Urban freight analysis method and related equipment based on multi-source data source fusion

Abstract

The present invention relates to the technical field of traffic analysis, and specifically discloses a method for analyzing urban freight transport by integrating multiple data sources and related equipment, wherein the method comprises the following steps: obtaining GPS information, checkpoint data information and map information about heavy trucks in a target urban area; generating multiple traffic zones according to preset division rules and map information; generating a first OD matrix about the traffic zones according to GPS information; obtaining information about the ratio of the number of local registered heavy trucks to the actual number of heavy trucks in the target urban area according to the checkpoint data information; expanding the first OD matrix according to the ratio information to obtain a second OD matrix; generating a heavy truck distribution map according to the second OD matrix; the method makes full use of the advantages of different data sources, makes up for the shortcomings of a single data source, realizes extrapolation from the local to the whole through the expansion of the OD matrix, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making.

Description

Technical Field

[0001] The present application relates to the technical field of traffic analysis, and in particular to a method for analyzing urban freight transport by integrating multiple data sources and related equipment. Background Art

[0002] With the booming development of globalization and e-commerce, the demand for freight continues to grow. The efficiency and safety of freight are directly related to the reduction of logistics costs and the optimization of the supply chain. Therefore, the analysis of freight vehicle traffic operation is particularly important.

[0003] However, the current data acquisition and analysis of heavy-duty freight vehicles faces many challenges. It is generally analyzed based on checkpoint data information or GPS data alone. In related technologies, checkpoint data information can only distinguish between large trucks and small trucks, but cannot distinguish heavy trucks within the large truck category. Heavy trucks are generally equipped with GPS systems, and the GPS data they provide mainly provides location and speed information, which cannot reflect the interaction between heavy trucks and the traffic environment.

[0004] There is currently no effective technical solution to the above problems. Summary of the invention

[0005] The purpose of this application is to provide a multi-source data source fusion urban freight analysis method and related equipment, so as to combine multiple data sources to infer the overall operation status of heavy trucks in different traffic areas, and provide a scientific basis for traffic management and decision-making.

[0006] In a first aspect, the present application provides a method for analyzing urban freight data by integrating multiple data sources, which is used to analyze freight data. The method comprises the following steps:

[0007] S1. Obtain GPS information, checkpoint data information and map information about heavy trucks in the target city;

[0008] S2. Generate multiple traffic zones according to preset division rules and map information;

[0009] S3, generating a first OD matrix about the traffic cell according to the GPS information;

[0010] S4. Obtaining information on the ratio of the number of local registered heavy trucks to the actual number of heavy trucks in the target city according to the checkpoint data information;

[0011] S5, expanding the first OD matrix according to the ratio information to obtain a second OD matrix;

[0012] S6. Generate a heavy truck distribution map according to the second OD matrix.

[0013] The multi-source data source fusion urban freight analysis method of the present application combines map information, GPS data and checkpoint data information, which can not only obtain the operation status of local registered heavy trucks in different traffic areas, but also infer the operation status of the overall heavy trucks in different traffic areas. It fully utilizes the advantages of different data sources and makes up for the shortcomings of a single data source. It realizes the extrapolation from local to overall through the OD matrix expansion sample, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making.

[0014] The urban freight analysis method integrating multiple data sources, wherein the heavy truck distribution map includes a heavy truck travel expected route map and / or a heavy truck travel density map.

[0015] These two chart forms can present the operating characteristics of heavy trucks more comprehensively and intuitively, meeting analysis needs at different levels.

[0016] The method for analyzing urban freight transport by integrating multiple data sources, wherein step S3 comprises:

[0017] S31, extracting parking data information and movement data information according to the change relationship of the GPS information with respect to time;

[0018] S32: Generate the first OD matrix according to the positional relationship between the starting point and the end point of the mobile data information and the traffic cell.

[0019] The technical solution of the present application performs time series analysis on GPS information, divides it into parking data and mobile data, and then uses the starting and ending point information of the mobile data and the positional relationship of the traffic cells to generate an OD matrix. It can effectively utilize the spatiotemporal information in the GPS data and accurately reflect the flow of heavy trucks between different traffic cells.

[0020] The method for analyzing urban freight traffic by integrating multiple data sources, wherein step S3 further includes the following steps performed after step S31:

[0021] S3A. Generate a parking distribution heat map of local heavy trucks of the target city based on the parking data information, and / or generate a transportation channel flow distribution map of local heavy trucks of the target city based on the mobile data information.

[0022] In the multi-source data source fusion urban freight analysis method, the step of generating a traffic distribution map of the local household registered heavy truck transportation channel of the target urban area according to the mobile data information comprises:

[0023] S3A1, extracting trajectory point information according to the movement data information;

[0024] S3A2, performing road network matching on the track point information to assign road segment IDs to all track point information;

[0025] S3A3. Assigning trajectory point information to the road network of the target city based on the road segment ID to generate a traffic distribution map of the local registered heavy truck transport channel.

[0026] The method for analyzing urban freight traffic by integrating multiple data sources, wherein step S32 comprises:

[0027] S321, when the starting point or the end point of the moving trajectory of the mobile data information is located in the target city, the geometric center of the traffic zone where the starting point or the end point is located is set as the O point or the D point matching the corresponding moving trajectory in the first OD matrix;

[0028] S322, when the starting point or the end point of the moving trajectory of the mobile data information is located on the boundary of the target urban area, according to the neighboring relationship of the traffic zones of the target urban area, an outer city traffic zone is set outside the target urban area, and the point O or the point D corresponding to the starting point or the end point located on the boundary of the target urban area is set as the geometric center of the corresponding outer city traffic zone;

[0029] S323: Construct the first OD matrix by comprehensively analyzing the relationship between the O points and the D points corresponding to all the movement tracks in the movement data information.

[0030] The method for analyzing urban freight traffic by integrating multiple data sources, wherein step S4 comprises:

[0031] S41, extracting the license plate type information of the large truck according to the checkpoint data information;

[0032] S42. According to the proportion of local registered license plates in the license plate type information, infer the ratio information of the number of heavy trucks with local registered residence and the actual number of heavy trucks in the target city.

