Method, device and equipment for processing traffic volume data of cross modulation station and storage medium

Abstract

The invention provides a traffic volume data processing method, device and equipment of an intermodulation station, and a storage medium. The specific implementation scheme is as follows: determining a reference traffic volume corresponding to a to-be-processed intermodulation station in a target time period; the reference traffic volume is determined according to the traffic volume of the surrounding cross-modulation stations of the to-be-processed cross-modulation station, the average traffic volume of the regional road network where the to-be-processed cross-modulation station is located or the year-on-year traffic volume or the ring-on-year traffic volume of the reference cross-modulation station of the to-be-processed cross-modulation station; and according to the reference traffic volume, determining a target traffic volume corresponding to the to-be-processed cross modulation station in the target time period. According to the technical scheme provided by the invention, the missing traffic volume of the cross-dispatching station in the specific time period can be accurately estimated, and the quality and integrity of traffic data acquired by the cross-dispatching station can be improved, so that the traffic management requirement is met, and the overall operation efficiency of a traffic system is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method, apparatus, equipment and storage medium for processing traffic volume data at a traffic control station. Background Technology

[0002] Traffic volume statistics and survey stations (hereinafter referred to as "traffic survey stations") are sites in the transportation field used to collect and monitor road traffic data. They use specialized equipment such as sensors, monitoring devices, and communication equipment to collect, transmit, and store traffic data such as road network traffic volume and vehicle axle load in real time, providing decision-making basis for highway planning and construction, traffic operation management, and emergency response. During traffic condition statistics and surveys, data may be missing for certain time periods due to factors such as equipment failure, network failure, electromechanical failure, and weather. This can prevent the data collected by traffic survey stations from effectively meeting the needs of planning and construction, traffic scheduling, and congestion reduction, thereby reducing the operational efficiency and safety of the transportation system. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for processing traffic volume data at a traffic control station, in order to alleviate or solve one or more technical problems existing in the prior art.

[0004] In a first aspect, embodiments of this application provide a method for processing traffic volume data from a traffic control station, including:

[0005] Determine the reference traffic volume for the traffic control station to be processed within the target time period; the reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations, the average traffic volume of the road network in the area where the traffic control station is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station.

[0006] Based on the reference traffic volume, determine the target traffic volume corresponding to the traffic control station to be processed within the target time period.

[0007] Secondly, embodiments of this application provide a processing apparatus for traffic volume data at a traffic control station, comprising:

[0008] The reference traffic volume determination module is used to determine the reference traffic volume corresponding to the traffic control station to be processed within the target time period. The reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations of the traffic control station to be processed, the average traffic volume of the road network in the area where the traffic control station to be processed is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station of the traffic control station to be processed.

[0009] The target traffic volume determination module is used to determine the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume.

[0010] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, wherein instructions are stored in the memory, and the instructions are loaded and executed by the processor to implement any of the methods of embodiments of this application.

[0011] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0012] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the embodiments of this application.

[0013] The traffic volume data processing method for traffic control stations in this application embodiment can determine a reference traffic volume based on the traffic volume of surrounding traffic control stations, the average traffic volume of the regional road network, or the year-on-year or month-on-month traffic volume of a reference traffic control station. Based on the reference traffic volume, the target traffic volume corresponding to the traffic control station in the target time period can be determined. This application embodiment can accurately estimate the missing traffic volume at a traffic control station within a specific time period, improving the quality and completeness of traffic data collected by the traffic control station, thereby meeting traffic management needs and improving the overall operational efficiency of the traffic system.

[0014] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of this application will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0015] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0016] Figure 1 A flowchart illustrating a method for processing traffic volume data at a traffic control station according to an embodiment of this application is provided.

[0017] Figure 2 This diagram illustrates the correlation between traffic volume at a traffic control station to be processed and a surrounding traffic control station according to an embodiment of this application.

[0018] Figure 3 This diagram illustrates the weighted average traffic volume of the province where the traffic control station to be processed is located, and the actual traffic volume of the traffic control station to be processed, according to an embodiment of this application.

[0019] Figure 4This diagram illustrates the traffic volume correlation between a traffic control station to be processed and a reference traffic control station according to an embodiment of this application.

[0020] Figure 5 A schematic diagram showing the estimated traffic volume based on surrounding traffic control stations according to an embodiment of this application is provided.

[0021] Figure 6 A schematic diagram showing the estimation results of the average traffic volume based on the provincial regional road network according to an embodiment of this application is provided.

