A method, device, equipment and medium for calculating freight transport volume on ordinary roads

Abstract

The present invention relates to the technical field of highway freight statistics, and discloses a method, device, equipment, and medium for calculating the freight transport volume on ordinary highways. The method comprises: pre-processing the basic information database of commercial freight vehicles to obtain a pre-processed basic information database; determining the final transport trips based on satellite trajectory point data; determining the ordinary highway trips in the final transport trips based on vector map data of the highway network; correcting the overweight truck data to obtain corrected overweight truck data; calculating the average freight weight on ordinary highways for each commercial freight vehicle based on the corrected overweight truck data, the pre-processed basic information database, and the final transport trips; and calculating the total freight transport volume on ordinary highways based on the ordinary highway trips, the total mileage of the ordinary highway trips, and the average freight weight on ordinary highways. This application significantly improves the accuracy of the data by integrating multi-source big data, while ensuring the standardization and scientific nature of the statistical process.

Description

Technical Field

[0001] The present invention relates to the technical field of highway freight statistics, and in particular to a method, device, equipment and medium for calculating the freight transport volume of ordinary highways. Background Art

[0002] Highway freight transport volume includes two indicators: freight volume and freight turnover. It is a key indicator to characterize the service results of the highway freight transport industry, the core statistical content in the statistical work of transportation authorities at all levels, and an important reference indicator for the calculation of road transport added value in the calculation of regional GDP.

[0003] The current statistical method for ordinary highway freight transport volume adopts a combination of "comprehensive statistics of above-scale enterprises + fluctuation estimation of below-scale businesses". However, the data information used in this method is not authentic and representative, long-term estimation may lead to deviations, and the statistical frequency is low, which makes it difficult to meet the needs of daily management and decision-making. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, device, equipment and medium for calculating the freight transport volume of ordinary roads.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present disclosure provides a method for calculating the freight transport volume of ordinary roads, the method comprising:

[0007] Obtaining a basic information database of commercial freight vehicles in the measurement area, and preprocessing the basic information database to obtain a preprocessed basic information database;

[0008] Obtain satellite trajectory point data of commercial freight vehicles in the measurement area during the measurement time period, and determine the final transportation trips based on the satellite trajectory point data;

[0009] Obtain vector map data of the highway network in the measurement area, and determine the number of ordinary highway trips in the final transportation trips based on the vector map data;

[0010] Obtain overweight truck data of commercial freight vehicles collected by off-site overweight truck enforcement equipment in the measurement area during the measurement period, and correct the overweight truck data to obtain corrected overweight truck data;

[0011] Match the corrected overweight truck data with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle. Based on the hierarchical information and the final transport trips, calculate the average freight weight of each commercial freight vehicle on ordinary roads.

[0012] Obtain the total mileage of ordinary highway trips, and calculate the total freight volume of ordinary highways based on the number of ordinary highway trips, the total mileage of ordinary highway trips, and the average freight weight of ordinary highways.

[0013] Optionally, the basic information database includes multiple vehicle records, each vehicle record includes at least one of a license plate number, a license plate color, a vehicle type, a gross weight, a curb weight, and an annual inspection time. The basic information database is preprocessed to obtain a preprocessed basic information database, including:

[0014] Delete vehicle records whose annual inspection time is greater than the preset age threshold according to the annual inspection time, delete vehicle records whose vehicle type is trailer, delete vehicle records with missing curb weight or vehicle records outside the preset weight range, and supplement vehicle records with missing curb weight to obtain a cleaned basic information database;

[0015] Each vehicle record in the cleaned basic information database is divided into a plurality of basic layers according to the vehicle type and the total mass, thereby obtaining a pre-processed basic information database.

[0016] Optionally, the satellite trajectory point data includes at least one of time information, longitude and latitude information, speed information, and direction information. Determining the final transport trip based on the satellite trajectory point data includes:

[0017] The stop points of commercial freight vehicles are identified based on the time information and speed information of each satellite trajectory point data, and the basic trip frequency of commercial freight vehicles is determined according to the stay time at the stop points;

[0018] Traverse the basic trips, merge the non-transportation loading and unloading trips, and obtain the initial transportation trips of commercial freight vehicles;

[0019] Determine the long-distance transport trips in the initial transport trips, traverse the long-distance transport trips, merge the non-long-distance transport loading and unloading trips according to the time information and direction information, and obtain the final transport trips.

[0020] Optionally, determining the number of ordinary road trips in the final transport trips based on the vector map data includes:

[0021] Matching the information of each trip in the final transport trip with the vector map data to determine whether the vehicle trajectory of each trip information matches the highway;

[0022] The trip information without vehicle trajectory matching on the highway is determined as ordinary road trip information, and the ordinary road trip information of each layer is summarized according to the basic layer.

