Traffic flow statistical method and device based on traffic big data analysis and storage medium

Abstract

The invention provides a traffic flow statistical method and device based on traffic big data analysis and a storage medium. The method comprises the steps of obtaining road network data after road section splicing based on road section codes and head and tail coordinates of the road network data; determining vehicle point location data and vehicle categories; determining a road section code corresponding to the vehicle point location data based on the road network data after the road sections are spliced and the vehicle point location data; and determining traffic flow data based on the road section code corresponding to the vehicle point location data and the vehicle category. According to the traffic flow statistical method and device based on traffic big data analysis and the storage medium provided by the invention, the road sections can be reasonably spliced according to the road section codes and the head and tail coordinates of the road network data, the road network data after the road sections are spliced are obtained, and then the vehicle point location data and the road network data after the road sections are spliced are matched, so that the traffic flow statistical accuracy is improved. The traffic flow distribution condition analysis of the road section can be realized, and the statistical analysis efficiency of the road section is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a vehicle flow statistics method, device and storage medium based on traffic big data analysis. Background Art

[0002] Road traffic volume is the most basic traffic parameter. Through the analysis of road traffic volume, we can understand the situation of vehicles passing through a certain area, thereby assisting in vehicle refueling market analysis and decision-making and refueling station site selection. Through the analysis of station inbound traffic volume, we can further understand and track the number and changes of vehicles entering the station for refueling on the basis of road traffic volume, estimate the station sales volume and inbound rate, and provide reference for the station's business analysis and market potential analysis. Based on traffic big data, the research on the statistical methods of station and road traffic volume has very important practical significance and application value.

[0003] The current mainstream traffic flow data analysis method is mainly based on camera-based traffic flow statistics analysis. Due to the installation location of the collection terminal and the location of the road intersection, it is impossible to conduct statistical analysis on all road sections at the national level, and it is impossible to measure the inbound and through traffic at refueling stations. Summary of the invention

[0004] The present invention provides a vehicle flow statistics method, device and storage medium based on traffic big data analysis, which are used to solve the technical problem of low efficiency in the prior art when performing statistical analysis on all road sections at the national level.

[0005] The present invention provides a vehicle flow statistics method based on traffic big data analysis, comprising the following steps.

[0006] Obtain the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; Determine vehicle location data and vehicle category; Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data; The vehicle flow data is determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0007] In some embodiments, the step of obtaining the road network data after the road segments are spliced ​​based on the road segment codes and the first and last coordinates of the road network data includes: Obtaining the section coordinates and section codes in the road network data, wherein the section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; When the end coordinates of the first road network data are not repeated and the length of the first road network data is less than the first threshold, the end coordinates of the first road network data are used as the start coordinates of the second road network data; The first road network data and the second road network data are concatenated.

[0008] In some embodiments, the method further comprises: In the case where the same road segment codes exist, the road network data after the road segments are spliced ​​is corrected based on the road segment coordinates.

[0009] In some embodiments, determining the vehicle location data and the vehicle category includes: Determine vehicle type and fuel type; Corresponding vehicle location data is acquired based on the vehicle type and the fuel type.

[0010] In some embodiments, the determining the road segment code corresponding to the vehicle point data based on the road network data after the road segment is spliced ​​and the vehicle point data includes: Based on the coordinate system conversion, the road network data after the road sections are spliced ​​and the vehicle point data are converted to the same coordinate system, and the road section code corresponding to the vehicle point data is determined.

[0011] In some embodiments, the determining of the vehicle flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: The station inbound vehicle flow data and the station through-station vehicle flow data are determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0012] In some embodiments, determining the station entry traffic flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold, determining the station entry information data of the vehicle; Determine the station entry traffic data based on the entry information data.

[0013] In some embodiments, the determining of the station transit traffic data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a fourth threshold, determining the station passing information data of the vehicle; Based on the vehicle category, the data that is repeated with the station entry information data is removed from the station passing information data; Determine the station transit traffic data based on the deduplicated transit information data.

[0014] The present invention also provides a vehicle flow statistics device based on traffic big data analysis, comprising the following modules: A first acquisition module is used to acquire the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; A first determination module is used to determine vehicle location data and vehicle category; A second determination module is used to determine the road section code corresponding to the vehicle point data based on the road network data after the road section is spliced ​​and the vehicle point data; The third determination module is used to determine the vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category.

