road junction in real time;
and the information extraction and allocation module is used for extracting the high road junction congestion level information and uploading the high road junction congestion level information to the server, and the server automatically distributes the high road junction congestion level information correspondingly and issues the high road junction congestion level information to the branch management terminals of the corresponding road junctions.
8. The big data urban traffic volume statistical system according to claim 7, wherein the data acquisition module comprises a light control data module and an infrared monitoring module arranged at a road junction;
the lamp control data module is arranged on one side of the road junction;
the road junction is provided with a plurality of lanes, and a plurality of groups of infrared monitoring modules are uniformly arranged on the middle line of every two adjacent lanes correspondingly;
each group of infrared monitoring modules comprises two groups of monitoring probes which are arranged oppositely, the two groups of monitoring probes respectively correspond to two adjacent lanes on two sides of the infrared monitoring modules one by one, the detection angle range of the monitoring probes is 90-120 degrees, the two adjacent groups of infrared monitoring modules of each intermediate line are overlapped with each other in the detection area corresponding to the lanes, and the infrared monitoring modules positioned at two ends in the three adjacent groups of intermediate lines are mutually jointed in the detection area corresponding to the lanes.
9. An electronic device, comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1 to 6.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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