[0023] Optionally, the overweight truck data includes at least one of license plate number, license plate color, number of axles, and weighing data. Correcting the overweight truck data to obtain corrected overweight truck data includes:

[0024] Match the overweight truck data with the pre-processed basic information database according to the license plate number and license plate color, and filter out the traction vehicles among the commercial freight vehicles;

[0025] Determining the no-load weighing value and the loaded weighing value of the tractor vehicle respectively according to the frequency distribution of the weighing data of the tractor vehicle;

[0026] Calculate the deadweight and fully loaded weight of the towing vehicle based on the pre-processed basic information database;

[0027] The linear correction method is used to correct the data of overloaded trucks according to the deadweight, full-load weighing value, empty-load weighing value and actual-load weighing value of the towing vehicle to obtain the corrected data of overloaded trucks.

[0028] Optionally, the corrected overweight truck data is matched with the pre-processed basic information database to obtain hierarchical information of each commercial freight vehicle, and the average freight weight of each commercial freight vehicle on ordinary roads is calculated based on the hierarchical information and the final transport trips, including:

[0029] Layering the ordinary highway trips according to the total mileage to obtain multiple first distance layers, cross-grouping each first distance layer with each basic layer to obtain multiple first subgroups, and calculating the number of trips in each first subgroup;

[0030] The difference between the weighing data and the curb weight of each commercial freight vehicle is used as the freight volume of the corresponding commercial freight vehicle;

[0031] Match the corrected overweight truck data with the pre-processed basic information database according to license plate number and color. Layer the successfully matched trips according to total mileage to obtain multiple second distance layers. Cross-group each second distance layer with each basic layer to obtain multiple second sub-groups, and calculate the average trip weight of each second sub-group.

[0032] The average cargo weight of each commercial freight vehicle on ordinary roads is calculated based on the number of trips of each first subgroup and the average weighing value of the trips of each second subgroup.

[0033] Optionally, the total freight volume on ordinary roads is calculated based on the number of ordinary road trips, the total mileage of ordinary road trips, and the average freight weight on ordinary roads, including:

[0034] Calculate the total freight volume on ordinary roads based on the number of trips and average freight weight on ordinary roads;

[0035] The total turnover of ordinary roads is calculated based on the total mileage of ordinary road trips and the average cargo weight of ordinary roads.

[0036] In a second aspect, an embodiment of the present disclosure provides a device for calculating the freight transport volume on ordinary roads, the device comprising:

[0037] A preprocessing module is used to obtain a basic information database of commercial freight vehicles in the measurement area and preprocess the basic information database to obtain a preprocessed basic information database;

[0038] The first determination module is used to obtain satellite trajectory point data of commercial freight vehicles in the measurement area within the measurement time period, and determine the final transportation trip according to the satellite trajectory point data;

[0039] The second determination module is used to obtain vector map data of the highway network in the measurement area and determine the number of ordinary highway trips in the final transportation trips based on the vector map data;

[0040] The correction module is used to obtain the overweight truck data of commercial freight vehicles collected by non-site overweight truck enforcement equipment in the measurement area during the measurement period, and to correct the overweight truck data to obtain the corrected overweight truck data;

[0041] The first calculation module is used to match the corrected overweight truck data with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle, and calculate the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trip number;

[0042] The second calculation module is used to obtain the total mileage of ordinary highway trips, and calculate the total cargo transportation volume of ordinary highways based on the number of ordinary highway trips, the total mileage of ordinary highway trips and the average cargo weight of ordinary highways.

[0043] In a third aspect, a computer device is provided in an embodiment of the present disclosure. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the ordinary highway freight transport volume calculation method in the first aspect are implemented.

[0044] In a fourth aspect, a computer-readable storage medium is provided in an embodiment of the present disclosure. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ordinary highway freight transport volume calculation method in the first aspect are implemented.

[0045] Beneficial effects of this application:

[0046] The embodiment of the present application provides a method for calculating the freight transport volume on ordinary roads, which includes: obtaining a basic information database of commercial freight vehicles in a measurement area, and preprocessing the basic information database to obtain a preprocessed basic information database; obtaining satellite trajectory point data of commercial freight vehicles in the measurement area within a measurement time period, and determining the final transport trips based on the satellite trajectory point data; obtaining vector map data of the highway network in the measurement area, and determining the number of ordinary road trips in the final transport trips based on the vector map data; obtaining overweight truck data of commercial freight vehicles collected by non-site overweight truck control law enforcement equipment in the measurement area within the measurement time period, and correcting the overweight truck data to obtain corrected overweight truck data; matching the corrected overweight truck data with the preprocessed basic information database to obtain layered information of each commercial freight vehicle, and calculating the average freight weight of each commercial freight vehicle on ordinary roads based on the layered information and the final transport trips; obtaining the total mileage of ordinary road trips, and calculating the total freight transport volume of ordinary roads based on the ordinary road trips, the total mileage of ordinary road trips, and the average freight weight of ordinary roads. This application significantly improves the accuracy of data by integrating multi-source big data, while ensuring the standardization and scientific nature of the statistical process.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. Similar components are numbered similarly in the various drawings.

