CN116311889A - High-ranking road section identification method, device, equipment and medium - Google Patents
High-ranking road section identification method, device, equipment and medium Download PDFInfo
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- CN116311889A CN116311889A CN202211679989.4A CN202211679989A CN116311889A CN 116311889 A CN116311889 A CN 116311889A CN 202211679989 A CN202211679989 A CN 202211679989A CN 116311889 A CN116311889 A CN 116311889A
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- G08G1/00—Traffic control systems for road vehicles
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- 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
Abstract
The disclosure provides a high-ranking road section identification method, which comprises the following steps: acquiring real-time road network data and vehicle data, estimating the traffic flow of each road section in the road network according to the road network data and the vehicle data, calculating the total discharge of the road types of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section, accumulating the total discharge of all the road types to obtain a whole road network discharge list, and identifying the high-discharge road sections with the discharge higher than a preset value in the whole road network discharge list. The disclosure also provides a device, an electronic device and a storage medium corresponding to the method. The method is based on real-time emission monitoring data of vehicles, the road network emission list is high in calculation accuracy, the vehicle emission is subdivided into hour road sections, the vehicle emission time and spatial resolution are high, the time change rule of the high-emission road sections can be dynamically obtained, and the correct recognition rate of the suspected high-emission road sections is high.
Description
Technical Field
The disclosure relates to the technical field, and in particular relates to a high-ranking road section identification method, a device, electronic equipment and a medium.
Background
With the rapid and continuous increase of the maintenance of urban motor vehicles, motor vehicle emissions have become an important source of urban air pollution. The tail gas of the motor vehicle can cause great harm to the health of human bodies, but the wide mobility of the motor vehicle also brings great challenges to the emission supervision of the motor vehicle, and how to efficiently supervise the motor vehicle becomes a difficult problem for the environmental protection department.
Current in-use motor vehicle supervision means include: firstly, irregular road spot inspection tour inspection, wherein due to limited manpower and road spot inspection range, spot inspection lacks vehicle pertinence; secondly, according to the past law enforcement experience, road congestion sections or sections with more large vehicles are selected for vehicle detection, but the problem of uncertainty of law enforcement objects still exists because no necessarily-linked vehicle emission and road congestion degree exists; thirdly, the high-emission vehicles are rapidly screened by the motor vehicle tail gas remote sensing monitoring technology, but the method only screens vehicles passing through the road section and cannot be used for other road vehicles.
The high-row road section is a distribution road section for identifying the concentrated presence or high incidence of suspected high-row vehicles. The road section range of the high-row vehicles is narrowed, so that law enforcement can be tracked timely and efficiently according to the road superrow clues. However, the accurate identification of the high-emission road section depends on the refinement degree of the road network emission list, at present, the method for calculating the emission factor by adopting the emission model is most adopted by the mobile source emission list, the high-emission road section is easy to be misjudged or missed to be judged due to the larger uncertainty of the list, in addition, the high-emission road section is difficult to be accurately identified and dynamically tracked due to the fact that the total emission amount of the area scale is mainly used as the list from the time dimension and the space dimension.
Disclosure of Invention
In view of the above, the present invention provides a method for identifying a high-ranking road segment, so as to at least solve some of the above technical problems.
One aspect of the present disclosure provides a high-ranking road segment identification method, including: acquiring real-time road network data and vehicle data; estimating the traffic flow of each road section in the road network according to the road network data and the vehicle data; calculating the total discharge of the road types of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section; accumulating the total discharge of all road types to obtain a whole road network discharge list; and identifying a high-emission road section with the emission higher than a preset value in the whole-network emission list.
Optionally, the road network data at least comprises a road longitude and latitude, a road type, a road length and a vehicle flow, and the vehicle data at least comprises a vehicle speed, a vehicle type, vehicle longitude and latitude information and a vehicle hour emission factor.
Optionally, the estimating the traffic flow of each road section in the road network according to the road network data and the vehicle data includes: dividing each road in the road network into a plurality of road sections; acquiring a quantitative relation between the traffic flow and the speed of the vehicle on each road section; and calculating the traffic flow on each road section according to the average hour speed on each road section based on the quantitative relation.
Optionally, the method further comprises: and predicting the traffic flow of the non-monitored road section of the same road type closest to the monitored road according to the traffic flow of the road section of the monitored road in the road network.