[0033] In a second aspect, the present application further provides a multi-source data source fusion urban freight analysis device for analyzing freight data, the device comprising:

[0034] An acquisition module is used to obtain GPS information, checkpoint data information and map information about heavy trucks in the target city;

[0035] A partitioning module, used to generate multiple traffic zones according to preset partitioning rules and map information;

[0036] A matrix module, used for generating a first OD matrix about the traffic cell according to the GPS information;

[0037] A ratio module, used to obtain the ratio information of the number of local registered heavy trucks and the actual number of heavy trucks in the target city according to the checkpoint data information;

[0038] An expansion module, configured to expand the first OD matrix according to the ratio information to obtain a second OD matrix;

[0039] A generating module is used to generate a heavy truck distribution map according to the second OD matrix.

[0040] The multi-source data source fusion urban freight analysis device of the present application combines map information, GPS data and checkpoint data information, and can not only obtain the operation status of local registered heavy trucks in different traffic areas, but also infer the operation status of the entire heavy trucks in different traffic areas. It fully utilizes the advantages of different data sources and makes up for the shortcomings of a single data source. It realizes the extrapolation from the local to the whole through the OD matrix expansion sample, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making.

[0041] In a third aspect, the present application further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are executed.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0043] From the above, it can be seen that the present application provides a method for analyzing urban freight with the fusion of multiple sources of data and related equipment, wherein the method for analyzing urban freight with the fusion of multiple sources of data combines map information, GPS data and checkpoint data information, which can not only obtain the operation status of local registered heavy trucks in different traffic areas, but also infer the operation status of the overall heavy trucks in different traffic areas. It makes full use of the advantages of different data sources, makes up for the shortcomings of a single data source, realizes the inference from local to overall through the expansion of the OD matrix, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flowchart of a method for analyzing urban freight traffic by integrating multiple data sources provided in some embodiments of the present application.

[0045] Figure 2 A flowchart of a method for analyzing urban freight by fusing multiple data sources provided in some other embodiments of the present application.

[0046] Figure 3 A schematic diagram of the division of traffic zones in some embodiments of the present application.

[0047] Figure 4 Schematic diagram of the division of traffic zones in other embodiments of the present application.

[0048] Figure 5 A parking distribution heat map displayed on a traffic zone map.

[0049] Figure 6 A parking distribution heat map displayed on a real map.

[0050] Figure 7 This is the flow distribution diagram of the transportation channel.

[0051] Figure 8 This is a parking and channel flow distribution map.

[0052] Fig. 9 Desired route map for heavy goods vehicle travel.

[0053] Fig.10 This is a heavy truck travel density map.

[0054] Fig.11 A schematic diagram of the structure of a metropolitan freight analysis device that integrates multiple data sources provided in some embodiments of the present application.

[0055] Fig.12 A schematic diagram of the structure of a metropolitan freight analysis device that integrates multiple data sources provided in other embodiments of the present application.

[0056] Fig.13 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0057] Figure numerals: 201, acquisition module; 202, partition module; 203, matrix module; 204, ratio module; 205, sample expansion module; 206, generation module; 207, secondary acquisition module; 208, secondary matrix module; 209, secondary generation module; 210, tertiary generation module; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0059] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0060] Freight occupies a core position in the modern logistics system and has an important impact on economic development. With the development of globalization and e-commerce, the demand for freight continues to grow and has become a key force in promoting economic growth. However, the current acquisition and analysis of heavy-duty freight vehicle data faces many challenges. In the existing technology, the data information at the checkpoint can only distinguish between large trucks and small trucks, and cannot accurately identify heavy trucks; while GPS data mainly provides location and speed information, which cannot reflect the interaction between heavy trucks and the traffic environment. This makes it impossible to fully and accurately analyze the operation of urban truck traffic, affecting the scientificity and effectiveness of traffic management decisions.

[0061] Specifically, in a typical urban freight management system, the traffic management department needs to monitor and analyze the operation status of trucks in real time. The system collects vehicle passing information through checkpoint equipment, and some heavy trucks are equipped with GPS devices to provide location data. However, due to the limitations of the data source, the system cannot accurately distinguish heavy trucks, nor can it fully grasp their operation trajectory and distribution. For example, checkpoint data information may show that 100 large trucks pass through a certain section of road, but the specific number of heavy trucks cannot be determined; GPS data may show the location information of 50 heavy trucks, but this is only the data of some vehicles and cannot reflect the overall situation. This problem of incomplete and inaccurate data seriously affects the accuracy of traffic flow analysis, road network planning and management decisions.

[0062] Therefore, if the problem of multi-source data fusion analysis cannot be effectively solved, there will be serious deviations in the analysis of urban truck traffic operations. Specific consequences include: inaccurate traffic flow predictions, resulting in increased risk of traffic congestion; inefficient freight route planning, increased transportation costs; inability to accurately identify key regulatory areas, affecting traffic safety management; inaccurate assessment of emissions and energy consumption of freight vehicles, affecting the formulation of environmental protection policies. These problems not only affect the efficiency of traffic management, but may also lead to waste of resources and environmental problems. Therefore, there is an urgent need for a technical solution that can effectively fuse multi-source data and accurately analyze the operation of urban truck traffic to improve the accuracy and scientificity of traffic management.

[0063] First, please refer to Figure 1-Figure 10 Some embodiments of the present application provide a method for analyzing urban freight data by integrating multiple data sources, which is used to analyze freight data. The method includes the following steps:

[0064] S1. Obtain GPS information, checkpoint data information and map information about heavy trucks in the target city;

[0065] S2. Generate multiple traffic zones according to preset division rules and map information;

[0066] S3, generating a first OD matrix about the traffic cell according to the GPS information;

[0067] S4. Obtain information on the ratio of the number of local registered heavy trucks to the actual number of heavy trucks in the target city based on the checkpoint data information;

[0068] S5, expanding the first OD matrix according to the ratio information to obtain a second OD matrix;

[0069] S6. Generate a heavy truck distribution map according to the second OD matrix.

[0070] Specifically, GPS information refers to data provided by a satellite positioning device. According to relevant regulations, ordinary freight vehicles with a total mass of 12 tons or more must be equipped with a satellite positioning device and displayed normally on the networked joint control system. Therefore, step S1 can obtain GPS information about heavy trucks in the target city through the relevant departments of the target city, but since the relevant departments of the target city only store GPS information of vehicles with local household registration, the GPS information of heavy trucks obtained is actually GPS information of vehicles with local household registration, which may include data such as the location, time and license plate type information of the heavy trucks.