[0022] Figure 7 A schematic diagram showing the estimation results of month-on-month traffic volume based on a reference traffic control station according to an embodiment of this application.

[0023] Figure 8 This is a structural block diagram of a traffic volume data processing apparatus for a traffic control station according to an embodiment of this application.

[0024] Figure 9 A block diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0025] Embodiments of this embodiment will now be described in more detail with reference to the accompanying drawings. While some embodiments of this embodiment are shown in the drawings, it should be understood that this embodiment can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this embodiment. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of this embodiment.

[0026] The following terminology will be used hereinafter. It should be noted that the following is a brief description of the basic concepts involved in the embodiments of this application, and it should be understood that the basic concepts introduced below do not limit the embodiments of this application.

[0027] Highway traffic condition statistical survey: This refers to the process of systematically collecting and analyzing key traffic characteristics such as traffic flow, vehicle speed, and axle load using specialized equipment and scientific methods. The data obtained from highway traffic surveys is a crucial basis for decision-making in highway planning, construction, operation management, and emergency response.

[0028] Traffic flow, or simply traffic volume, refers to the number of vehicles or pedestrians passing through a specific lane, location, or cross-section of a road within a unit of time. It is typically statistically analyzed on an hourly, daily, or yearly basis. Cross-sectional traffic volume is a commonly used indicator in the highway transportation sector for measuring highway traffic conditions, and it is directly obtained from traffic volume statistical survey stations set up along the highway.

[0029] ETC (Electronic Toll Collection) for highways: ETC can collect vehicle information (license plate number, vehicle type, color, brand), vehicle traffic data (travel time, travel location, direction of travel), vehicle speed and driving status, etc., to provide accurate toll collection services and comprehensive traffic management support, and improve the level of intelligent traffic management.

[0030] Traffic volume data collection network (hereinafter referred to as regional network) is a regional traffic volume collection system composed of automated traffic control stations and highway ETC (Electronic Toll Collection) systems. The regional network covers key roads, city boundaries, and provincial boundaries, connecting areas with high traffic flow such as city entrances / exits, tourist attractions, and logistics parks to reflect the traffic volume distribution characteristics of national, provincial, or county-level road networks. Regional network traffic volume, as a representation of the total traffic volume in a region within a unit of time, can be characterized by a weighted average of the cross-sectional traffic volumes observed by traffic control stations within the network area.

[0031] During traffic condition statistical surveys, factors such as equipment failure, network failure, electromechanical failure, and weather may cause data collection gaps at traffic survey stations during certain time periods. This results in the collected data failing to effectively meet the needs of traffic applications such as road planning and construction, traffic scheduling, and congestion reduction, thereby reducing the operational efficiency and safety of the traffic system.

[0032] Figure 1 A flowchart illustrating a method for processing traffic volume data at a traffic control station according to an embodiment of this application is provided. See also... Figure 1 The method for processing traffic volume data at traffic control stations provided in this application embodiment specifically includes the following steps:

[0033] Step S101: Determine the reference traffic volume corresponding to the traffic control station to be processed within the target time period; the reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations of the traffic control station to be processed, the average traffic volume of the road network in the area where the traffic control station to be processed is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station of the traffic control station to be processed.

[0034] Step S102: Based on the reference traffic volume, determine the target traffic volume corresponding to the traffic control station to be processed within the target time period.

[0035] For example, the traffic control station to be processed can be a traffic control station where traffic volume data for the target time period is missing and the traffic volume for the target time period needs to be calculated.

[0036] It should be noted that within the same geographical area, due to the combined effects of factors such as economic development level, population distribution, residents' travel habits, transportation infrastructure construction, emergencies, and meteorological conditions, traffic volume data collected by different traffic monitoring stations within the same geographical area exhibit significant correlation characteristics.

[0037] The traffic control stations to be processed are correlated with their surrounding traffic control stations or other traffic control stations within the road network of their respective areas in terms of traffic volume trends. Specifically, the traffic volume of the surrounding traffic control stations of the station to be processed can characterize the traffic volume of roads in the vicinity of the station; the average traffic volume of the road network in the area where the station is located can characterize the overall traffic volume of the area; and the year-on-year or month-on-month traffic volume of the reference traffic control station of the station to be processed can reflect the trend of traffic volume changes over time in the vicinity of the station. The reference traffic control station can be a surrounding traffic control station that has a high correlation with the station to be processed in terms of traffic volume trends.