Optionally, the calculating the total discharge amount of the road type to which each road section belongs based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section includes: acquiring the vehicle hour emission factors of the vehicles of the same type on each road section, and calculating the average emission factors of the vehicle types; calibrating the average emission factor based on an emission factor speed calibration coefficient to obtain a comprehensive emission factor of each vehicle type on each road section; calculating to obtain the total discharge of the road section based on the comprehensive discharge factor, the traffic flow of the corresponding type of vehicles on the road section and the length of the road section; and calculating the sum of the total discharge of the road sections of the same road type, and calculating the total discharge of the road types of the road sections.
Optionally, the formula for calculating the total discharge amount of the road type to which each of the road segments belongs includes:
EFimjkh＝EFimjk*a(imjkvh)；
Eimjkh＝EFimjkh*Limj*Q(imjkh)；
Ei，h＝m＝1nj＝1nk＝1nEimjkh；
wherein E (i, h) represents the total amount of discharge at h hours on all roads of the ith type of road, E (imjkh) represents the amount of discharge at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/h, EF (imjkh) represents the integrated discharge factor at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, EF (imjk) represents the average discharge factor of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, a (imjkh) represents the discharge factor speed calibration factor of the average speed v at h hours in the jth road segment on the mth road of the ith type of road, L (imj) represents the length of the jth road segment on the mth road type of road, and Q (imjh) represents the flow rate of the kth road type of vehicle type in the jth road segment on the mth road type of the ith road type of the mth road segment.
Optionally, the method further comprises: and counting the occurrence frequency of the high-ranking road sections within a preset time to further identify and lock the high-ranking road sections.
Another aspect of the present disclosure provides an apparatus comprising: the real-time data acquisition module is used for acquiring real-time road network data and vehicle data; the vehicle flow estimation module is used for estimating the vehicle flow of each road section in the road network according to the road network data and the vehicle data; the emission amount calculating module is used for calculating the total emission amount of the road type of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section; the list acquisition module is used for accumulating the total discharge of all road types to obtain a whole road network discharge list; and the high-emission road section identification module is used for identifying the high-emission road section with the emission higher than a preset value in the whole-network emission list.
Another aspect of the present disclosure provides an electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method when executing the computer program.
Another aspect of the present disclosure provides a computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method.
The above at least one technical scheme adopted in the embodiment of the disclosure can achieve the following beneficial effects:
1. the road network emission list calculation accuracy is high based on the real-time emission monitoring data of the vehicle;
2. the vehicle emission is subdivided into an hour section, the vehicle emission time and the spatial resolution are high, and the time change rule of the high-emission section can be dynamically obtained;
3. based on the high-precision road section level emission characteristics, the highest road section ranking of the road network is combined, the correct recognition rate of the suspected high-ranking road sections is high, and the misjudgment or missed judgment rate of the high-ranking road sections is reduced to the greatest extent.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically illustrates a flowchart of a method for identifying a high-ranking road segment according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a block diagram of an apparatus provided by an embodiment of the present disclosure;
fig. 3 schematically illustrates a block diagram of an electronic device provided in an embodiment of the disclosure;
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Some of the block diagrams and/or flowchart illustrations are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, 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, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart.
Thus, the techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). Additionally, the techniques of this disclosure may take the form of a computer program product on a computer-readable medium having instructions stored thereon, the computer program product being usable by or in connection with an instruction execution system. In the context of this disclosure, a computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the instructions. For example, a computer-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the computer readable medium include: magnetic storage devices such as magnetic tape or hard disk (HDD); optical storage devices such as compact discs (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or a wired/wireless communication link.
As shown in fig. 1, the present disclosure provides a high-ranking road segment identification method, which performs temporal and spatial emission characteristic analysis on road segments in a road network based on a high-spatial-temporal resolution road network emission list subdivided into road segment levels and identifies the high-ranking road segments, including operations S110 to S150.
S110, acquiring real-time road network data and vehicle data.
According to an embodiment of the present disclosure, the road network data includes at least a road longitude and latitude, a road type, a road length, and a vehicle flow, and the vehicle data includes at least a vehicle speed, a vehicle type, vehicle longitude and latitude information, and a vehicle hour emission factor. The road network data are divided into road types with more than two levels such as expressways, circular lines, main roads, secondary main roads and the like and road data of each road type, and the roads can be regarded as line source emission.
The vehicle-mounted OBD remote on-line monitoring equipment can acquire vehicle monitoring data in real time, wherein the vehicle monitoring data comprises a concentration emission value per second, a vehicle speed, a vehicle type, vehicle longitude and latitude information and the like, the concentration value per second is converted into a vehicle hour emission factor after data cleaning treatment, the vehicle speed is converted into an hour average vehicle speed, and the quantitative relation between the vehicle speed and the emission factor is acquired according to correlation fitting analysis of the average vehicle speed and the emission factor, so that the emission factor is calibrated in real time.