[0071] More specifically, checkpoint data information refers to vehicle passing information collected by monitoring equipment installed at key road nodes, which can be collected using video recognition or electronic tag reading equipment.

[0072] More specifically, in an embodiment of the present application, after the checkpoint data information is collected, it is preferably acquired by importing the csv file collected by the checkpoint device through python. The checkpoint data information may include: checkpoint device ID, checkpoint name, checkpoint address, road ID, section type, direction type, administrative division, checkpoint point type, checkpoint longitude, checkpoint latitude, checkpoint status, vehicle type, license plate number, license plate color, vehicle speed, vehicle passing timestamp and other data.

[0073] More specifically, map information includes data such as the terrain, buildings, and road networks of the target city, which can be obtained through the OpenStreetMap platform, the gotrackit library in Python, and the AutoNavi open platform API.

[0074] More specifically, the first OD matrix and the second OD matrix both belong to the OD matrix (Origin-Destination Matrix), which refers to a matrix in which all traffic partitions are sorted by rows (starting points) and columns (destination points), with the travel volume of residents or vehicles (OD volume) between any two partitions as elements. In this embodiment of the application, the OD matrix preferably adopts a rectangular matrix, which can be represented by a two-dimensional table or data structure, wherein each element of the OD pair represents the corresponding flow value from point O to point D, that is, the corresponding vehicle flow from point O to point D.

[0075] More specifically, sample expansion refers to the process of extrapolating overall data based on known sample data, wherein the proportion information used for sample expansion is related to the number of local registered heavy trucks and the actual number of heavy trucks, which can be a proportional relationship of the total amount or a proportion matrix paired with the first OD matrix.

[0076] It should be noted that the GPS information, checkpoint data information and map information obtained in step S1 all belong to the same time period. For example, all GPS information, checkpoint data information and corresponding map information from 0:00 to 24:00 on a certain day can be obtained, or data information within a certain hour to ensure the consistency of the data source.

[0077] More specifically, in step S2, the preset division rules may include division rules based on factors such as topography, land properties, etc., to ensure that the division results of the traffic district can reasonably reflect the traffic characteristics, so as to provide a spatial unit basis for subsequent analysis; among them, in the embodiment of the present application, it can be based on boundary conditions such as rivers, green spaces and roads, as well as factors such as resident travel survey units and population density to merge, organize and adjust the regions to obtain traffic districts; in some other implementation methods, the division of traffic districts can also be carried out in the same way as the division of towns, streets or communities.

[0078] More specifically, in step S3, based on the GPS information, the traffic zones to which the starting and ending points of the transportation tracks generated by local heavy trucks during each trip belong can be analyzed and obtained, so as to statistically obtain the truck traffic between different zones and form a preliminary OD matrix as the first OD matrix, which can reflect the distribution of transportation tracks of local heavy trucks in the target urban area during the corresponding period of time.

[0079] More specifically, step S4 can calculate the ratio of the number of local registered heavy trucks to the actual number of heavy trucks by identifying the license plate type in the checkpoint data information, or it can calculate the ratio of the number of local registered heavy trucks to the total number of heavy trucks in different transport trajectories based on the location relationship between the checkpoint data information and the traffic zone. This step provides key parameters for subsequent sample expansion.

[0080] More specifically, step S5 uses the proportion information to expand the first OD matrix to obtain the second OD matrix. The expansion process takes into account the ratio of local registered vehicles to non-local vehicles, making the analysis results more comprehensive and accurate. Finally, step S6 generates a heavy truck distribution map based on the second OD matrix, which can visualize the data analysis results, facilitate intuitive understanding and application, overcome the limitations of a single data source, and improve the comprehensiveness and accuracy of the analysis.

[0081] The urban freight analysis method with multi-source data source fusion in the embodiment of the present application combines map information, GPS data and checkpoint data information, which can not only obtain the operation status of local registered heavy trucks in different traffic areas, but also infer the operation status of the overall heavy trucks in different traffic areas. It fully utilizes the advantages of different data sources and makes up for the shortcomings of a single data source. It realizes the inference from local to overall through OD matrix expansion, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making.

[0082] In some preferred embodiments, Fig. 9 and Fig.10 As shown, the heavy truck distribution map includes a heavy truck travel expected route map and / or a heavy truck travel density map.

[0083] Specifically, the heavy truck distribution map is the final output result of the urban freight analysis method of the multi-source data source fusion of this application. It includes two specific forms of expression: the heavy truck travel expected route map and the heavy truck travel density map. The heavy truck travel expected route map can intuitively display the main driving routes of heavy trucks in the city, which helps to identify important freight channels and routes. The heavy truck travel density map can reflect the distribution density of heavy trucks in different regions and help identify hot spots where freight activities are concentrated. These two chart forms can present the operating characteristics of heavy trucks more comprehensively and intuitively, meeting the analysis needs at different levels.

[0084] More specifically, the process of generating the expected travel route map for heavy trucks may include the following steps: First, based on the starting point and end point information in the second OD matrix, the shortest path algorithm is used to calculate the optimal path between each pair of ODs. Then, the optimal paths of all OD pairs are superimposed on the map, and the thickness or color depth of the path can be used to indicate the size of the truck flow on the path. In this way, the main driving routes and traffic flow distribution of heavy trucks can be intuitively displayed. The process of generating the expected travel route map for heavy trucks may also include the following steps: First, based on the starting point and end point information in the second OD matrix, the corresponding traffic areas are determined, and then the straight lines between the traffic areas are used as the corresponding optimal paths. Then, the optimal paths of all OD pairs are superimposed on the map, and the thickness or color depth of the path can be used to indicate the size of the truck flow on the path. Both processing methods can intuitively display the main driving routes and traffic flow distribution of heavy trucks, among which the former focuses more on the selection of paths, and the latter focuses more on the flow relationship between traffic areas; preferably, in Fig. 9 In the expected travel route map for heavy trucks, the color of the straight line connecting the traffic zones changes gradually from yellow to red, which corresponds to the increasing relationship of the number of OD pairs, that is, the more OD pairs there are in the second OD matrix, the closer the color of the corresponding straight line is to red.