[0049] Figure 1 A flowchart of a method for calculating freight transport volume on ordinary roads provided in an embodiment of the present application is shown;

[0050] Figure 2 A schematic diagram of the frequency peak of weighing data provided by an embodiment of the present application is shown;

[0051] Figure 3 A schematic diagram of the structure of a device for calculating the freight transport volume on ordinary roads provided in an embodiment of the present application is shown;

[0052] Figure 4 A structural diagram of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0053] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0054] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. Conversely, when an element is referred to as being "directly on" another element, there is no intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0055] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0056] 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 the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in the template description herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0058] Example 1

[0059] like Figure 1 FIG. 1 is a flow chart of a method for calculating freight transport volume on ordinary roads in an embodiment of the present application. The method for calculating freight transport volume on ordinary roads provided in an embodiment of the present application includes the following steps:

[0060] Step S110 , obtaining a basic information database of commercial freight vehicles in the measurement area, and preprocessing the basic information database to obtain a preprocessed basic information database.

[0061] In this embodiment, a basic information database of commercial freight vehicles in the measurement area X is first obtained. The basic information database includes multiple vehicle records, each of which includes but is not limited to data such as the license plate number, license plate color, vehicle type, gross weight, curb weight, annual inspection time, and marked tonnage of the commercial freight vehicle.

[0062] Specifically, the basic information database needs to be cleaned. First, the vehicle records whose annual inspection time is greater than the preset age threshold (such as 2 years) are deleted according to the current annual inspection time, and then all vehicle records of trailer type are deleted. At the same time, the vehicle records with missing curb weight, curb weight of 0 and exceeding the upper limit of abnormal value (such as 40,000 kg) are kept as vehicle curb weight to be supplemented.

[0063] Next, a curb weight reference library for vehicle brands and models is constructed. First, vehicle records with complete curb weight records and a curb weight within a preset weight range (such as 0 to 40,000 kg) are screened out from the basic information database. Then, these vehicle records are aggregated according to the brand and model fields. During the aggregation process, vehicle records with a preset percentage of curb weight before and after (such as the first 5% and the last 5%) for each brand and model are first deleted, and then the remaining vehicle records for each brand and model are ensured to be no less than a preset number (such as 3). After that, the curb weight mode of the brand and model is calculated as the curb weight reference value of the brand and model. Finally, vehicle records with a curb weight less than a preset weight (such as 920 kg) in the calculation results are eliminated to form the final vehicle brand and model curb weight reference library.

[0064] Furthermore, the vehicle curb mass to be supplemented library is matched with the vehicle curb mass reference library according to the brand model, and vehicle records with missing curb mass, curb mass of 0 and exceeding the abnormal value upper limit (such as 40,000 kg) are supplemented.

[0065] For vehicle records that are still incomplete in the above steps, further methods are used to supplement them: (1) For records of non-towing vehicles with missing curb mass, first, the difference between the total mass and the marked tonnage of the vehicle record is used as the curb mass to supplement the database. Secondly, when the total mass record is incomplete, the marked tonnage is directly used as the curb mass to supplement; (2) For records of towing vehicles with missing curb mass, the mode of the curb mass of all towing vehicles is calculated using the existing complete records, and then this value is used as the curb mass of the remaining towing vehicles to supplement.

[0066] Furthermore, the curb mass of the towing vehicle is converted into the deadweight of the vehicle when towing the trailer as a whole. The calculation method is: filter the vehicle records whose vehicle type is trailer and whose curb mass is within a preset mass range (such as greater than 0 and less than 40,000 kg) in the original basic information database, calculate the average curb mass of all trailer vehicle records, and then convert the curb mass of each towing vehicle into the original curb mass + the average curb mass of the trailer as the final curb mass of the towing vehicle, and finally form a cleaned basic information database.

[0067] Preferably, each vehicle record in the cleaned basic information database is stratified and labeled based on vehicle type and gross mass. First, vehicle records with incomplete gross mass records require data supplementation. This supplementation is performed by first estimating the gross mass of vehicles of the same make and model. If this is still incomplete, the data is supplemented based on the gross mass of vehicles of the same length, width, and height. During stratification, vehicle type is categorized into six categories: tractors, special-structure vehicles, tank trucks, dump trucks, vans, and standard vehicles. Gross mass is divided into four levels: less than 12 tons, 12-20 tons, 20-30 tons, and greater than 30 tons. Then, the vehicle type and gross mass indicators are cross-stratified. Layers with fewer vehicle records are then merged with adjacent layers to form multiple basic layers, forming the preprocessed basic information database. In this example, 13 basic layers are ultimately formed, as shown in Table 1.