[0038] The method provided in this application embodiment can determine the reference traffic volume corresponding to the traffic control station to be processed within the target time period based on the correlation between the traffic control station to be processed and surrounding traffic control stations and other traffic control stations within the regional road network. By using existing traffic volume data, the missing data of the traffic control station to be processed within the target time period can be estimated, thereby improving the accuracy and completeness of traffic data collected by the traffic control station, thus meeting traffic management needs and improving the overall operating efficiency of the traffic system.

[0039] In one implementation, determining the reference traffic volume of the traffic control station to be processed within a target time period may include: determining at least one surrounding traffic control station associated with the traffic control station to be processed within a preset distance range; determining a first reference traffic control station from the at least one surrounding traffic control station based on the correlation between each surrounding traffic control station and the traffic control station to be processed; and determining the traffic volume corresponding to the first reference traffic control station within the target time period as the reference traffic volume.

[0040] For example, the surrounding traffic control stations can be traffic control stations located within a preset distance range around the traffic control station to be processed. The traffic volume data collected by the surrounding traffic control stations and the traffic control station to be processed can be affected by the same factors, such as the same level of economic development, population distribution, travel patterns, or traffic infrastructure, making the traffic volume data of the surrounding traffic control stations correlated with the traffic volume data of the traffic control station to be processed.

[0041] For example, the correlation between the communication station to be processed and each surrounding communication station can be screened to determine a first reference communication station from at least one surrounding communication station.

[0042] For example, the Spearman rank correlation coefficient can be used to screen traffic control stations between the one being analyzed and its surrounding stations. The Spearman rank correlation coefficient measures the monotonic relationship between two variables, with values ​​ranging from -1 to 1. A positive Spearman rank correlation coefficient indicates a positively correlated monotonic relationship between the traffic volume data of the two stations; that is, an increase in traffic volume at one station corresponds to an increase in traffic volume at the other. A negative Spearman rank correlation coefficient indicates a negatively correlated monotonic relationship; that is, an increase in traffic volume at one station corresponds to a decrease in traffic volume at the other. A Spearman rank correlation coefficient of 0 indicates no monotonic correlation between the traffic volume data of the two stations; that is, there is no clear trend correlation between changes in traffic volume at one station and changes in traffic volume at the other. The closer the absolute value of the Spearman rank correlation coefficient is to 1, the stronger the correlation between the traffic volumes of the two traffic control stations; the closer the absolute value of the Spearman rank correlation coefficient is to 0, the weaker the correlation between the traffic volumes of the two traffic control stations.

[0043] Figure 2 This diagram illustrates the traffic volume correlation between a traffic control station to be processed and a surrounding traffic control station according to an embodiment of this application. For example, the traffic control station to be processed may be traffic control station X. Figure 2 As shown, the Spearman correlation coefficient between traffic control station X and traffic control station Y is 0.83, indicating a strong monotonic correlation between their traffic volumes.

[0044] For example, if the Spearman-level correlation coefficient between the traffic control station to be processed and a certain surrounding traffic control station is high, it indicates that the traffic volume change trends of the two are highly similar. The surrounding traffic control station can be identified as the first reference traffic control station, and the traffic volume corresponding to the first reference traffic control station in the target time period can be identified as the reference traffic volume.

[0045] In this way, the method provided in this application embodiment can filter the first reference traffic control station based on the correlation between the traffic control station to be processed and its surrounding traffic control stations, thereby improving the accuracy and completeness of the calculation of missing traffic volume data of the traffic control station to be processed, thus meeting the needs of traffic management and improving the overall operating efficiency of the traffic system.

[0046] In one implementation, determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume may include: determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume and the average traffic volume corresponding to the traffic control station to be processed and the first reference traffic control station within at least one reference time period in a preset time cycle.

[0047] For example, the reference time period can be one or more segments of time specifically used to analyze the average traffic volume of the traffic control station to be processed and the first reference traffic control station. The reference time period can be divided by hour, day, week, etc., or by specific time periods such as morning peak and evening peak. The embodiments of this application do not limit the specific duration of the reference time period in the technical solution.