[0085] More specifically, the generation of a heavy truck travel density map may involve the following steps: First, the city is divided into small grid-like areas. Then, based on the data in the second OD matrix, the frequency of heavy trucks or their stay time in each small area is calculated. Finally, these data are visualized in the form of a heat map, where darker or warmer colors indicate a higher density of heavy trucks in the area. Fig.10 In the heavy truck travel density map, the gradient change of the thermal block color from yellow to red corresponds to the increasing relationship of the heavy truck density, that is, the more OD pairs in the second OD matrix involved in the traffic cell, the closer the color of the corresponding thermal block is to red.

[0086] More specifically, the combination of these two charts can provide more comprehensive information about the distribution of heavy trucks. For example, the expected travel route map may show that there are a large number of trucks flowing between two areas, while the travel density map may further reveal that these trucks have a high concentration in certain specific areas. This multi-dimensional information is of great reference value for traffic planning and management.

[0087] More specifically, the urban freight analysis method based on the fusion of multiple data sources in the embodiment of the present application has significant advantages over the traditional single data source analysis method. Traditional methods may only be able to provide traffic flow data at certain fixed points, or only provide a rough OD matrix, which is difficult to fully reflect the actual operating conditions of heavy trucks. However, by fusing GPS information and checkpoint data information, this method can not only more accurately estimate the overall truck flow, but also intuitively display the spatial distribution and operating characteristics of heavy trucks through these two chart forms, which can directly provide support for traffic planning and management decisions without the need for a complicated data interpretation process. These advantages make this application more valuable and efficient in practical applications.

[0088] In some preferred embodiments, step S3 comprises:

[0089] S31, extracting parking data information and movement data information according to the change relationship of GPS information with respect to time;

[0090] S32: Generate a first OD matrix according to the positional relationship between the starting point and the end point of the mobile data information and the traffic zone.

[0091] Specifically, the technical solution of the present application performs a time series analysis on the GPS information, divides it into parking data and mobile data, and then uses the start and end point information of the mobile data and the positional relationship of the traffic cells to generate the OD matrix, which can effectively utilize the spatiotemporal information in the GPS data and accurately reflect the flow of heavy trucks between different traffic cells. The core of this step is to subdivide the GPS data into two categories: parking and moving, and only use mobile data to construct the OD matrix, which can more accurately reflect the actual transportation situation of the trucks and avoid the interference of parking time on the OD matrix. Compared with directly using all GPS data to construct the OD matrix, this method can improve the accuracy and reliability of the analysis results and provide a more reliable data basis for subsequent freight traffic analysis.

[0092] More specifically, in the step of extracting parking data information and moving data information, a variety of methods can be used to distinguish the parking state and moving state of heavy trucks. For example, a time threshold can be set, and when the GPS information shows that the vehicle's position change does not exceed a certain range (such as 100 meters) within a certain period of time (such as 30 minutes), it is determined to be in a parking state. Another method is to analyze the speed data in the GPS information, and when the speed continues to be lower than a certain threshold (such as 3km / h) for a period of time, it is determined to be in a parking state. These methods can be adjusted and combined according to actual conditions to improve the accuracy of judgment.

[0093] More specifically, in the step of generating the first OD matrix, the starting point and the end point of the mobile data information need to be matched with the traffic cell, and the process can be to perform spatial relationship analysis on the GPS coordinate points corresponding to the starting point and the end point of the mobile data information and the pre-divided traffic cell boundaries. For long-distance transportation across multiple traffic cells, the nearest neighbor method or the centroid method can be considered to determine the traffic cell to which the starting and ending points belong.

[0094] More specifically, the urban freight analysis method of the multi-source data source fusion of the embodiment of the present application can improve the quality of mobile data information by accurately distinguishing between parking and moving states, thereby improving the accuracy of the OD matrix. At the same time, the processing results of the mobile data information can also help optimize the judgment criteria of the parking state in turn, forming a feedback optimization process. Finally, based on the matching results, the number of truck flows between different traffic areas is counted to generate the first OD matrix. This matrix reflects the relationship between freight flow between different traffic areas. This processing method effectively avoids the interference of parking time on the OD matrix, improves the reliability of the analysis results, and helps to better understand and optimize the urban freight transportation system.

[0095] It should be noted that step S31 actually extracts the movement trajectory of the corresponding heavy truck based on the GPS information, and then splits the data according to the change relationship of the movement trajectory over time to obtain parking data information and movement data information.

[0096] In some preferred embodiments, step S3 further includes the following steps performed after step S31:

[0097] S3A. Generate a parking distribution heat map of local registered heavy trucks in the target city based on the parking data information, and / or generate a transportation channel flow distribution map of local registered heavy trucks in the target city based on the mobility data information.

[0098] Specifically, the multi-source data source fusion urban freight analysis method of the embodiment of the present application uses parking data information and mobile data information to generate different distribution maps, among which the parking distribution heat map can intuitively display the parking location and density of heavy trucks with local household registration, which is helpful for analyzing parking needs and planning parking facilities. The transportation channel flow distribution map shows the specific transportation routes and flows of heavy trucks in the road network, which is helpful for analyzing traffic flows and optimizing transportation routes. The combination of these two distribution maps provides strong support for a comprehensive understanding and analysis of the operating conditions of heavy trucks with local household registration in the target city, thereby solving the technical problem of obtaining the parking distribution of heavy trucks with local household registration and the transportation channel flow distribution.

[0099] More specifically, if Figure 5 and Figure 6As shown in Figure 1, the generation of parking distribution heat map can adopt kernel density estimation method. Specifically, the target city area can be divided into grids, and the contribution of each parking spot to the surrounding grids decreases according to the distance, and finally the heat value of each grid is obtained. The heat value can be represented by different colors to form an intuitive heat map. Preferably, Figure 5 , Figure 6 and Figure 8 In the figure, the gradual change of the color of the thermal core from yellow to red corresponds to the increasing relationship of the heat value, that is, the increasing relationship of the parking density.

[0100] More specifically, if Figure 7 As shown in Figure 1, the traffic distribution map of the transport channel can be generated by matching the mobile data to the road network. First, the GPS track points are matched to the actual roads, and then the number of vehicles or the frequency of passing on each road are counted. Finally, the flow size can be represented by the thickness of the line and / or the depth of the color. Preferably, Figure 7 and Figure 8 In the figure, the color of the line representing the flow of the transport channel changes from green to red, which corresponds to the increasing relationship of vehicle flow.