S120, estimating the traffic flow of each road section in the road network according to the road network data and the vehicle data.
Specifically, operation S120 includes operations S121 to S123.
S121, dividing each road in the road network into a plurality of road segments.
Even if the road is the same, the traffic flow of different road sections and other activity level intensity are different, such as an intersection, a speed-limiting road section or a road section merging inlet and outlet, and in order to improve the discharge accuracy of the road section, it is necessary to refine the vehicle discharge to the road section level.
According to the embodiment of the disclosure, road network data of each road type are segmented at equal intervals according to the longitude and latitude positions of the roads, and then the running track of the vehicle is matched with the road segments, so that the data matching of the road network, the road segments and the vehicles is completed.
Alternatively, a road segment scale may be selected according to different road segment fineness, for example, the road segment is divided into 100m, and road segment segmentation is completed, where the road segment includes road type and road segment length information.
S122, obtaining the quantitative relation between the traffic flow and the vehicle speed on each road section.
In the embodiment of the disclosure, under the influence of an intersection, a traffic signal lamp and the like, the traffic flow on different road sections on the same road is different, the traffic flow on the road sections is thinned to be helpful for improving the resolution of a road network emission list, a road traffic flow density model is constructed by carrying out correlation fitting analysis on the road speed and the traffic flow, a quantitative relation between the traffic flow and the vehicle speed is obtained, and then the real-time hour traffic flow on each road type road section is calculated according to the hour average vehicle speed of each road type road section.
And S123, calculating the traffic flow on each road section according to the average hour speed on each road section based on the quantitative relation.
The method is aimed at the road which is monitored and obtained with data in the road network, and for the road which is not monitored, the method further comprises:
and predicting the traffic flow of the non-monitored road sections of the same road type closest to the monitored road according to the traffic flow of the monitored road sections in the road network.
And estimating the road traffic flow according to the real-time vehicle speed of the monitored road section in the road network, and simultaneously estimating and predicting the traffic flow and the vehicle speed of the non-monitored road section, so as to construct the real-time traffic flow data of the urban whole road network.
S130, calculating the total discharge amount of the road type of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section.
According to the embodiment of the disclosure, as the vehicle emission factors are related to the vehicle speed, the emission factors of the same type of vehicles are averaged and the vehicle speed is calibrated to obtain the comprehensive emission factors of the same type of vehicles, the emission amounts of the same type of vehicles on the road section can be calculated by combining the vehicle flow and the road section length of the same type of vehicles on the road section, and then the total emission amounts of all types of vehicles on the road section are accumulated to obtain the total emission amount of the road section
Specifically, operation S130 includes operations S131 to S134.
S131, acquiring the vehicle hour emission factors of the vehicles of the same type on each road section, and calculating the average emission factors of the vehicle types.
S132, calibrating the average emission factor based on the emission factor speed calibration coefficient to obtain the comprehensive emission factor of each vehicle type on each road section.
S133, calculating the total discharge of the road section based on the comprehensive discharge factor, the traffic flow of the corresponding type of vehicles on the road section and the length of the road section.
S134, calculating the sum of total discharge amounts of all road sections of the same road type, and calculating the total discharge amount of the road type of each road section.
According to the above steps, the formula for calculating the total discharge amount of the road type to which each road section belongs includes:
EFimjkh＝EFimjk*a(imjkVh)；
Eimjkh＝EFimjkh*Limj*Q(imjkh)；
Ei，h＝m＝1nj＝1nk＝1nEimjkh；
wherein E (i, h) represents the total amount of discharge at h hours on all roads of the ith type of road, E (imjkh) represents the amount of discharge at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/h, EF (imjkh) represents the integrated discharge factor at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, EF (imjk) represents the average discharge factor of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, a (imjkh) represents the discharge factor speed calibration factor of the average speed v at h hours in the jth road segment on the mth road of the ith type of road, L (imj) represents the length of the jth road segment on the mth road type of road, and Q (imjh) represents the flow rate of the kth road type of vehicle type in the jth road segment on the mth road type of the ith road type of the mth road segment.
And S140, accumulating the total discharge amount of all road types to obtain a whole road network discharge list.
And S150, identifying a high-emission road section with the emission higher than a preset value in the whole-network emission list.