[0048] For example, the average traffic volume of the traffic control station to be processed and the first reference traffic control station within at least one reference time period in a preset time cycle can be the average traffic volume of the traffic control station to be processed and the first reference traffic control station within at least one identical time period in the preset time cycle. For instance, if the traffic volume collected by traffic control station A from 9:00 AM to 10:00 AM on each Monday of the past four weeks is 100, 120, 110, and 130 vehicles respectively, then the average traffic volume of traffic control station A during this reference time period is: (100 + 120 + 110 + 130) ÷ 4 = 115 vehicles. Similarly, if the traffic volume collected by traffic control station B from 9:00 AM to 10:00 AM on each Monday of the past four weeks is 80, 90, 85, and 95 vehicles respectively, then the average traffic volume of traffic control station B during this reference time period is: (80 + 90 + 85 + 95) ÷ 4 = 87.5 vehicles.

[0049] It should be noted that the specific division methods of the above-mentioned preset time period and reference time period, as well as the specific examples of traffic volume, are only illustrative and do not constitute a limitation on this application. Those skilled in the art can set them flexibly according to the actual situation.

[0050] For example, the traffic volume data of the first reference traffic control station can be used as a reference traffic volume. The target traffic volume of the traffic control station to be processed in the target time period can be determined based on the traffic volume of the first reference traffic control station in the target time period and the ratio of the average traffic volume of the traffic control station to be processed and the first reference traffic control station in at least one reference time period in the preset time period.

[0051] For example, the formula for determining the traffic volume of the traffic control station to be processed within the target time period may include:

[0052]

[0053] Where Q can represent the traffic volume at the traffic control station to be processed within the target time period, Q a It can represent the traffic volume at the first reference traffic control station within the target time period. These can represent the average traffic volume of the traffic control station to be processed and the first reference traffic control station during the same time period within a preset time cycle.

[0054] In one example, the traffic control station to be processed can be traffic control station A. Table 1 shows the traffic volume correlation information of its surrounding traffic control stations when traffic control station A is the traffic control station to be processed. As shown in Table 1, the Spearman-level correlation coefficient between the traffic control station to be processed and 10 surrounding traffic control stations within a preset distance range can be calculated. The surrounding traffic control stations with Spearman-level correlation coefficients in the range of [0.6-1] and with valid data collection within the target time period are designated as the first reference traffic control stations. They are then sorted in descending order of correlation coefficient as the priority for reference. The valid traffic volume collected by the first reference traffic control stations within the target time period is determined as the reference traffic volume. Based on the traffic volume of each first reference traffic control station within the target time period, and the ratio of the average traffic volume of traffic control station A to that of each first reference traffic control station within at least one reference time period in the preset time cycle, the target traffic volume corresponding to the traffic control station to be processed within the target time period can be determined. It should be noted that the above example of surrounding traffic control station selection based on correlation coefficients is only illustrative and does not constitute a limitation of this application. Those skilled in the art can flexibly set it according to the actual situation.

[0055] Table 1. Traffic volume correlation information of surrounding traffic control stations A

[0056]

[0057] In this way, the method provided in this application embodiment can determine the target traffic volume of the traffic control station to be processed within the target time period based on the traffic volume of the first reference traffic control station within the target time period, and the average traffic volume of the traffic control station to be processed and the first reference traffic control station within at least one reference time period in the preset time cycle. This enables the target traffic volume to fit the traffic volume benchmark value of the traffic control station to be processed, improves the accuracy and completeness of the calculation of missing traffic volume data of the traffic control station to be processed, thereby meeting traffic management needs and improving the overall operating efficiency of the traffic system.

[0058] In one implementation, determining the reference traffic volume of the traffic control station to be processed within a target time period includes: determining the regional road network where the traffic control station to be processed is located; calculating the average traffic volume of all traffic control stations within the target time period based on the traffic volume of each traffic control station in the regional road network within the target time period, and determining the average traffic volume as the reference traffic volume.

[0059] For example, the regional road network can be the road network within a certain geographical area where the traffic control station to be processed is located. It should be noted that the average traffic volume of the regional road network where the traffic control station is located can characterize the overall traffic volume of the area where the traffic control station is located. For example, the average traffic volume of all traffic control stations included in the regional road network within the target time period can be determined as the reference traffic volume.

[0060] In one example, the regional road network can be the road network within the administrative region where the traffic dispatch station to be processed is located. It may include the regional road network of the district or county where the traffic dispatch station to be processed is located, the regional road network of the prefecture-level city where the traffic dispatch station to be processed is located, or the regional road network of the province where the traffic dispatch station to be processed is located. This application embodiment does not limit the method of selecting the region or the specific method of dividing the regional road network.