[0101] More specifically, there is a close correlation and interaction between these features. Parking data information and mobile data information are extracted from the same GPS data, but reflect different states of the vehicle. The parking distribution heat map and transportation channel flow distribution map are generated based on these two types of data information, respectively, and together constitute a comprehensive analysis of the operating conditions of heavy trucks.

[0102] It should be noted that when generating the traffic distribution map of the local household registration heavy truck transportation channel, the road network matching is first performed. Using the nearest neighbor algorithm, each GPS point is matched to the nearest road. Then the number of vehicles passing on each road is counted.

[0103] In this way, the traffic management department can visually see the main parking areas and main transportation channels for heavy trucks, providing a basis for further traffic planning and management.

[0104] It should be noted that although the above-mentioned parking distribution heat map and transport channel flow distribution map are generated based on the GPS information of local registered heavy trucks, they can indirectly reflect the characteristics of parking distribution and transport channel flow of all heavy trucks in the target city, achieving the effect of seeing the whole picture from a small part.

[0105] In some preferred embodiments, in the embodiment where S3A simultaneously generates a parking distribution heat map of local registered heavy trucks and a transportation channel flow distribution map, step S3A can also superimpose and integrate the parking distribution heat map and the transportation channel flow distribution map into the following: Figure 8The parking and channel flow distribution map shown enables users to more intuitively see the main parking areas and main transportation channels for heavy trucks.

[0106] In some preferred embodiments, the step of generating a traffic distribution map of a local registered heavy truck transport channel in a target city according to the mobile data information includes:

[0107] S3A1, extracting trajectory point information according to the mobile data information;

[0108] S3A2, performing road network matching on the trajectory point information to assign a road segment ID to all trajectory point information;

[0109] S3A3. Assign trajectory point information to the target city's road network based on the road segment ID to generate a traffic distribution map of the local registered heavy truck transport channel.

[0110] Specifically, step S3A realizes the conversion from raw GPS data to a visual traffic distribution map, and the process includes: extracting trajectory point information from mobile data information, which converts continuous GPS data into discrete location points, and then, through road network matching, these trajectory points are matched with the actual road network, and a section ID is assigned to each point, which ensures the accuracy and analyzability of the data. Finally, based on the section ID, the trajectory point information is assigned to the road network of the target urban area, thereby generating a visual graphic reflecting the heavy truck traffic distribution. The visual graphic can use different colors or line thicknesses to represent the traffic size of different sections. For example, red represents high traffic and green represents low traffic; or the thicker the line, the greater the traffic.

[0111] It should be noted that the trajectory point information needs to be paired with the map information. In the embodiment of the present application, it is preferably matched based on the WGS84 coordinate system (EPSG:4326) to facilitate spatial query. The spatial query process can be performed based on the within or sjoin function in GeoPandas.

[0112] More specifically, road network matching of trajectory point information is a key step to ensure data accuracy. This process can use a variety of algorithms to achieve road network matching, such as geometric matching, topological matching or probabilistic matching. For example, when using the geometric matching method, the distance from the trajectory point to the surrounding roads can be calculated, and the nearest road can be selected as the matching result. In order to improve the matching accuracy, the weighted distance calculation can be used in combination with information such as the vehicle's driving direction and speed. In an embodiment of the present application, the road network matching is preferably performed using the GoTrackIt library in Python. The road network matching process can match the GPS trajectory data points to the road network through the matching algorithm of the GoTrackIt library and the map matching algorithm based on the hidden Markov model (HMM), and obtain the matched GeoDataFrame, thereby matching the trajectory points in the corresponding road network, so as to extract the road section ID of the corresponding road network and configure it to the original trajectory point information, ensuring that each trajectory point information has a corresponding road section ID, which is convenient for subsequent traffic statistics and analysis.

[0113] More specifically, the advantage of the urban freight analysis method integrating multi-source data sources in the embodiment of the present application is that it makes full use of the accuracy of GPS data, while improving the practicality of the data through road network matching. The traffic distribution map generated by it can not only intuitively display the use of transportation channels for heavy trucks, but also provide important reference for traffic planning, road maintenance and logistics optimization.

[0114] In some preferred embodiments, step S32 includes:

[0115] S321, when the starting point or the end point of the moving trajectory of the mobile data information is located in the target city, the geometric center of the traffic zone where the starting point or the end point is located is set as the O point or the D point matching the corresponding moving trajectory in the first OD matrix;

[0116] S322, when the starting point or the end point of the moving trajectory of the mobile data information is located on the boundary of the target city area, according to the neighboring relationship of the traffic zones of the target city area, an outer city traffic zone is set outside the target city area, and the point O or the point D corresponding to the starting point or the end point located on the boundary of the target city area is set as the geometric center of the corresponding outer city traffic zone;

[0117] S323 , constructing a first OD matrix based on the relationship between the points O and D corresponding to all movement trajectories in the integrated movement data information.

[0118] Specifically, when the starting point or end point of the mobile trajectory is located within the target city, the geometric center of the traffic zone where it is located is set as point O or point D in the OD matrix, so that the starting and ending positions can be accurately located. When the starting point or end point is located on the boundary of the target city, the boundary point positioning problem can be solved by setting an outer city traffic zone and corresponding the points on the boundary to the geometric center of the outer city traffic zone. Finally, by integrating the relationship between the points O and D of all mobile trajectories, a complete first OD matrix is ​​constructed. This processing method realizes the effective processing of mobile data and the accurate construction of the OD matrix by accurately locating the starting and ending points inside and outside the target city and mapping them to the geometric center of the traffic zone. It takes into account both the traffic flow within the target city and the cross-domain traffic conditions, thereby improving the accuracy and comprehensiveness of the OD matrix.

[0119] More specifically, when dealing with the start or end point on the boundary of the target city, the setting of out-of-city traffic cells is a key step. These out-of-city traffic cells can be set according to the actual situation of the neighboring cities, and their number and size can be determined according to the characteristics of the boundary traffic flow. For example, for the boundary area with heavy traffic flow, multiple sparse out-of-city traffic cells can be set to improve the accuracy; while for the boundary area with light traffic flow, dense out-of-city traffic cells can be set; among them, the out-of-city traffic cells are characterized by Figure 4 , Fig. 9 and Fig.10 The triangular blocks located outside the city map.