[0015] In some embodiments, the first acquisition module includes: A first acquisition submodule is used to acquire the road section coordinates and road section codes in the road network data, wherein the road section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; A first determination submodule, configured to use the end coordinates of the first road network data as the start coordinates of the second road network data when the end coordinates of the first road network data are not repeated and the length of the first road network data is less than a first threshold; The first splicing submodule is used to splice the first road network data and the second road network data.

[0016] In some embodiments, the first acquisition module further includes: The first processing submodule is used to correct the road network data after the road sections are spliced ​​based on the road section coordinates when there are identical road section codes.

[0017] In some embodiments, the first determining module includes: A second determination submodule, for determining a vehicle type and a fuel type; The second acquisition submodule is used to acquire corresponding vehicle point data based on the vehicle type and the fuel type.

[0018] In some embodiments, the second determining module includes: The third determination submodule is used to convert the road network data after the road sections are spliced ​​and the vehicle point data into the same coordinate system based on the coordinate system conversion, and determine the road section code corresponding to the vehicle point data.

[0019] In some embodiments, the third determination module includes: The fourth determination submodule is used to determine the station entry vehicle flow data and the station transit vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category.

[0020] In some embodiments, the fourth determining submodule includes: A first determining unit, configured to determine a distance between the vehicle and the site based on the vehicle position data and the site data; A second determining unit, configured to determine the station entry information data of the vehicle when the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold; The third determining unit is used to determine the station entry vehicle flow data based on the station entry information data.

[0021] In some embodiments, the fourth determining submodule includes: A fourth determining unit, configured to determine a distance between the vehicle and the site based on the vehicle position data and the site data; A fifth determining unit, configured to determine the station passing information data of the vehicle when the distance between the vehicle and the station is not greater than a fourth threshold; A first processing unit, configured to remove data in the stop-by information data that is duplicated with the stop-by information data based on the vehicle category; The sixth determination unit is used to determine the station transit traffic flow data based on the deduplicated transit information data.

[0022] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a vehicle flow statistics method based on traffic big data analysis as described above is implemented.

[0023] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the vehicle flow statistics method based on traffic big data analysis as described in any one of the above is implemented.

[0024] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned vehicle flow statistics methods based on traffic big data analysis.

[0025] The vehicle flow statistics method, device and storage medium based on traffic big data analysis provided by the present invention can reasonably splice the road sections according to the road section codes and the first and last coordinates of the road network data, obtain the road network data after the road sections are spliced, and then match the vehicle point data with the road network data after the road sections are spliced, so as to realize the analysis of the vehicle flow distribution of the road sections and improve the efficiency of statistical analysis of the road sections. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a flow chart of the vehicle flow statistics method based on traffic big data analysis provided by the present invention.

[0028] Figure 2 It is a schematic diagram of the process of road section splicing provided by the present invention.

[0029] Figure 3 It is a schematic diagram of the flow chart of road section traffic flow statistics provided by the present invention.

[0030] Figure 4 It is a schematic diagram of the process of calculating the site vehicle flow provided by the present invention.

[0031] Figure 5 It is a structural schematic diagram of a vehicle flow statistics device based on traffic big data analysis provided by the present invention.

[0032] Figure 6 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0033] The current method for calculating vehicle flow requires the use of camera data at traffic intersections, which is quite limited. With the regulatory requirements for hazardous chemicals and heavy-duty trucks, these types of vehicles are required to install GPS signal terminals, so flow measurement based on vehicle GPS point data has become possible. The road section traffic flow measurement technology in the present invention uses the RD-tree algorithm as a spatial index on the basis of the existing road section flow algorithm on the market to optimize and accelerate road network query performance. After comparative analysis, the daily traffic flow calculation time for roads across the country has been reduced from the original 4 hours to 30 minutes, greatly improving the efficiency of the algorithm. At the same time, combined with actual business experience, the characteristics of vehicle refueling behavior are summarized, and the site traffic flow measurement method is innovatively designed, and it is continuously optimized according to actual data to achieve a higher data accuracy.