[0068] Table 1: Basic layer division

[0069]

[0070]

[0071] The above method improves the accuracy, completeness and classification clarity of the data by preprocessing the basic information database, while also improving the efficiency of measurement, providing a solid foundation for subsequent transportation volume measurement work.

[0072] Step S120: obtaining satellite trajectory point data of commercial freight vehicles in the measurement area within the measurement time period, and determining the final transport trips based on the satellite trajectory point data.

[0073] Specifically, satellite trajectory point data of commercial freight vehicles in the measurement area X within the measurement time period Y is obtained, including but not limited to time information, latitude and longitude information, speed information, direction information, etc., and duplicate or abnormal data is deleted.

[0074] Furthermore, based on the time information, the continuous satellite trajectory point data is used to identify the stop points in chronological order. That is, a preset vehicle speed threshold (such as 1m / s) is used to preliminarily determine whether the vehicle is in a stop state. When the vehicle speed is greater than a certain preset vehicle speed threshold, it is determined to be in a driving state; otherwise, it is determined to be in a stopped state, i.e., a stop point. The stop time at the stop point is then used to determine whether the corresponding transport trip is classified as a valid trip. That is, when the stop time at a stop point is less than a preset stop time threshold (such as 3 minutes), it is determined to be an invalid stop point; when the stop time at a stop point exceeds the preset stop time threshold, it is marked as the end of the transport trip. If the moving distance between the previous stop point and the next stop point is within the preset moving distance threshold (such as 50 meters), the trip between the two stop points is recorded as an invalid trip, and the stop time is recorded as the start of the previous stop point to the end of the next stop point. The basic trip times of all commercial freight vehicles are obtained according to the above rules.

[0075] Then, all basic trips are traversed and non-transportation and loading / unloading trips are merged to obtain the initial transport trips of commercial freight vehicles. The merging rule is as follows: different time thresholds are set according to the location of the stop. For example, if the stop is located on a highway, the previous and next trips are merged. If the stop is located elsewhere, the previous and next trips are merged if the stop time is less than the corresponding time threshold (such as 40 minutes).

[0076] Furthermore, it is necessary to screen the long-distance transport trips in the initial transport trips. First, the traction vehicles and vehicles with a total mass above the preset total mass threshold (such as 30 tons) are screened out according to the vehicle type, and then the number of districts and counties passed through in the measurement time period Y is summarized. The trips passing through the preset number of districts and counties (such as 10) or more are defined as long-distance transport trips.

[0077] Finally, the long-distance transport trips are traversed again, and the non-long-distance transport loading and unloading trips are merged. The merging judgment is based on the vehicle driving time period and the trip direction angle. The specific rule is that when driving at night (generally defined as the end time of the previous trip is between 9 pm and 3 am), if the parking time exceeds the first preset parking time upper limit threshold (such as 8 hours), the trips will not be merged; if the parking time is between the first preset parking time lower limit threshold and the first preset parking time upper limit threshold (such as 1 to 8 hours), and the difference between the direction angle of the next trip and the direction angle of the previous trip is within the first preset angle range (such as plus or minus 60 degrees), the previous and next trips of the stop point will be merged; if the parking time is less than the first preset parking time lower limit threshold (such as 1 hour), the previous and next trips of the stop point will be merged. When driving during the day (generally defined as the last trip ending between 3 a.m. and 9 p.m.), if the parking time exceeds the second preset parking time upper limit threshold (such as 4 hours), the trips will not be merged; if the parking time is between the second preset parking time lower limit threshold and the second preset parking time upper limit threshold (such as 1 to 4 hours), and the difference between the direction angle of the next trip and the direction angle of the previous trip is within the second preset angle range (such as plus or minus 60 degrees), the previous and next trips of the stop point will be merged; if the parking time is less than the second preset parking time lower limit threshold (such as 1 hour), the previous and next trips of the stop point will be merged. The final transport trips are formed according to the above rules, and the basic vehicle information, starting and ending time, starting and ending longitude and latitude, total mileage of each final transport trip, and the longitude and latitude information of all track points under the final transport trip are recorded.

[0078] The above method identifies the stop points and divides the trips of long-distance vehicles by combining the driving time period and direction angle factors to obtain the final transport trips, thereby improving the accuracy of long-distance transport trip identification and reducing redundant records in long-distance transport, thereby improving the accuracy of transport volume measurement.

[0079] Step S130: obtaining vector map data of the highway network in the measurement area, and determining the number of ordinary highway trips in the final transportation trips based on the vector map data.