[0120] It should be noted that, in other implementations, Figure 4 As shown, the out-of-city traffic zone can also be generated simultaneously according to the relationship between the boundary areas when multiple traffic zones are generated in step S2, and step S322 is changed to: when the starting point or the end point of the moving trajectory of the mobile data information is located on the boundary of the target city, according to the proximity relationship between the starting point or the end point and the out-of-city traffic zone, the point O or the point D corresponding to the starting point or the end point located on the boundary of the target city is set as the geometric center of the corresponding out-of-city traffic zone.

[0121] In some preferred embodiments, step S3 further includes the following steps performed after step S32:

[0122] S3B. Generate a local registered heavy truck distribution map based on the first OD matrix.

[0123] Specifically, after generating the first OD matrix, the urban freight analysis method integrating multiple data sources in the embodiment of the present application can also use the first OD matrix to generate a distribution map of heavy trucks with local household registration. The distribution map of heavy trucks with local household registration can also include an expected travel route map of heavy trucks with local household registration and / or a travel density map of heavy trucks with local household registration, which can intuitively display the spatial distribution of heavy trucks with local household registration and provide strong support for traffic planning and management. It can be combined with the aforementioned heavy truck distribution map, parking distribution heat map and transport channel flow distribution map to comprehensively reflect the truck traffic conditions in the target urban area, so as to provide more types of intuitive data for comprehensive analysis of traffic conditions. This combination not only improves data utilization efficiency, but also ensures the accuracy and comprehensiveness of the analysis results.

[0124] More specifically, the distribution map of heavy trucks with local household registration intuitively shows the spatial distribution of heavy trucks with local household registration, which helps local traffic management departments to formulate targeted traffic management strategies, such as implementing restrictions on vehicles with out-of-town license plates during peak hours to alleviate traffic congestion problems for heavy trucks with local household registration, adjusting recommended travel routes for heavy trucks with local household registration, and adding dedicated lanes for heavy trucks with local household registration in high-density areas, etc., which can effectively optimize route planning to improve utilization.

[0125] In some preferred embodiments, step S4 comprises:

[0126] S41, extracting the license plate type information of the large truck according to the checkpoint data information;

[0127] S42. According to the proportion of local registered license plates in the license plate type information, infer the ratio information of the number of heavy trucks with local registered residence and the actual number of heavy trucks in the target city.

[0128] Specifically, step S41 extracts the license plate type information of large trucks from the checkpoint data information. This step can distinguish between local registered vehicles and non-local vehicles. Step S42 analyzes the proportion of local registered license plates in the license plate type information to infer the ratio of the number of local registered heavy trucks to the total number of heavy trucks in the target city. This processing method cleverly utilizes the license plate type information in the checkpoint data information and estimates the proportion of heavy trucks through statistical analysis, thereby overcoming the difficulty of directly obtaining heavy truck data.

[0129] More specifically, in the specific implementation process, the above steps can be achieved in the following ways:

[0130] First, the checkpoint data information is preprocessed, including data cleaning, format unification and other steps to ensure data quality and consistency. Then, the license plate type information of large trucks is extracted from the preprocessed checkpoint data information. This step can be completed by license plate recognition algorithms or pre-set rules, such as identifying large trucks based on license plate prefixes or specific characters.

[0131] Next, the extracted license plate type information of large trucks is classified into two categories: local household registration license plates and non-local license plates. This can be achieved by comparing the license plate number with the license plate prefix of the target city.

[0132] After the classification is completed, calculate the proportion of local household registration license plates in the total number of license plates in large trucks. This step can be completed through simple statistical calculations, that is, the number of local household registration license plates divided by the total number of license plates; heavy trucks are a type of large trucks. In actual experience, the proportion of local household registration license plates in large trucks and the proportion of local household registration license plates in heavy trucks are similar or have a relatively stable proportional relationship. Therefore, the proportion of large trucks can directly represent or convert the proportion of heavy trucks.

[0133] Finally, based on this proportion, the ratio of the number of heavy trucks with local household registration to the total number of heavy trucks in the target city is inferred.

[0134] More specifically, the above processing method makes full use of the easily accessible checkpoint data information, and indirectly calculates the heavy truck proportion information that is difficult to obtain directly through analysis of the license plate type information.

[0135] In some other embodiments, step S4 includes:

[0136] S41 ', extracting the heavy truck entry and exit timestamp information, license plate type information and bayonet location information according to the bayonet data information;

[0137] S42', based on the heavy truck entry and exit timestamp information, license plate type information and checkpoint location information and the positional relationship corresponding to each OD pair in the first OD matrix, obtain the ratio information about the number of local registered heavy trucks in the target city and the actual number of heavy trucks matching each OD pair in the first OD matrix.

[0138] Specifically, in this embodiment, by pairing the checkpoint location information with the position of the OD pair, the ratio of the number of local registered heavy trucks corresponding to different OD pairs to the actual number of heavy trucks is more accurately identified to form ratio information expressed as a ratio matrix, so that step S5 can expand the first OD matrix according to the ratio matrix to obtain the second OD matrix to more accurately generate a heavy truck distribution map, wherein step S42' can determine the driving route of the heavy truck according to the checkpoint location information and the license plate type information, and the entry and exit timestamp information can be used to determine whether the heavy truck has parked or detoured to filter the driving route, and then the corresponding driving starting point and end point can be determined according to the filtered driving route, and these driving starting points and end points can be matched with the corresponding positional relationship of each OD pair in the first OD matrix, and then the corresponding route number can be distinguished according to the license plate type information, so as to obtain the ratio information of the number of local registered heavy trucks and the actual number of heavy trucks in the target city that matches the OD pairs of the first OD matrix.

[0139] In some preferred embodiments, the method further comprises the following steps:

[0140] S7, obtaining truck movement data information and / or small truck movement data information according to the checkpoint data information;

[0141] S8, generating a third OD matrix and / or a fourth OD matrix according to the positional relationship between the starting point and the end point of the truck movement data information and / or the small truck movement data information and the traffic zone;

[0142] S9. Generate a truck distribution map and / or a small truck distribution map according to the third OD matrix and / or the fourth OD matrix.