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Figure 1 is a flow chart of a vehicle flow statistics method based on traffic big data analysis provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101, obtaining the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data.

[0036] Specifically, firstly, all road network data are obtained, and the road network data may include the section code of each road section and the first and last coordinates corresponding to the road section.

[0037] Then the road sections are spliced ​​by the first and last coordinates, and manually corrected according to the road section coding, and finally the road network data after the road sections are spliced ​​is obtained.

[0038] Step 102, determine vehicle location data and vehicle category.

[0039] Specifically, the vehicle flow statistics method based on traffic big data analysis provided by the present application can count the vehicle flow of a certain category of vehicles by screening the vehicle range.

[0040] Vehicle category can be determined based on vehicle type, fuel type, etc.

[0041] Step 103: Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data.

[0042] Specifically, the vehicle point data and the road network data after the road sections are spliced ​​can be quickly matched through the coordinate system projection transformation and the query algorithm to obtain the road section code corresponding to the vehicle point data.

[0043] Step 104: determine the vehicle flow data based on the road segment code corresponding to the vehicle point data and the vehicle category.

[0044] Specifically, the road section traffic flow data of the vehicle category to be counted is determined according to the road section code corresponding to the vehicle point data.

[0045] The daily flow table of the road section can be calculated first, and then the daily table data can be merged and accumulated to obtain the monthly table data.

[0046] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can reasonably splice the road sections according to the road section codes and the first and last coordinates of the road network data, obtain the road network data after the road sections are spliced, and then match the vehicle point data with the road network data after the road sections are spliced, so as to realize the analysis of the vehicle flow distribution of the road sections and improve the efficiency of statistical analysis of the road sections.

[0047] In some embodiments, the step of obtaining the road network data after the road segments are spliced ​​based on the road segment codes and the first and last coordinates of the road network data includes: Obtaining the section coordinates and section codes in the road network data, wherein the section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; When the end coordinates of the first road network data are not repeated and the length of the first road network data is less than the first threshold, the end coordinates of the first road network data are used as the start coordinates of the second road network data; The first road network data and the second road network data are concatenated.

[0048] Specifically, Figure 2 is a schematic diagram of the process of splicing road sections provided by the present invention, such as Figure 2 As shown, first generate all the road network data of about 1.56 million, obtain the road section coordinates and road section codes in the road network data. Then loop all the road network data, and when the end coordinates of the first road network data are not repeated and the length of the first road network data is less than the first threshold, query the beginning coordinates in the road network data set to find the second road network data whose beginning coordinates are equal to the end coordinates of the first road network data.

[0049] Furthermore, when there are multiple equalities (more than one) between the end coordinates of the first road network data and the end coordinates of all road network data, no calculation is performed. The first threshold is 3000 meters, 4000 meters or 5000 meters. The specific parameters can be adjusted according to actual needs. The larger the first threshold, the less road network data will be obtained by the final splicing, and vice versa.

[0050] Furthermore, the road section coordinates can be taken to two decimal places, and can also be adjusted according to actual needs.

[0051] When there is only one second road network data that meets the conditions, calculate the distance from the starting coordinates of the first road network data to the ending coordinates of the second road network data. When the distance is less than the first threshold, concatenate the second road network data and the first road network data, and continue to query the starting coordinates in the network data set based on the ending coordinates of the second road network data.

[0052] Furthermore, if there are multiple second road network data that meet the conditions, it is determined whether the end coordinates of the first road network data and the beginning coordinates of the second road network data are completely equal. If there are more than one, the process ends. If there is only one, the end coordinates of the road network data are obtained, and the distance from the beginning coordinates of the first road network data to the end coordinates of the second road network data is calculated. If the distance is less than the first threshold, the second road network data and the first road network data are spliced, and the beginning coordinates in the road network data set are continued to be queried according to the end coordinates of the second road network data.

[0053] The vehicle flow statistics method based on traffic big data analysis provided by the present invention reduces the amount of calculation of road section analysis data by reasonably splicing road sections, and realizes rapid calculation and processing of data while ensuring analysis accuracy.

[0054] In some embodiments, the method further comprises: In the case where the same road segment codes exist, the road network data after the road segments are spliced ​​is corrected based on the road segment coordinates.