[0061] For example, the average traffic volume corresponding to all traffic control stations included in the regional road network within the target time period can be a weighted average traffic volume. The calculation weight of each traffic control station included in the regional road network can be determined based on factors such as the distance between the traffic control station and the traffic control station to be processed, the grade of the road monitored by the traffic control station, the reliability of the traffic control station data, and the road connectivity between the traffic control station and the traffic control station to be processed. This application embodiment does not limit the specific factors for determining the above-mentioned calculation weights.

[0062] It should be noted that the above-mentioned method for calculating the average traffic volume of the regional road network is only an example and does not constitute a limitation on this application. Those skilled in the art can flexibly set it according to the actual situation.

[0063] Figure 3 This diagram illustrates the weighted average traffic volume of the province where the traffic control station to be processed is located, and the actual traffic volume of the traffic control station to be processed, according to an embodiment of this application. In one example, such as... Figure 3 As shown, the trend of the weighted average traffic volume in the province where the traffic control station is located has a significant correlation with the trend of the actual traffic volume at the traffic control station. Therefore, the average traffic volume of the regional road network in the province where the traffic control station is located during the target time period can be determined as the reference traffic volume.

[0064] In one implementation, determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume may include: determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume and the average traffic volume of the traffic control station to be processed and the regional road network within at least one reference time period in a preset time cycle.

[0065] For example, the reference time period can be one or more segments of time specifically used to analyze the average traffic volume of the traffic control station to be processed and the average traffic volume of the road network in the area where the traffic control station is located. The reference time period can be divided by hour, day, week, etc., or by specific time periods such as morning peak and evening peak. The specific duration of the reference time period in the embodiments of this application is not limited.

[0066] For example, the average traffic volume of the traffic control station to be processed and the regional road network in at least one reference time period in the preset time period can be the average traffic volume of the traffic control station to be processed and the regional road network in at least one same time period in the preset time period.

[0067] It should be noted that the above description of the reference time period is for illustrative purposes only and does not constitute a limitation on this application. Those skilled in the art can set it flexibly according to the actual situation.

[0068] In one implementation, the formula for determining the traffic volume of the traffic control station to be processed within the target time period includes:

[0069]

[0070] Where Q can represent the traffic volume at the traffic control station to be processed within the target time period, Q sj It can represent the average traffic volume of the regional road network during the target time period. These can represent the average traffic volume of the traffic control station to be processed and the regional road network during the same time period within a preset time cycle.

[0071] In this way, the method provided in this application embodiment can determine the target traffic volume of the traffic control station in the target time period based on the average traffic volume of the road network in the area where the traffic control station is located, which is reliable and accurate. This can improve the accuracy and completeness of the calculation of missing traffic volume data of the traffic control station, thereby meeting traffic management needs and improving the overall operating efficiency of the traffic system.

[0072] In one implementation, determining the reference traffic volume of the traffic control station to be processed within a target time period includes:

[0073] Identify at least one reference inter-station associated with the inter-station to be processed within a preset distance range;

[0074] Based on the year-on-year or month-on-month correlation coefficients between each reference traffic control station and the traffic control station to be processed, a second reference traffic control station is determined from at least one reference traffic control station, and the traffic volume corresponding to the second reference traffic control station in the target time period is used as the reference traffic volume.

[0075] For example, the reference traffic control station can be a traffic control station located within a preset distance of the traffic control station to be processed, and whose traffic volume trend is correlated with that of the traffic control station to be processed in terms of year-on-year or month-on-month changes. The traffic volume data collected by the reference traffic control station and the traffic control station to be processed can be affected by the same factors, such as the traffic control station to be processed and the reference traffic control station being located on roads of the same grade (e.g., both being highways, arterial roads, etc.), similar types (e.g., both being urban expressways, national highways, etc.), and similar surrounding land use functions, so that the traffic volume data of the traffic control station to be processed and the traffic volume data of the reference traffic control station are correlated in terms of year-on-year or month-on-month changes.

[0076] For example, the correlation between the inter-station to be processed and each reference inter-station can be screened based on the year-on-year or month-on-month correlation coefficient between each reference inter-station and the inter-station to be processed, and a second reference inter-station can be determined from at least one reference inter-station.