[0080] Specifically, vector map data of the highway network in the measurement area X is obtained, and then each trip information of the final transportation trip in step S120 is matched with the vector map data. Based on the map matching method of the hidden Markov model, it is matched to determine whether the vehicle trajectory of each trip information passes through the highway.

[0081] If some trajectories in the trip information match on the highway, it is recorded as a highway trip. If no trajectory matches on the highway, it is recorded as an ordinary road trip. The number of ordinary road trips in each layer is summarized according to the basic layer.

[0082] The above method obtains more accurate transportation volume measurement results by accurately classifying transportation trips.

[0083] Step S140, obtaining the overweight truck data of commercial freight vehicles collected by non-site overweight truck enforcement equipment in the measurement area during the measurement time period, and correcting the overweight truck data to obtain corrected overweight truck data.

[0084] Specifically, obtain the overweight truck data of commercial freight vehicles collected by non-site overweight enforcement equipment in measurement area X during measurement time period Y, including but not limited to license plate number, license plate color, number of axles, weighing data, etc. Due to the inaccuracy of the weighing data collected by non-site overweight enforcement equipment, the weighing data of each station needs to be corrected. The correction method is as follows:

[0085] (1) First, the overweight truck data is matched with the pre-processed basic information database according to the license plate number and license plate color, and the towing vehicle is selected according to the matched vehicle type;

[0086] (2) Figure 2 As shown, the weighing data of the filtered tractor vehicles are then grouped according to the preset weight (such as 2 tons) as a group interval, and the frequency value of each group is calculated; then, the frequency value of each group of each site is traversed, and the peak with the highest frequency is first found, which is recorded as A. i , then at distance A i Find the second frequency peak within the range above the preset group interval (such as at least 5 group intervals), record it as B i ; Further compare the load values corresponding to these two peaks, the larger value is taken as the actual load peak, and the corresponding group record weighing average is the actual load weighing value a i The smaller value is taken as the no-load peak value, and the corresponding recorded weighing mean in the group is the no-load weighing value b i ;

[0087] (3) Based on the pre-processed basic information database, calculate the mode of the curb mass of the tractor vehicle, which is recorded as its own weight p, and calculate the mode of the curb mass of the tractor and trailer combination, which is recorded as the fully loaded weight q;

[0088] (4) Finally, the linear correction method is used to correct the weighing data of each station. The formula is as follows:

[0089] t kc =α k t km +β k

[0090] Where, t kcis the corrected weighing value of the kth station, t km is the weight value observed at the kth station, and the correction coefficient α of the kth station k and β k Calculated by the following formula:

[0091]

[0092] β k =p-α k *b k

[0093] The above formula is used to correct the weighing data observed at each station, and finally the corrected overweight truck data is obtained.

[0094] The above method accurately corrects the problem of inaccurate weighing data collected by off-site overweight control law enforcement equipment, ensuring that the weighing data of each transport trip is accurate and reliable, thereby improving the accuracy and reliability of the data.

[0095] In step S150, the corrected overweight truck data is matched with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle, and the average cargo weight of each commercial freight vehicle on ordinary roads is calculated based on the hierarchical information and the final transport trips.

[0096] As can be understood, the ordinary highway trips in step S130 are first stratified by total mileage to obtain multiple first distance layers. Exemplarily, the stratification method can be based on eight distance groups: 0-10 km, 10-20 km, 20-50 km, 50-100 km, 100-200 km, 200-500 km, 500-800 km, and 800 km and above. Each first distance layer is cross-grouped with each basic layer to form multiple first subgroups, which are incorporated into the overall set of trip divisions, and the number of trips in each first subgroup is calculated.

[0097] Furthermore, the corrected overweight truck data in step S140 is matched with the pre-processed basic information database according to the license plate number and license plate color, and the difference between the weighing data and the curb weight of each vehicle is used as the corresponding freight volume, and then the successfully matched trip samples are included in the calculation trip matching set C of the average freight weight.

[0098] The samples in set C are stratified according to the total mileage of each trip to obtain multiple second distance layers. For example, the stratification method can be based on eight distance groups: 0-10 km, 10-20 km, 20-50 km, 50-100 km, 100-200 km, 200-500 km, 500-800 km, and 800 km and above. Each second distance layer is cross-grouped with each basic layer to obtain multiple second subgroups, and the trip weight average of each second subgroup is calculated.

[0099] Since most of the overweight control stations are located on ordinary national and provincial highways, most of the observed transport is long-distance transport. Therefore, there is a certain difference between the trip distance distribution and the satellite positioning data. In addition, the actual load of vehicles under different transport distances is also different. Therefore, it is necessary to calculate the average cargo weight based on the overall trip set distribution. The specific method is to adjust the average trip weight of each second subgroup according to the number of trips in each first subgroup, and finally obtain the average cargo weight of ordinary roads for commercial freight vehicles of each basic layer. The adjustment formula is as follows:

[0100]

[0101] Where, is the average freight weight of commercial freight vehicles on ordinary roads at the i-th basic level, n ij is the number of passes in the total set of passes partitioned in the jth distance layer of the base layer of the i-th pass sample, is the weighted average of the trip matching set C in the jth distance layer of the basic layer of the i-th trip sample.