According to the embodiment of the disclosure, according to the refined total road network emission list time and space emission characteristics, a preset proportion of road segments with highest emission, such as 10% of road segments with highest emission, are automatically selected first, and then the preselected suspected high-emission road segments are ranked in descending order of emission severity, so that typical high-emission road segments are confirmed.
Further, the method may further include operation S160.
And S160, counting the occurrence frequency of the high-ranking road sections within a preset time to further identify and lock the high-ranking road sections.
According to embodiments of the present disclosure, when certain road segment emissions occur significantly higher than surrounding road emissions levels or severe emissions change over a certain period of time, suspected high-emission road segments, including road segment latitude and longitude locations and road segment emissions levels, are automatically identified from refined road network emissions.
The high-emission road section identification method provided by the disclosure is high in calculation accuracy of the road network emission list based on real-time emission monitoring data of vehicles; the vehicle emission is subdivided into an hour section, the vehicle emission time and the spatial resolution are high, and the time change rule of the high-emission section can be dynamically obtained; based on the high-precision road section level emission characteristics, the highest road section ranking of the road network is combined, the correct recognition rate of suspected high-ranking road sections is high, and the misjudgment or missed judgment rate of the high-ranking road sections is reduced to the greatest extent
As shown in fig. 2, another aspect of the present disclosure provides a high-ranking road segment identifying apparatus 200, including: the system comprises a real-time data acquisition module 210, a vehicle flow estimation module 220, an emission calculation module 230, a list acquisition module 240 and a high-emission road section identification module 250.
The real-time data acquisition module 210 is configured to acquire real-time road network data and vehicle data.
The traffic flow estimating module 220 is configured to estimate traffic flow of each road section in the road network according to the road network data and the vehicle data.
The emission amount calculation module 230 is configured to calculate a total emission amount of a road type to which each road segment belongs based on a vehicle flow amount of each type of vehicle on each road segment and a length of the corresponding road segment.
The list obtaining module 240 is configured to accumulate the total discharge amounts of all road types to obtain a full road network discharge list.
The high-emission road section identification module 250 is configured to identify a high-emission road section with an emission higher than a preset value in the whole network emission list.
The high-row road segment identification device 200 provided in the present disclosure has the same technical features and technical effects as the high-row road segment identification method shown in fig. 1, and will not be described herein.
It is understood that the real-time data acquisition module 210, the traffic flow estimation module 220, the emission calculation module 230, the manifest acquisition module 240, the high-ranking section identification module 250 may be incorporated in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the invention, at least one of the real-time data acquisition module 210, the traffic estimation module 220, the emissions calculation module 230, the inventory acquisition module 240, the high-speed road segment identification module 250 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or any other reasonable way in which circuitry is integrated or packaged, or as hardware or firmware, or as a suitable combination of software, hardware, and firmware implementations. Alternatively, at least one of the real-time data acquisition module 210, the traffic flow estimation module 220, the emission amount calculation module 230, the inventory acquisition module 240, and the high-road section identification module 250 may be at least partially implemented as a computer program module, which may perform the functions of the corresponding module when the program is run by a computer.
Fig. 3 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
As shown in fig. 3, the electronic device described in the present embodiment includes: the electronic device 300 includes a processor 310, a computer-readable storage medium 320. The electronic device 300 may perform the method described above with reference to fig. 1 to enable detection of a particular operation.
In particular, processor 310 may include, for example, a general purpose microprocessor, an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 310 may also include on-board memory for caching purposes. Processor 310 may be a single processing unit or a plurality of processing units for performing different actions in accordance with the method flow described with reference to fig. 1 in accordance with an embodiment of the present disclosure.
The computer-readable storage medium 320 may be, for example, any medium that can contain, store, communicate, propagate, or transport the instructions. For example, a readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the readable storage medium include: magnetic storage devices such as magnetic tape or hard disk (HDD); optical storage devices such as compact discs (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or a wired/wireless communication link.
The computer-readable storage medium 320 may include a computer program 321, which computer program 321 may include code/computer-executable instructions that, when executed by the processor 310, cause the processor 310 to perform the method flow as described above in connection with fig. 1 and any variations thereof.
The computer program 321 may be configured with computer program code comprising, for example, computer program modules. For example, in an example embodiment, code in the computer program 321 may include one or more program modules, including 321A, 321B, for example. It should be noted that the division and number of modules is not fixed, and that a person skilled in the art may use suitable program modules or combinations of program modules according to the actual situation, which when executed by the processor 310, enable the processor 310 to perform the method flow as described above in connection with fig. 1 and any variations thereof.