[0077] For example, the correlation between the traffic monitoring station to be processed and each reference traffic monitoring station can be screened based on the year-on-year or month-on-month Pearson correlation coefficient. The year-on-year or month-on-month Pearson correlation coefficient can be used to measure the degree of linear correlation between two variables in their year-on-year or month-on-month trends. In one example, the year-on-year Pearson correlation coefficient can be used to measure the correlation between the traffic volume trends of two traffic monitoring stations in the same historical period, for example, comparing the traffic volume data of two traffic monitoring stations in the same quarter of each year. The month-on-month Pearson correlation coefficient can be used to measure the correlation between the trends of two variables in adjacent periods, for example, comparing the traffic volume changes of two traffic monitoring stations in a specific month compared to the previous month. The closer the absolute value of the year-on-year or month-on-month Pearson correlation coefficient is to 1, the stronger the correlation between the traffic volume trends of the two traffic monitoring stations in their year-on-year or month-on-month trends; the closer the absolute value of the year-on-year or month-on-month Pearson correlation coefficient is to 0, the weaker the correlation between the traffic volume trends of the two traffic monitoring stations in their year-on-year or month-on-month trends. It should be noted that the above specific examples are merely illustrative and do not constitute a limitation of this application. Those skilled in the art can flexibly set the parameters according to the actual situation.

[0078] Table 2 shows the chain correlation coefficients of reference stations when station B is the station to be processed. For example, station B can be the station to be processed. As shown in Table 2, a second reference station can be determined among at least one reference station based on the chain correlation coefficients between each reference station and station B.

[0079] Table 2. Chain correlation coefficients of reference stations for dispatch station B

[0080]

[0081]

[0082] Figure 4 This diagram illustrates the traffic volume correlation between a traffic control station to be processed and a reference traffic control station according to an embodiment of this application. For example, the traffic control station to be processed may be traffic control station B, and the reference traffic control station may be traffic control station C. Figure 4 As shown, the traffic volume trends at traffic control station B and traffic control station C are strongly correlated.

[0083] For example, if the year-on-year or month-on-month Pearson correlation coefficient between the traffic control station to be processed and a specific reference traffic control station is high, it indicates that the traffic volume change trends of the two are highly similar. The surrounding traffic control station can be identified as the second reference traffic control station, and the traffic volume corresponding to the second reference traffic control station in the target time period can be identified as the reference traffic volume.

[0084] In this way, the method provided in this application embodiment can screen the second reference traffic control station based on the correlation of year-on-year or month-on-month traffic volume change trends, thereby improving the accuracy and completeness of the calculation of missing traffic volume data of the traffic control station to be processed, thus meeting the needs of traffic management and improving the overall operating efficiency of the traffic system.

[0085] In one implementation, determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume may include: determining the year-on-year or month-on-month result of the year-on-year or month-on-month traffic volume corresponding to the second reference traffic control station within the reference time period; and determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the traffic volume corresponding to the traffic control station to be processed within the reference time period and the year-on-year or month-on-month result.

[0086] For example, the reference time period can be the base period, i.e., the period used as the benchmark in year-on-year or month-on-month calculations, and the target time period for the traffic control station to be processed can be the current period in year-on-year or month-on-month calculations. The traffic volume data of the traffic control station to be processed in the base period is known and determined, and the traffic volume data of the second reference traffic control station in both the base period and the current period are known and determined. The base period and the current period can be divided by hour, day, week, etc. This application embodiment does not limit the specific duration of the reference time period in the technical solution.

[0087] It should be noted that the specific examples of the reference time periods mentioned above are merely illustrative and do not constitute a limitation on this application. Those skilled in the art can flexibly set them according to the actual situation.

[0088] For example, the traffic volume data corresponding to the second reference traffic control station within the target time period can be used as reference traffic volume. The year-on-year or month-on-month result of the current traffic volume of the second reference traffic control station relative to its base period traffic volume can be determined. Based on the year-on-year or month-on-month result of the current traffic volume of the second reference traffic control station relative to its base period traffic volume, the target traffic volume corresponding to the traffic control station to be processed within the target time period can be determined.

[0089] In one implementation, the formula for determining the traffic volume of the traffic control station to be processed within the target time period may include:

[0090]

[0091] Where Q represents the traffic volume at the traffic control station to be processed within the target time period. The traffic volume of the traffic control station to be processed is the traffic volume of the base period calculated year-on-year or month-on-month, and r represents the traffic volume of the second reference traffic control station in the target time period relative to its year-on-year or month-on-month traffic volume in the base period calculated year-on-year or month-on-month.

[0092] In this way, the method provided in this application embodiment can determine a reference traffic control station that is strongly correlated with the traffic volume change trend of the traffic control station to be processed. By referring to the year-on-year or month-on-month traffic volume results of the reference traffic control station, the calculated target traffic volume can be made to fit the true traffic volume value of the traffic control station to be processed, thereby improving the accuracy and completeness of the calculation of missing traffic volume data of the traffic control station to be processed, thus meeting the traffic management needs and improving the overall operating efficiency of the traffic system.