[0102] The above method ensures the accuracy and consistency of the data, and the calculated average cargo weight of each commercial freight vehicle on ordinary roads provides strong support for subsequent transportation volume calculations.

[0103] Step S160, obtaining the total mileage of ordinary highway trips, and calculating the total cargo transportation volume of ordinary highways based on the number of ordinary highway trips, the total mileage of ordinary highway trips and the average cargo weight of ordinary highways.

[0104] It is understandable that the total freight volume of ordinary roads includes two indicators: freight volume and turnover volume. The total freight volume of ordinary roads is obtained by multiplying the number of ordinary road trips in step S130 by the average freight weight of ordinary roads in step S150. The specific calculation formula is as follows:

[0105]

[0106] Where H is the total freight volume of ordinary roads, N i is the number of ordinary road trips.

[0107] Then, the total mileage of each ordinary highway trip is summed up and multiplied by the average freight weight of ordinary highways in each basic layer to obtain the total turnover of ordinary highways. The specific calculation formula is as follows:

[0108]

[0109] Where, L i is the total mileage of the ith basic layer of ordinary highway trips, l ik is the mileage of the kth trip on the i-th basic layer on the ordinary highway obtained by calculation in step S130, and Z is the total turnover of the ordinary highway.

[0110] The above method realizes comprehensive and dynamic monitoring of the freight industry by integrating multi-source big data. The entire measurement process is standardized and scientific, which enhances the multidimensionality of statistical results and significantly improves the accuracy of freight volume data. It overcomes the shortcomings of traditional statistical methods such as low precision, slow update frequency, and inability to comprehensively, dynamically and objectively reflect the changing trends and degree of the road freight industry, and provides reliable data support for transportation development and policy formulation.

[0111] The method for calculating the freight transport volume on ordinary roads provided in the embodiment of the present application obtains a basic information database of commercial freight vehicles in the measurement area and preprocesses the basic information database to obtain a preprocessed basic information database; obtains satellite trajectory point data of commercial freight vehicles in the measurement area within the measurement time period, and determines the final transport trips based on the satellite trajectory point data; obtains vector map data of the highway network in the measurement area, and determines the number of ordinary road trips in the final transport trips based on the vector map data; obtains overweight truck data of commercial freight vehicles collected by non-site overweight truck control law enforcement equipment in the measurement area within the measurement time period, and corrects the overweight truck data to obtain corrected overweight truck data; matches the corrected overweight truck data with the preprocessed basic information database to obtain layered information of each commercial freight vehicle, and calculates the average freight weight of each commercial freight vehicle on ordinary roads based on the layered information and the final transport trips; obtains the total mileage of ordinary road trips, and calculates the total freight transport volume on ordinary roads based on the ordinary road trips, the total mileage of ordinary road trips and the average freight weight on ordinary roads. This application significantly improves the accuracy of data by integrating multi-source big data, while ensuring the standardization and scientific nature of the statistical process.

[0112] Example 2

[0113] like Figure 3 FIG. 1 is a schematic diagram of a device 300 for calculating the volume of freight transported by ordinary roads according to an embodiment of the present application, and the device includes:

[0114] A preprocessing module 310 is used to obtain a basic information database of commercial freight vehicles in the measurement area and preprocess the basic information database to obtain a preprocessed basic information database;

[0115] The first determination module 320 is used to obtain satellite trajectory point data of commercial freight vehicles in the measurement area within the measurement time period, and determine the final transportation trip based on the satellite trajectory point data;

[0116] The second determining module 330 is used to obtain vector map data of the highway network in the measurement area and determine the number of ordinary highway trips in the final transportation trips based on the vector map data;

[0117] The correction module 340 is used to obtain overweight truck data of commercial freight vehicles collected by off-site overweight truck enforcement equipment in the measurement area during the measurement period, and correct the overweight truck data to obtain corrected overweight truck data;

[0118] The first calculation module 350 is used to match the corrected overweight truck data with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle, and calculate the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trip number;

[0119] The second calculation module 360 is used to obtain the total mileage of ordinary highway trips, and calculate the total cargo transportation volume of ordinary highways based on the number of ordinary highway trips, the total mileage of ordinary highway trips and the average cargo weight of ordinary highways.

[0120] The ordinary highway freight transport volume calculation device provided in the embodiment of the present application significantly improves the accuracy of data by integrating multi-source big data, while ensuring the standardization and scientific nature of the statistical process.