At least one of the embodiments according to the invention may be implemented as a computer program module as described with reference to fig. 3, which when executed by the processor 310 may implement the respective operations described above.
The present disclosure also provides a computer-readable medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer readable medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
While the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents. The scope of the disclosure should, therefore, not be limited to the above-described embodiments, but should be determined not only by the following claims, but also by the equivalents of the following claims.
Claims (10)
1. A method for identifying a high-row road segment, comprising:
acquiring real-time road network data and vehicle data;
estimating the traffic flow of each road section in the road network according to the road network data and the vehicle data;
calculating the total discharge of the road types of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section;
accumulating the total discharge of all road types to obtain a whole road network discharge list;
and identifying a high-emission road section with the emission higher than a preset value in the whole-network emission list.
2. The method of claim 1, wherein the road network data comprises at least a road longitude and latitude, a road type, a road length, and a traffic flow, and the vehicle data comprises at least a vehicle speed, a vehicle type, vehicle longitude and latitude information, and a vehicle hour emission factor.
3. The method of claim 1, wherein estimating traffic flow for each road segment in the road network based on the road network data and the vehicle data comprises:
dividing each road in the road network into a plurality of road sections;
acquiring a quantitative relation between the traffic flow and the speed of the vehicle on each road section;
and calculating the traffic flow on each road section according to the average hour speed on each road section based on the quantitative relation.
4. A method according to claim 3, characterized in that the method further comprises:
and predicting the traffic flow of the non-monitored road section of the same road type closest to the monitored road according to the traffic flow of the road section of the monitored road in the road network.
5. The method of claim 1, wherein calculating the total discharge of the road type to which each of the road segments belongs based on the traffic flow of each of the types of vehicles on each of the road segments and the length of the corresponding road segment comprises:
acquiring the vehicle hour emission factors of the vehicles of the same type on each road section, and calculating the average emission factors of the vehicle types;
calibrating the average emission factor based on an emission factor speed calibration coefficient to obtain a comprehensive emission factor of each vehicle type on each road section;
calculating to obtain the total discharge of the road section based on the comprehensive discharge factor, the traffic flow of the corresponding type of vehicles on the road section and the length of the road section;
and calculating the sum of the total discharge of the road sections of the same road type, and calculating the total discharge of the road types of the road sections.
6. The method of claim 5, wherein the formula for calculating the total discharge of the road types to which each of the segments belongs comprises:
EFimjkh＝EFimjk*a(imjkvh)；
Eimjkh＝EFimjkh*Limj*Q(imjkh)；
Ei，h＝m＝1nj＝1nk＝1nEimjkh；
wherein E (i, h) represents the total amount of discharge at h hours on all roads of the ith type of road, E (imjkh) represents the amount of discharge at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/h, EF (imjkh) represents the integrated discharge factor at h hours of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, EF (imjk) represents the average discharge factor of the kth vehicle type in the jth road segment on the mth road of the ith type of road, in g/km, a (imjkh) represents the discharge factor speed calibration factor of the average speed v at h hours in the jth road segment on the mth road of the ith type of road, L (imj) represents the length of the jth road segment on the mth road type of road, and Q (imjh) represents the flow rate of the kth road type of vehicle type in the jth road segment on the mth road type of the ith road type of the mth road segment.
7. The method according to claim 1, wherein the method further comprises:
and counting the occurrence frequency of the high-ranking road sections within a preset time to further identify and lock the high-ranking road sections.
8. A high-traffic road segment recognition apparatus, characterized by comprising:
the real-time data acquisition module is used for acquiring real-time road network data and vehicle data;
the vehicle flow estimation module is used for estimating the vehicle flow of each road section in the road network according to the road network data and the vehicle data;
the emission amount calculating module is used for calculating the total emission amount of the road type of each road section based on the traffic flow of each type of vehicle on each road section and the length of the corresponding road section;
the list acquisition module is used for accumulating the total discharge of all road types to obtain a whole road network discharge list;
and the high-emission road section identification module is used for identifying the high-emission road section with the emission higher than a preset value in the whole-network emission list.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 7.
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| CN117422594A (en) * | 2023-08-14 | 2024-01-19 | 广东省科学院广州地理研究所 | High space-time resolution highway van carbon emission metering method and device |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117422594A (en) * | 2023-08-14 | 2024-01-19 | 广东省科学院广州地理研究所 | High space-time resolution highway van carbon emission metering method and device |
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