[0093] For example, in some complex application scenarios, the practicality of traffic volume data processing methods and the accuracy of calculation results can be comprehensively considered, and appropriate traffic volume data processing methods can be adopted based on the specific circumstances of the traffic monitoring station to be processed. Specifically, the cross-sectional average traffic volume error rate can be selected as an evaluation index to evaluate the calculation results of each traffic volume data processing method, so as to determine the actual traffic volume data processing method to be adopted.

[0094] Figure 5 This diagram illustrates the estimation results of traffic volume based on surrounding traffic control stations according to an embodiment of this application. Figure 6 This diagram illustrates the estimation results based on the average traffic volume of a provincial regional road network according to an embodiment of this application. Figure 7 A schematic diagram showing the estimation results of month-on-month traffic volume based on a reference traffic control station according to an embodiment of this application.

[0095] like Figures 5 to 7 As shown, for example, based on the existing actual traffic volume data of the traffic control station to be processed during a specific time period, the target traffic volume of the traffic control station to be processed during that specific time period can be estimated using various traffic volume data processing methods. Specifically, the target traffic volume of the traffic control station to be processed during that specific time period can be estimated based on the traffic volume of surrounding traffic control stations, the average traffic volume of the provincial regional road network, or the month-on-month traffic volume of a reference traffic control station, and the error of each traffic volume data processing method can be calculated based on the existing actual traffic volume data of the traffic control station to be processed during that specific time period.

[0096] For example, the error rate of each traffic volume data processing method can be calculated based on existing traffic volume data. The error rate of each traffic volume data processing method can characterize the error between the daily weighted average of national traffic volume and the estimated weighted average of the target traffic volume of the traffic control station to be processed for that specific time period.

[0097] Table 3 shows the estimation results for selected provinces based on various traffic volume data processing methods.

[0098]

[0099]

[0100] Table 3 shows the estimation results of traffic volume data processing stations in some provinces based on various traffic volume data processing methods. As shown in Table 3, from a national perspective, the error rate of each estimation method does not exceed 3%, ensuring the macro-level accuracy of traffic volume estimation. From the perspective of individual provinces, the estimation method based on traffic volume from surrounding traffic monitoring stations has a smaller error, but in actual scenarios, there are few traffic monitoring stations that meet the estimation requirements, thus leading to situations where estimation results cannot be obtained stably. The estimation based on the provincial average traffic volume yields the best results, with a smaller error in traffic volume estimation.

[0101] Therefore, a fusion method can be used to estimate the target traffic volume of traffic monitoring stations within a specific time period. Specifically, an appropriate traffic volume data processing method can be applied based on the specific circumstances of the traffic monitoring station. For example, the average error rate of each traffic volume data processing method within each province can be calculated, and a traffic volume data processing method with a smaller error rate can be uniformly used to estimate the traffic volume of traffic monitoring stations within that province on that day. If the traffic monitoring station does not have the conditions for estimation based on data from surrounding highway traffic monitoring stations, an estimation method based on the average traffic volume of the provincial regional road network can be adopted. If the average traffic volume of the provincial regional road network cannot be obtained, an estimation can be based on the average traffic volume of the national regional road network. Table 0 shows the average error of the fusion method estimation. As shown in Table 4, the error rate of traffic volume estimated by the fusion method is significantly smaller than the error rate of traffic volume estimated using a single traffic volume data processing method.

[0102] Table 0 shows the average error estimated by the fusion method.

[0103]

[0104]

[0105] This application also provides a device for processing traffic volume data at a traffic control station. Figure 8 This diagram illustrates a structural block diagram of a traffic volume data processing apparatus for a traffic control station according to an embodiment of this application. Figure 8 As shown, the apparatus provided in this application embodiment may include:

[0106] The reference traffic volume determination module 801 is used to determine the reference traffic volume corresponding to the traffic control station to be processed within the target time period. The reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations of the traffic control station to be processed, the average traffic volume of the road network in the area where the traffic control station to be processed is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station of the traffic control station to be processed.

[0107] The target traffic volume determination module 802 is used to determine the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume.