[0121] Example 3

[0122] The present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0123] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0124] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0125] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D slot compatibility test memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for the slot compatibility test method. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0126] In some embodiments, the processor 42 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other common highway freight volume measurement chip. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for executing the slot compatibility testing method.

[0127] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0128] The computer device provided in this embodiment can execute the above-mentioned method for calculating the freight transport volume of ordinary roads. The method for calculating the freight transport volume of ordinary roads here can be the method for calculating the freight transport volume of ordinary roads in each of the above-mentioned embodiments.

[0129] Example 4

[0130] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for calculating the ordinary highway freight transport volume in the embodiment are implemented.

[0131] In this embodiment, the computer-readable storage medium includes flash memory, hard disks, multimedia cards, card-type memories (e.g., SD or DX memories), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped with the computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Of course, the computer-readable storage medium may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are about to be output.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or flowchart, and the combination of boxes in the structure diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0133] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0134] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. 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 for enabling a computer device (which can be a smart phone, 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 can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and other media that can store program codes.

[0135] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for calculating the freight transport volume of ordinary roads, characterized in that: The method comprises: Obtaining a basic information database of commercial freight vehicles in the measurement area, and preprocessing the basic information database to obtain a preprocessed basic information database; Obtaining satellite trajectory point data of commercial freight vehicles in the measurement area within the measurement time period, and determining the final transportation trip number based on the satellite trajectory point data; Obtaining vector map data of the highway network in the measurement area, and determining the number of ordinary highway trips in the final transportation trips based on the vector map data; Obtaining overweight truck data of commercial freight vehicles collected by off-site overweight truck enforcement equipment in the measurement area during the measurement time period, and correcting the overweight truck data to obtain corrected overweight truck data; Matching the corrected overweight truck data with the pre-processed basic information database to obtain hierarchical information of each commercial freight vehicle, and calculating the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trip number; Obtaining the total mileage of the ordinary highway trips, and calculating the total freight transport volume of the ordinary highway based on the ordinary highway trips, the total mileage of the ordinary highway trips, and the average freight weight of the ordinary highway; The overweight truck data includes at least one of the license plate number, license plate color, number of axles, and weighing data. The correction of the overweight truck data to obtain the corrected overweight truck data includes: Matching the overweight truck data with the pre-processed basic information database according to the license plate number and license plate color to filter out the traction vehicles among the commercial freight vehicles; determining an empty weighing value and a loaded weighing value of the tractor vehicle according to a frequency distribution of the weighing data of the tractor vehicle; Calculating the deadweight and fully loaded weight of the towing vehicle according to the pre-processed basic information database; Using a linear correction method and based on the deadweight, full-load weighing value, empty-load weighing value, and actual-load weighing value of the towing vehicle, the data of the overweight truck is corrected to obtain the corrected overweight truck data; The process of matching the corrected overweight truck data with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle and calculating the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trips includes: Layering the ordinary highway trips according to total mileage to obtain multiple first distance layers, cross-grouping each of the first distance layers with each of the basic layers to obtain multiple first subgroups, and calculating the number of trips in each of the first subgroups, wherein each of the basic layers is obtained by dividing each vehicle record in the cleaned basic information database according to vehicle type and total mass; The difference between the weighing data and the curb mass of each commercial freight vehicle is used as the freight volume of the corresponding commercial freight vehicle; Matching the corrected overweight truck data with the pre-processed basic information database according to license plate number and license plate color, stratifying the successfully matched trips according to total mileage to obtain multiple second distance layers, cross-grouping each of the second distance layers with each of the basic layers to obtain multiple second subgroups, and calculating the trip weight average of each of the second subgroups; The average cargo weight of each commercial freight vehicle on ordinary roads is calculated based on the number of trips of each first subgroup and the average weighing value of the trips of each second subgroup.

2. The method for calculating the freight transport volume of ordinary roads according to claim 1, characterized in that: The basic information database includes a plurality of vehicle records, each of which includes at least one of a license plate number, a license plate color, a vehicle type, a gross weight, a curb weight, and an annual inspection time. The preprocessing of the basic information database to obtain a preprocessed basic information database includes: Delete vehicle records whose annual inspection time is greater than a preset age threshold according to the annual inspection time, delete vehicle records whose vehicle type is trailer, delete vehicle records with missing curb weight and vehicle records outside the preset weight range, and supplement vehicle records with missing curb weight to obtain a cleaned basic information database; Each vehicle record in the cleaned basic information database is divided into a plurality of basic layers according to vehicle type and total mass, thereby obtaining the preprocessed basic information database.