[0055] Specifically, after obtaining all the data, if it is found that there are road section codes that are the same, manual correction is performed, mainly due to the following situations: The one with the same code and exactly the same road section coordinates is retained.

[0056] The encoding is the same, the road segment coordinates contain one another, and the longer one is retained.

[0057] The codes are the same, but the coordinates of the sections are completely different. Depending on the length of the section, one is retained. It can be judged by humans. For example, if some records have only two coordinates and the other record is very long, the shorter one is deleted. For example, if some section names are garbled, the section names must be corrected.

[0058] The codes are the same but the road section coordinates are completely different. The codes are modified. For example, if the original codes are 1, 2, and 3 and there is more data, one record each for 1, 2, and 3 is retained. The remaining codes are made larger and are retained by adding one in sequence.

[0059] The vehicle flow statistics method based on traffic big data analysis provided by the present invention reduces the amount of calculation of road section analysis data by reasonably splicing road sections, and realizes rapid calculation and processing of data while ensuring analysis accuracy.

[0060] In some embodiments, determining the vehicle location data and the vehicle category includes: Determine vehicle type and fuel type; Corresponding vehicle location data is acquired based on the vehicle type and the fuel type.

[0061] Specifically, Figure 3 It is a schematic diagram of the flow chart of road section traffic flow statistics provided by the present invention, such as Figure 3 As shown, the vehicle category to be counted, that is, the vehicle range to be counted, can be determined by screening the vehicle type and the fuel type.

[0062] For example, you can filter out vehicles whose vehicle type is "medium and large trucks" and whose fuel types are "gasoline", "diesel", "electricity", "natural gas" and "hydrogen".

[0063] Then, the vehicle point data of the vehicle to be counted is determined according to the filtered vehicle information.

[0064] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can realize the analysis of the vehicle flow distribution of the road section by matching the original vehicle point data with the road section data, thereby supporting the business to find the main roads with high traffic flow and helping the site selection for site construction.

[0065] In some embodiments, the determining the road segment code corresponding to the vehicle point data based on the road network data after the road segment is spliced ​​and the vehicle point data includes: Based on the coordinate system conversion, the road network data after the road sections are spliced ​​and the vehicle point data are converted to the same coordinate system, and the road section code corresponding to the vehicle point data is determined.

[0066] Specifically, the road network data is initialized first (the road network coordinates are based on the coordinate system of the National Surveying and Mapping Bureau). The road network data of expressways and national highways are selected, and the road network data sections are composed of fragments.

[0067] Then compare the vehicle point data with the road network data. The vehicle point coordinates are converted into the coordinates of the National Surveying and Mapping Bureau coordinate system. The coordinates use the corresponding formula to obtain the geographic coordinates. The road network data is projected using the Mercator projection transformation. After the vehicle point data is projected, the RD-tree algorithm is used to query the road network segment number corresponding to the vehicle point data, that is, the road section code.

[0068] Finally, the road traffic is deduplicated and stored in the database. The number of vehicles on the road section can be calculated by road section number, vehicle type, fuel type, and enterprise ID, and after deduplication in the same road section ID according to the unique vehicle ID, vehicle type, and fuel type, it is written into the road section traffic daily table. The monthly table data is calculated by merging and accumulating the daily table data.

[0069] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can realize the analysis of the vehicle flow distribution of the road section by matching the original vehicle point data with the road section data, thereby supporting the business to find the main roads with high traffic flow and helping the site selection for site construction.

[0070] In some embodiments, the determining of the vehicle flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: The station inbound vehicle flow data and the station through-station vehicle flow data are determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0071] Specifically, the traffic volume at the station can also be determined based on the road section code and vehicle category corresponding to the vehicle point data. The traffic volume calculation at the station is divided into the traffic volume calculation at the station and the traffic volume calculation at the station. The traffic volume calculation at the station mainly calculates the traffic volume entering a station. The traffic volume calculation at the station is used to calculate the traffic volume passing through a station.

[0072] Figure 4 It is a flow chart of the station traffic flow measurement provided by the present invention, such as Figure 4 As shown, first, as in the previous article, the vehicles to be counted are screened out to obtain vehicle point data, and then the vehicle point data are matched with the station data to obtain the inbound vehicle flow data and the station passing vehicle flow data.