[0108] The device provided in this application embodiment can determine a reference traffic volume based on the traffic volume of surrounding traffic control stations, the average traffic volume of the regional road network, or the year-on-year or month-on-month traffic volume of a reference traffic control station. Based on the reference traffic volume, it can determine the target traffic volume corresponding to the traffic control station within a target time period. The traffic data processing device in this application embodiment can accurately estimate the missing traffic volume at a traffic control station within a specific time period, improving the quality and completeness of traffic data collected by the traffic control station, thereby meeting traffic management needs and improving the overall operational efficiency of the traffic system.

[0109] This application also provides an electronic device. Figure 9 A structural block diagram of an electronic device according to an embodiment of this application is shown. Figure 9 As shown, the electronic device includes a processor 910 and a memory 920. The memory 920 stores instructions, which are loaded and executed by the processor 910 to implement the method of any of the embodiments of this application. The number of memories 920 and processors 910 can be one or more.

[0110] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of the embodiments of this application.

[0111] This application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the methods described in this application.

[0112] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, 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, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting the Advanced Reduced Instruction Set Computing (RISC) machine (ARM) architecture.

[0113] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0114] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0115] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0117] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0119] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing traffic volume data at a traffic control station, characterized in that, include: Determine the reference traffic volume for the traffic control station to be processed within the target time period; The reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations of the traffic control station to be processed, the average traffic volume of the road network in the area where the traffic control station to be processed is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station of the traffic control station to be processed. Based on the reference traffic volume, determine the target traffic volume corresponding to the traffic control station to be processed within the target time period.

2. The method according to claim 1, characterized in that, The determination of the reference traffic volume for the traffic control station to be processed within the target time period includes: Identify at least one surrounding inter-station associated with the inter-station to be processed within a preset distance range; Based on the correlation between each of the surrounding traffic control stations and the traffic control station to be processed, a first reference traffic control station is determined from at least one of the surrounding traffic control stations, and the traffic volume corresponding to the first reference traffic control station in the target time period is determined as the reference traffic volume.

3. The method according to claim 2, characterized in that, Determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume includes: Based on the reference traffic volume and the average traffic volume of the traffic control station to be processed and the first reference traffic control station in at least one reference time period in a preset time period, the target traffic volume corresponding to the traffic control station to be processed in the target time period is determined.

4. The method according to claim 1, characterized in that, The determination of the reference traffic volume for the traffic control station to be processed within the target time period includes: Determine the road network of the area where the traffic control station to be processed is located; Based on the traffic volume corresponding to each of the traffic control stations included in the regional road network during the target time period, the average traffic volume of all traffic control stations during the target time period is calculated, and the average traffic volume is determined as the reference traffic volume.

5. The method according to claim 4, characterized in that, Determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume includes: Based on the reference traffic volume and the average traffic volume of the traffic control station to be processed and the regional road network in at least one reference time period within a preset time cycle, the target traffic volume corresponding to the traffic control station to be processed in the target time period is determined.

6. The method according to claim 1, characterized in that, The determination of the reference traffic volume for the traffic control station to be processed within the target time period includes: Identify at least one reference inter-station associated with the inter-station to be processed within a preset distance range; Based on the year-on-year or month-on-month correlation coefficient between each of the reference traffic control stations and the traffic control station to be processed, a second reference traffic control station is determined from at least one of the reference traffic control stations, and the traffic volume corresponding to the second reference traffic control station in the target time period is used as the reference traffic volume.

7. The method according to claim 6, characterized in that, Determining the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume includes: Determine the year-on-year or month-on-month result of the reference traffic volume relative to the second reference traffic control station within the reference time period; Based on the traffic volume corresponding to the traffic control station to be processed within the reference time period, as well as the year-on-year results and / or the month-on-month results, the target traffic volume corresponding to the traffic control station to be processed within the target time period is determined.

8. A device for processing traffic volume data at a traffic control station, characterized in that, include: The reference traffic volume determination module is used to determine the reference traffic volume corresponding to the traffic control station to be processed within the target time period; The reference traffic volume is determined based on the traffic volume of the surrounding traffic control stations of the traffic control station to be processed, the average traffic volume of the road network in the area where the traffic control station to be processed is located, or the year-on-year or month-on-month traffic volume of the reference traffic control station of the traffic control station to be processed. The target traffic volume determination module is used to determine the target traffic volume corresponding to the traffic control station to be processed within the target time period based on the reference traffic volume.

9. An electronic device, characterized in that, include: A processor and a memory, wherein instructions are stored in the memory and loaded and executed by the processor to implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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