3. The method for calculating the freight transport volume of ordinary roads according to claim 1, characterized in that: The satellite trajectory point data includes at least one of time information, longitude and latitude information, speed information, and direction information. Determining the final transport trip according to the satellite trajectory point data includes: Identifying the stop points of the commercial freight vehicles based on the time information and speed information of the satellite trajectory point data to obtain the stop points of the commercial freight vehicles, and determining the basic trip frequency of the commercial freight vehicles based on the stay time at the stop points; Traversing the basic trips, merging the non-transportation loading and unloading trips, and obtaining the initial transport trips of the commercial freight vehicle; Determine the long-distance transport trips in the initial transport trips, traverse the long-distance transport trips, merge the non-long-distance transport loading and unloading trips according to the time information and the direction information, and obtain the final transport trips.

4. The method for calculating the freight transport volume of ordinary roads according to claim 2, characterized in that: The determining, according to the vector map data, the number of ordinary road trips in the final transportation trips includes: Matching the trip information of each of the final transport trips with the vector map data to determine whether the vehicle trajectory of each trip information matches the highway; The trip information without vehicle track matching on the expressway is determined as the ordinary highway trip information, and the ordinary highway trip information of each layer is summarized according to the basic layer.

5. The method for calculating the freight transport volume of ordinary roads according to claim 1, characterized in that: The calculation of the total freight transport volume of ordinary roads based on the number of ordinary road trips, the total mileage of the ordinary road trips and the average freight weight of the ordinary roads includes: Calculate the total freight volume of the ordinary highway based on the number of ordinary highway trips and the average freight weight of the ordinary highway; The total turnover of the ordinary highway is calculated based on the total mileage of the ordinary highway trips and the average cargo weight of the ordinary highway.

6. A device for calculating the freight transport volume on ordinary roads, characterized in that: The device comprises: A preprocessing module is used to obtain a basic information database of commercial freight vehicles in the measurement area and preprocess the basic information database to obtain a preprocessed basic information database; A first determination module is configured to obtain satellite trajectory point data of commercial freight vehicles in the measurement area within a measurement time period, and determine a final transport trip based on the satellite trajectory point data; A second determining module is configured to obtain vector map data of the highway network in the measurement area and determine the number of ordinary highway trips in the final transportation trips based on the vector map data; a correction module, configured to obtain overweight truck data of commercial freight vehicles collected by off-site overweight truck enforcement equipment in the measurement area during the measurement time period, and correct the overweight truck data to obtain corrected overweight truck data; A first calculation module is configured to match the corrected overweight truck data with the pre-processed basic information database to obtain hierarchical information of each commercial freight vehicle, and calculate the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trip number; a second calculation module, configured to obtain the total mileage of the ordinary highway trips, and calculate the total freight transport volume of the ordinary highway based on the ordinary highway trips, the total mileage of the ordinary highway trips, and the average freight weight of the ordinary highway; The overweight truck data includes at least one of the license plate number, license plate color, number of axles, and weighing data. The correction of the overweight truck data to obtain the corrected overweight truck data includes: Matching the overweight truck data with the pre-processed basic information database according to the license plate number and license plate color to filter out the traction vehicles among the commercial freight vehicles; determining an empty weighing value and a loaded weighing value of the tractor vehicle according to a frequency distribution of the weighing data of the tractor vehicle; Calculating the deadweight and fully loaded weight of the towing vehicle according to the pre-processed basic information database; Using a linear correction method and based on the deadweight, full-load weighing value, empty-load weighing value, and actual-load weighing value of the towing vehicle, the data of the overweight truck is corrected to obtain the corrected overweight truck data; The process of matching the corrected overweight truck data with the pre-processed basic information database to obtain the hierarchical information of each commercial freight vehicle and calculating the average freight weight of each commercial freight vehicle on ordinary roads based on the hierarchical information and the final transport trips includes: Layering the ordinary highway trips according to total mileage to obtain multiple first distance layers, cross-grouping each of the first distance layers with each of the basic layers to obtain multiple first subgroups, and calculating the number of trips in each of the first subgroups, wherein each of the basic layers is obtained by dividing each vehicle record in the cleaned basic information database according to vehicle type and total mass; The difference between the weighing data and the curb mass of each commercial freight vehicle is used as the freight volume of the corresponding commercial freight vehicle; Matching the corrected overweight truck data with the pre-processed basic information database according to license plate number and license plate color, stratifying the successfully matched trips according to total mileage to obtain multiple second distance layers, cross-grouping each of the second distance layers with each of the basic layers to obtain multiple second subgroups, and calculating the trip weight average of each of the second subgroups; The average cargo weight of each commercial freight vehicle on ordinary roads is calculated based on the number of trips of each first subgroup and the average weighing value of the trips of each second subgroup.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for calculating the ordinary highway freight transport volume according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ordinary highway freight transportation volume calculation method according to any one of claims 1 to 5 are implemented.

Images