[0073] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can realize the vehicle flow analysis of the site by matching the vehicle point data with the site data, thereby supporting the work of site refueling situation analysis, site potential entry rate analysis, surrounding competitor analysis, etc.

[0074] In some embodiments, determining the station entry traffic flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold, determining the station entry information data of the vehicle; Determine the station entry traffic data based on the entry information data.

[0075] Specifically, batch processing of point information is performed daily, and the distance (straight-line distance) between the vehicle point coordinates and the selected site coordinates is calculated to obtain the minimum distance between the points. If the minimum value is less than or equal to the second threshold, such as 100 meters, the point is successfully matched with the site, and the site ID is added to the vehicle point information.

[0076] After all vehicle points have completed the matching judgment with the station, they are grouped by license plate_license plate color code. Within the same vehicle group, the vehicle points are sorted in chronological order. The matching of vehicles and stations is calculated based on continuous points. For continuous matching to the same station, and the duration exceeds or equals the third threshold, for example, 5 minutes, an entry information data (including station ID, start entry time, entry duration) will be recorded; after the vehicle and station information are matched, for the data with entry information, the data is deduplicated by station, vehicle type, fuel type, vehicle ID, and enterprise ID.

[0077] Finally, based on the obtained station traffic flow data, the number of vehicles is calculated by station, vehicle type, fuel type, and enterprise ID, and written into the station traffic daily table. The monthly table data is calculated by merging and accumulating the daily table data.

[0078] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can realize the vehicle flow analysis of the site by matching the vehicle point data with the site data, thereby supporting the work of site refueling situation analysis, site potential entry rate analysis, surrounding competitor analysis, etc.

[0079] In some embodiments, the determining of the station transit traffic data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a fourth threshold, determining the station passing information data of the vehicle; Based on the vehicle category, the data that is repeated with the station entry information data is removed from the station passing information data; Determine the station transit traffic data based on the deduplicated transit information data.

[0080] Specifically, batch processing point information is performed on a daily basis, and the distance (straight-line distance) between the vehicle point coordinates and the selected site coordinates is calculated to obtain the minimum distance between the points. If the calculated vehicle position and site position are less than or equal to a fourth threshold, such as 500 meters, the vehicle point is successfully matched with the site, and the site ID is added to the vehicle point information.

[0081] After all vehicle points have been matched with the stations, they are grouped by license plate_license plate color code. Within the same vehicle group, the vehicle points are sorted in chronological order. After the vehicle and station information are matched, for data with entry information, the data is deduplicated by station, vehicle type, fuel type, vehicle ID, and company ID.

[0082] Finally, based on the obtained station traffic flow data, the number of vehicles is calculated by station, vehicle type, fuel type, and enterprise ID, and written into the station traffic daily table. The monthly table data is calculated by merging and accumulating the daily table data.

[0083] It is worth mentioning that the fourth threshold is greater than the second threshold.

[0084] The vehicle flow statistics method based on traffic big data analysis provided by the present invention can realize the vehicle flow analysis of the site by matching the vehicle point data with the site data, thereby supporting the work of site refueling situation analysis, site potential entry rate analysis, surrounding competitor analysis, etc.

[0085] Figure 5 is a structural schematic diagram of a vehicle flow statistics device based on traffic big data analysis provided by the present invention, such as Figure 5As shown, the vehicle flow statistics device based on traffic big data analysis provided by the present invention includes a first acquisition module 501, a first determination module 502, a second determination module 503 and a third determination module 504, wherein: The first acquisition module 501 is used to acquire the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; The first determination module 502 is used to determine the vehicle location data and the vehicle category; A second determination module 503 is used to determine the road section code corresponding to the vehicle point data based on the road network data after the road section is spliced ​​and the vehicle point data; The third determination module 504 is used to determine the vehicle flow data based on the road segment code corresponding to the vehicle point data and the vehicle category.

[0086] In some embodiments, the first acquisition module includes: A first acquisition submodule is used to acquire the road section coordinates and road section codes in the road network data, wherein the road section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; A first determination submodule, configured to use the end coordinates of the first road network data as the start coordinates of the second road network data when the end coordinates of the first road network data are not repeated and the length of the first road network data is less than a first threshold; The first splicing submodule is used to splice the first road network data and the second road network data.