[0143] Specifically, the above processing method realizes a comprehensive analysis of the traffic operation of more types of trucks by expanding the data source and analysis object. It obtains the mobile data information of more types of trucks including small trucks by processing the checkpoint data information, and then associates these data with the traffic cells to generate the corresponding OD matrix (wherein the third OD matrix corresponds to the mobile data information of all categories of trucks, that is, corresponds to the comprehensive data of all trucks, and the fourth OD matrix corresponds to the mobile data information of small trucks). This step converts the original data into analyzable structured data, and then generates distribution maps based on these OD matrices, which intuitively show the traffic operation conditions of different types of trucks.

[0144] It should be noted that, in step S9, the third OD matrix is ​​used to generate a truck distribution map, and the fourth OD matrix is ​​used to generate a small truck distribution map.

[0145] More specifically, unlike the heavy truck distribution map, the truck distribution map and the small truck distribution map generated in step S9 do not introduce GPS information for correction. They are used to conduct a comprehensive analysis of truck traffic in the entire city in combination with the aforementioned parking distribution heat map, transportation channel flow distribution map and heavy truck distribution map. Unlike heavy trucks, which are the core of freight and the main cause of large-scale traffic jams, the distribution maps corresponding to the overall truck data and small truck data do not need to be too precise, so the checkpoint data information can be directly analyzed and obtained to assist in traffic management decisions and urban planning regarding heavy trucks.

[0146] It should be noted that the truck distribution map and the small truck distribution map may also include corresponding truck travel expected route maps and / or truck travel density maps, and their generation methods and presentation forms are not described in detail here.

[0147] More specifically, by introducing truck distribution maps and small truck distribution maps, the urban freight analysis method that integrates multi-source data sources in the embodiment of the present application can further enhance the visualization effect by generating multiple types of truck distribution maps, and can further increase the analysis dimension by distinguishing the distribution maps of different types of trucks, so as to reveal more detailed traffic patterns, thereby providing a more comprehensive and accurate decision support tool for urban freight traffic management.

[0148] In some preferred embodiments, the method further comprises the following steps:

[0149] S10. Obtain a fifth OD matrix based on the third OD matrix by subtracting the fourth OD matrix and the second OD matrix, and generate a distribution map of other large trucks according to the fifth OD matrix.

[0150] Specifically, in this embodiment, the OD data of all large trucks can be inferred based on the subtraction of the fourth OD matrix from the third OD matrix, and then the OD data of the remaining large trucks except heavy trucks can be inferred by subtracting the second OD matrix. The distribution map of the remaining large trucks generated on this basis can intuitively display the traffic operation conditions of the remaining large trucks except heavy trucks, so as to further enhance the visualization effect and analysis dimension of the urban freight analysis method with multi-source data source fusion of the embodiment of the present application, and provide a more comprehensive and accurate decision support tool for urban freight traffic management.

[0151] In some preferred embodiments, before executing step S3, GPS information needs to be preprocessed, and the preprocessing includes: data coding check and transcoding, data merging, duplicate data deletion, data sorting and format conversion, data quality check, redundancy elimination and data drift removal.

[0152] Specifically, the data encoding check and transcoding steps are used to ensure that the data corresponding to all GPS information uses a unified encoding format. For example, GPS data from different sources can be uniformly converted to UTF-8 encoding for subsequent processing. The specific process can be achieved by using the read_csv function of the pandas library and setting the encoding parameter to achieve data conversion, and then saving it as a new CSV file.

[0153] More specifically, the data merging process is used to integrate the data of different time periods after the aforementioned transcoding. This can be achieved through timestamps and vehicle identifiers to ensure the complete driving trajectory of each heavy truck. The specific process can use the concat function or merge function of the pandas library to merge the transcoded data according to the date field. During the merging process, it is necessary to ensure the consistency of field names and data types, and there are no abnormal values ​​or missing values.

[0154] More specifically, the deduplication process can use the drop_duplicates function of the pandas library to delete duplicate rows with all the same fields. You can also consider using a hash function to verify the uniqueness of each row of data to delete duplicate data; verify the number of rows after deleting duplicate data to ensure that the amount of data is reduced.

[0155] More specifically, the data sorting and format conversion processing can use the sort_values ​​function of the pandas library to sort the data according to the GPS time field, and then convert the timestamp row into the datetime format based on the pd.to_datetime function.

[0156] More specifically, the data quality check process is used to check data such as the number of data rows, vehicle format, number of data points, data sampling density, data sampling frequency, etc.

[0157] More specifically, redundancy elimination is used to delete data that is identical to previous and subsequent data information in order to reduce the amount of data.

[0158] More specifically, drift is defined as data with a speed greater than a speed limit, or a distance between the current point and the next point greater than a distance limit, or an angle between the current point and the previous point and the next point less than an angle limit. Data drift deletion processing is used to delete these abnormal data to ensure data quality and improve data authenticity and reliability.

[0159] By implementing these preprocessing steps, the urban freight analysis method of multi-source data source fusion in the embodiment of the present application can significantly improve the quality and reliability of GPS data. Specifically, data coding check and transcoding ensure the consistency of data, which is convenient for subsequent processing. Data merging improves the integrity of data and makes the analysis results more comprehensive. Deduplication and redundancy elimination reduce the amount of data and improve processing efficiency. Data sorting and format conversion unify the data format for subsequent analysis. Data quality check and data drift removal improve the accuracy of data and reduce the impact of outliers on the analysis results.

[0160] Second, please refer to Fig.11 and Fig.12 Some embodiments of the present application also provide a multi-source data source fusion urban freight analysis device for analyzing freight data, the device comprising:

[0161] The acquisition module 201 is used to acquire GPS information, checkpoint data information and map information about heavy trucks in the target city;

[0162] A partitioning module 202, for generating a plurality of traffic zones according to preset partitioning rules and map information;

[0163] A matrix module 203, configured to generate a first OD matrix about the traffic cell according to the GPS information;

[0164] A ratio module 204 is used to obtain information on the ratio of the number of local registered heavy trucks to the actual number of heavy trucks in the target city according to the checkpoint data information;

[0165] An expansion module 205, configured to expand the first OD matrix according to the ratio information to obtain a second OD matrix;

[0166] The generating module 206 is used to generate a heavy truck distribution map according to the second OD matrix.