[0087] In some embodiments, the first acquisition module further includes: The first processing submodule is used to correct the road network data after the road sections are spliced ​​based on the road section coordinates when there are identical road section codes.

[0088] In some embodiments, the first determining module includes: A second determination submodule, for determining a vehicle type and a fuel type; The second acquisition submodule is used to acquire corresponding vehicle point data based on the vehicle type and the fuel type.

[0089] In some embodiments, the second determining module includes: The third determination submodule is used to convert the road network data after the road sections are spliced ​​and the vehicle point data into the same coordinate system based on the coordinate system conversion, and determine the road section code corresponding to the vehicle point data.

[0090] In some embodiments, the third determination module includes: The fourth determination submodule is used to determine the station entry vehicle flow data and the station transit vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category.

[0091] In some embodiments, the fourth determining submodule includes: A first determining unit, configured to determine a distance between the vehicle and the site based on the vehicle position data and the site data; A second determining unit, configured to determine the station entry information data of the vehicle when the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold; The third determining unit is used to determine the station entry vehicle flow data based on the station entry information data.

[0092] In some embodiments, the fourth determining submodule includes: A fourth determining unit, configured to determine a distance between the vehicle and the site based on the vehicle position data and the site data; A fifth determining unit, configured to determine the station passing information data of the vehicle when the distance between the vehicle and the station is not greater than a fourth threshold; A first processing unit, configured to remove data in the stop-by information data that is duplicated with the stop-by information data based on the vehicle category; The sixth determination unit is used to determine the station transit traffic flow data based on the deduplicated transit information data.

[0093] Specifically, the vehicle flow statistics device based on traffic big data analysis provided by the present invention can implement all the method steps implemented by the above-mentioned vehicle flow statistics method embodiment based on traffic big data analysis, and can achieve the same technical effect. The parts and beneficial effects that are the same as the method embodiment in this embodiment will not be described in detail here.

[0094] Figure 6 is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as Figure 6 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communications interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute a vehicle flow statistics method based on traffic big data analysis, the method comprising: Obtain the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; Determine vehicle location data and vehicle category; Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data; The vehicle flow data is determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0095] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0096] In some embodiments, the step of obtaining the road network data after the road segments are spliced ​​based on the road segment codes and the first and last coordinates of the road network data includes: Obtaining the section coordinates and section codes in the road network data, wherein the section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; When the end coordinates of the first road network data are not repeated and the length of the first road network data is less than the first threshold, the end coordinates of the first road network data are used as the start coordinates of the second road network data; The first road network data and the second road network data are concatenated.

[0097] In some embodiments, the method further comprises: In the case where the same road segment codes exist, the road network data after the road segments are spliced ​​is corrected based on the road segment coordinates.

[0098] In some embodiments, determining the vehicle location data and the vehicle category includes: Determine vehicle type and fuel type; Corresponding vehicle location data is acquired based on the vehicle type and the fuel type.

[0099] In some embodiments, the determining the road segment code corresponding to the vehicle point data based on the road network data after the road segment is spliced ​​and the vehicle point data includes: Based on the coordinate system conversion, the road network data after the road sections are spliced ​​and the vehicle point data are converted to the same coordinate system, and the road section code corresponding to the vehicle point data is determined.

[0100] In some embodiments, the determining of the vehicle flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: The station inbound vehicle flow data and the station through-station vehicle flow data are determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0101] In some embodiments, determining the station entry traffic flow data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold, determining the station entry information data of the vehicle; Determine the station entry traffic data based on the entry information data.

[0102] In some embodiments, the determining of the station transit traffic data based on the road segment code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a fourth threshold, determining the station passing information data of the vehicle; Based on the vehicle category, the data that is repeated with the station entry information data is removed from the station passing information data; Determine the station transit traffic data based on the deduplicated transit information data.

[0103] Specifically, the above-mentioned electronic device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution subject is the electronic device, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0104] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the vehicle flow statistics method based on traffic big data analysis provided by the above methods, the method includes: Obtain the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; Determine vehicle location data and vehicle category; Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data; The vehicle flow data is determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0105] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to execute the vehicle flow statistics method based on traffic big data analysis provided by the above methods, the method comprising: Obtain the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; Determine vehicle location data and vehicle category; Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data; The vehicle flow data is determined based on the road section code corresponding to the vehicle point data and the vehicle category.

[0106] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0108] It should also be noted that the terms "first", "second", etc. in the embodiments of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0109] In the embodiments of the present application, the term "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0110] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0111] In this application, "determine B based on A" means that the factor A should be considered when determining B. It is not limited to "B can be determined based on A alone", but should also include: "determine B based on A and C", "determine B based on A, C and E", "determine C based on A, and further determine B based on C", etc. In addition, it can also include taking A as a condition for determining B, for example, "when A meets the first condition, use the first method to determine B"; for example, "when A meets the second condition, determine B", etc.; for example, "when A meets the third condition, determine B based on the first parameter", etc. Of course, it can also be a condition that takes A as a factor for determining B, for example, "when A meets the first condition, use the first method to determine C, and further determine B based on C", etc.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle flow statistics method based on traffic big data analysis, characterized in that: include: Obtain the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; Determine vehicle location data and vehicle category; Determine the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data; The vehicle flow data is determined based on the road section code corresponding to the vehicle point data and the vehicle category.

2. The vehicle flow statistics method based on traffic big data analysis according to claim 1 is characterized in that: The method of obtaining the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data includes: Obtaining the section coordinates and section codes in the road network data, wherein the section coordinates include the beginning coordinates of each section of the road network data and the ending coordinates of each section of the road network data; When the end coordinates of the first road network data are not repeated and the length of the first road network data is less than the first threshold, the end coordinates of the first road network data are used as the start coordinates of the second road network data; The first road network data and the second road network data are concatenated.

3. The vehicle flow statistics method based on traffic big data analysis according to claim 2 is characterized in that: The method further comprises: In the case where the same road segment codes exist, the road network data after the road segments are spliced ​​is corrected based on the road segment coordinates.

4. The vehicle flow statistics method based on traffic big data analysis according to claim 1 is characterized in that: The determining of the vehicle location data and the vehicle category includes: Determine vehicle type and fuel type; Corresponding vehicle location data is acquired based on the vehicle type and the fuel type.

5. The vehicle flow statistics method based on traffic big data analysis according to claim 1 is characterized in that: The determining the road section code corresponding to the vehicle point data based on the road network data after the road sections are spliced ​​and the vehicle point data comprises: Based on the coordinate system conversion, the road network data after the road sections are spliced ​​and the vehicle point data are converted to the same coordinate system, and the road section code corresponding to the vehicle point data is determined.

6. The vehicle flow statistics method based on traffic big data analysis according to claim 1 is characterized in that: The determining of the vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category includes: The station inbound vehicle flow data and the station through-station vehicle flow data are determined based on the road section code corresponding to the vehicle point data and the vehicle category.

7. The vehicle flow statistics method based on traffic big data analysis according to claim 6 is characterized in that: The determining of the station inbound vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a second threshold and the duration is greater than a third threshold, determining the station entry information data of the vehicle; Determine the station entry traffic data based on the entry information data.

8. The vehicle flow statistics method based on traffic big data analysis according to claim 6 is characterized in that: The determining of the station passing vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category includes: Determine the distance between the vehicle and the site based on the vehicle point data and the site data; When the distance between the vehicle and the station is not greater than a fourth threshold, determining the station passing information data of the vehicle; Based on the vehicle category, the data that is repeated with the station entry information data is removed from the station passing information data; Determine the station transit traffic data based on the deduplicated transit information data.

9. A vehicle flow statistics device based on traffic big data analysis, characterized in that: include: A first acquisition module is used to acquire the road network data after the road sections are spliced ​​based on the road section codes and the first and last coordinates of the road network data; A first determination module is used to determine vehicle location data and vehicle category; A second determination module is used to determine the road section code corresponding to the vehicle point data based on the road network data after the road section is spliced ​​and the vehicle point data; The third determination module is used to determine the vehicle flow data based on the road section code corresponding to the vehicle point data and the vehicle category.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the vehicle flow statistics method based on traffic big data analysis as described in any one of claims 1 to 8 is implemented.

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