[0167] The multi-source data source fusion urban freight analysis device of the embodiment of the present application combines map information, GPS data and checkpoint data information, and can not only obtain the operation status of local registered heavy trucks in different traffic areas, but also infer the operation status of the entire heavy trucks in different traffic areas. It fully utilizes the advantages of different data sources and makes up for the shortcomings of a single data source. It realizes the extrapolation from the local to the whole through the OD matrix expansion sample, improves the representativeness of the analysis results, and provides a scientific basis for traffic management and decision-making.

[0168] In some preferred embodiments, the urban freight analysis device with fusion of multiple source data sources of the embodiment of the present application is used to execute the urban freight analysis method with fusion of multiple source data sources provided in the first aspect above.

[0169] In some preferred embodiments, the device further comprises:

[0170] The secondary acquisition module 207 is used to acquire truck movement data information and / or small truck movement data information from the checkpoint data information;

[0171] The secondary matrix module 208 is used to generate a third OD matrix and / or a fourth OD matrix according to the position relationship between the starting point and the end point of the truck movement data information and / or the small truck movement data information and the traffic zone;

[0172] The secondary generating module 209 is configured to generate a truck distribution map and / or a small truck distribution map according to the third OD matrix and / or the fourth OD matrix.

[0173] The third-level generation module 210 is used to obtain a fifth OD matrix based on the third OD matrix minus the fourth OD matrix and the second OD matrix, and generate the remaining large truck distribution maps according to the fifth OD matrix.

[0174] Third, please refer to Fig.13 Some embodiments of the present application also provide a structural diagram of an electronic device. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanisms (not marked). The memory 302 stores computer-readable instructions executable by the processor 301. When the electronic device is running, the processor 301 executes the computer-readable instructions to execute the method in any optional implementation of the above embodiments.

[0175] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0176] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0177] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] Furthermore, the functional modules in the various embodiments of the present application 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.

[0179] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0180] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing urban freight data by integrating multiple data sources, which is used to analyze freight data, and is characterized in that: The method comprises the following steps: S1. Obtain GPS information, checkpoint data information and map information about heavy trucks in the target city; S2. Generate multiple traffic zones according to preset division rules and map information; S3, generating a first OD matrix about the traffic cell according to the GPS information; S4. Obtaining information on the ratio of the number of local registered heavy trucks to the actual number of heavy trucks in the target city according to the checkpoint data information; S5, expanding the first OD matrix according to the ratio information to obtain a second OD matrix; S6. Generate a heavy truck distribution map according to the second OD matrix; Step S3 includes: S31, extracting parking data information and movement data information according to the change relationship of the GPS information with respect to time; S32, generating the first OD matrix according to the positional relationship between the starting point and the end point of the mobile data information and the traffic zone; Step S32 includes: S321, when the starting point or the end point of the moving trajectory of the mobile data information is located in the target city, the geometric center of the traffic zone where the starting point or the end point is located is set as the O point or the D point matching the corresponding moving trajectory in the first OD matrix; S322, when the starting point or the end point of the moving trajectory of the mobile data information is located on the boundary of the target urban area, according to the neighboring relationship of the traffic zones of the target urban area, an outer city traffic zone is set outside the target urban area, and the point O or the point D corresponding to the starting point or the end point located on the boundary of the target urban area is set as the geometric center of the corresponding outer city traffic zone; S323: Construct the first OD matrix by comprehensively analyzing the relationship between the O points and the D points corresponding to all the movement tracks in the movement data information.

2. The method for analyzing urban freight transport by integrating multiple data sources according to claim 1 is characterized in that: The heavy truck distribution map includes a heavy truck travel expected route map and / or a heavy truck travel density map.

3. The method for analyzing urban freight transport by integrating multiple data sources according to claim 1 is characterized in that: Step S3 also includes the following steps performed after step S31: S3A. Generate a parking distribution heat map of local heavy trucks of the target city based on the parking data information, and / or generate a transportation channel flow distribution map of local heavy trucks of the target city based on the mobile data information.

4. The method for analyzing urban freight transport by integrating multiple data sources according to claim 3 is characterized in that: The step of generating a traffic distribution map of the local registered heavy truck transportation channel of the target city according to the mobile data information comprises: S3A1, extracting trajectory point information according to the movement data information; S3A2, performing road network matching on the track point information to assign road segment IDs to all track point information; S3A3. Assigning trajectory point information to the road network of the target city based on the road segment ID to generate a traffic distribution map of the local registered heavy truck transport channel.

5. The method for analyzing urban freight transport by integrating multiple data sources according to claim 1 is characterized in that: Step S4 includes: S41, extracting the license plate type information of the large truck according to the checkpoint data information; S42. According to the proportion of local registered license plates in the license plate type information, infer the ratio information of the number of heavy trucks with local registered residence and the actual number of heavy trucks in the target city.

6. A multi-source data source fusion urban freight analysis device for analyzing freight data, characterized in that: The device comprises: An acquisition module is used to obtain GPS information, checkpoint data information and map information about heavy trucks in the target city; A partitioning module, used to generate multiple traffic zones according to preset partitioning rules and map information; A matrix module, used for generating a first OD matrix about the traffic cell according to the GPS information; A ratio module, used to obtain the ratio information of the number of local registered heavy trucks and the actual number of heavy trucks in the target city according to the checkpoint data information; An expansion module, configured to expand the first OD matrix according to the ratio information to obtain a second OD matrix; A generating module, used for generating a heavy truck distribution map according to the second OD matrix; The step of generating a first OD matrix about the traffic cell according to the GPS information comprises: S31, extracting parking data information and movement data information according to the change relationship of the GPS information with respect to time; S32, generating the first OD matrix according to the positional relationship between the starting point and the end point of the mobile data information and the traffic zone; Step S32 includes: S321, when the starting point or the end point of the moving trajectory of the mobile data information is located in the target city, the geometric center of the traffic zone where the starting point or the end point is located is set as the O point or the D point matching the corresponding moving trajectory in the first OD matrix; S322, when the starting point or the end point of the moving trajectory of the mobile data information is located on the boundary of the target urban area, according to the neighboring relationship of the traffic zones of the target urban area, an outer city traffic zone is set outside the target urban area, and the point O or the point D corresponding to the starting point or the end point located on the boundary of the target urban area is set as the geometric center of the corresponding outer city traffic zone; S323: Construct the first OD matrix by comprehensively analyzing the relationship between the O points and the D points corresponding to all the movement tracks in the movement data information.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 5 are executed.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are executed.

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