Freight OD data acquisition method, device, medium and product

Abstract

The application discloses a freight OD data acquisition method and device, medium and product, relates to the field of road freight, and comprises the following steps: determining a freight vehicle driving area based on to-be-processed freight vehicle trajectory data and a regional road network; the freight vehicle driving area comprises a plurality of road sections; determining a freight vehicle parking node set based on the to-be-processed freight vehicle trajectory data; performing road section matching on the trajectory points in the to-be-processed freight vehicle trajectory data in the freight vehicle driving area, so as to obtain a freight vehicle driving path; and determining an OD point set based on the freight vehicle driving path and the freight vehicle parking node set; the OD point set comprises a trip starting point subset and a trip ending point subset, and the points in the OD point set are all located on the road sections. The application can efficiently acquire accurate freight OD data.

Description

Technical Field

[0001] This application relates to the field of road freight technology, and in particular to a method, device, medium and product for acquiring freight OD data. Background Technology

[0002] The distribution of freight origin-destination (OD) is one of the key areas that the road freight transport industry needs to monitor and analyze. Improving the industry's regulatory service level and promoting high-quality development requires comprehensive and accurate freight OD data. However, traditional methods for obtaining freight OD data mainly rely on traffic surveys, typically including roadside interviews and questionnaires. These methods are costly and inefficient, affecting the accuracy of the obtained freight OD data. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, medium, and product for acquiring freight OD data, which can efficiently acquire accurate freight OD data.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a method for acquiring freight OD data, including:

[0006] Acquire the trajectory data of freight vehicles to be processed and the regional road network;

[0007] Based on the trajectory data of the freight vehicles to be processed and the regional road network, the driving area of ​​the freight vehicles is determined; the driving area of ​​the freight vehicles includes multiple road segments.

[0008] Based on the trajectory data of the freight vehicles to be processed, determine the set of parking nodes for the freight vehicles.

[0009] Within the driving area of ​​the freight vehicle, road segment matching is performed on the trajectory points in the trajectory data of the freight vehicle to be processed to obtain the driving path of the freight vehicle.

[0010] Based on the travel routes of the freight vehicles and the set of parking nodes of the freight vehicles, an OD point set is determined; the OD point set includes a subset of travel origins and a subset of travel destinations, and all points in the OD point set are located on road segments.

[0011] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a freight OD data acquisition method.

[0012] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for acquiring freight OD data.

[0013] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements a method for acquiring freight OD data.

[0014] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, device, medium, and product for acquiring freight OD data. It determines the driving area of ​​freight vehicles based on the trajectory data of the freight vehicles to be processed and the regional road network, thereby reducing the size of the driving area and avoiding the need to process large amounts of data in subsequent matching processes, thus reducing computational resource consumption. It also extracts application value from the trajectory data of the freight vehicles to be processed, correspondingly determining the set of freight vehicle parking nodes. Then, within the driving area of ​​the freight vehicles, it performs road segment matching on the trajectory points in the trajectory data of the freight vehicles to be processed to obtain accurate freight vehicle driving paths. Finally, based on the freight vehicle driving paths and the set of freight vehicle parking nodes, it achieves the screening and classification of freight vehicle parking nodes, obtaining a subset of travel origins and a subset of travel destinations. Compared with manual surveys in the prior art, this application has higher efficiency and accuracy in acquiring freight OD data. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is an application environment diagram of a freight OD data acquisition method according to an embodiment of this application;

[0017] Figure 2 A flowchart illustrating a method for acquiring freight OD data according to an embodiment of this application;

[0018] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Studies have found that most freight vehicles are equipped with positioning software and connected to relevant information platforms, which makes it possible to acquire freight OD data based on vehicle trajectories. This allows for the accurate acquisition of vehicle trajectory information, such as vehicle number, trajectory time, instantaneous position (latitude and longitude), instantaneous speed, and direction angle, through these platforms. From this information, various details related to vehicle movement can be extracted, such as freight OD point information and freight type. Based on this, the freight OD data acquisition method, equipment, media, and products described in this application can, on the one hand, further improve the existing monitoring and analysis system of the road freight industry, enabling relevant personnel to more systematically grasp the dynamics and patterns of the spatiotemporal distribution of road freight; on the other hand, it can effectively overcome the drawbacks of traditional traffic surveys, such as high cost and low efficiency, and improve the accuracy and scientific nature of government management measures such as traffic planning and logistics planning, thereby enhancing the industry's governance capabilities and level.

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] The freight OD data acquisition method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the freight vehicle trajectory data and regional road network to be processed to server 104. Server 104 determines the freight vehicle's driving area based on the freight vehicle trajectory data and regional road network, determines the freight vehicle's parking node set based on the freight vehicle trajectory data, performs road segment matching on the trajectory points in the freight vehicle trajectory data within the freight vehicle's driving area to obtain the freight vehicle's driving path, and determines the OD point set based on the freight vehicle's driving path and the freight vehicle parking node set. Server 104 can feed back the obtained OD point set to terminal 102. Furthermore, in some embodiments, the freight OD data acquisition method can be implemented independently by server 104 or terminal 102, or server 104 can obtain the freight vehicle trajectory data and regional road network from the data storage system and process it to obtain the OD point set.

[0023] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0024] In one exemplary embodiment, such as Figure 2 As shown, a method for acquiring freight OD data is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 205.

[0025] Step 201: Obtain the trajectory data of the freight vehicles to be processed and the regional road network. The steps for obtaining the trajectory data of the freight vehicles to be processed include:

[0026] (11) Obtain the original freight vehicle trajectory data. This original freight vehicle trajectory data can be directly retrieved from relevant information platforms, but there may be problems such as missing, duplicate, or drifting data.

[0027] (12) Perform data cleaning on the original freight vehicle trajectory data; for the missing and duplicate problems in the original freight vehicle trajectory data, data cleaning technology is used to identify and remove them.

[0028] (13) The original freight vehicle trajectory data after data cleaning is optimized using a Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed. Specifically, to address the drift problem in the original freight vehicle trajectory data, the Kalman filter algorithm is used to optimize the trajectory point positions by calculating the change in the direction angle and average speed of the freight vehicle at each trajectory point, thereby obtaining the freight vehicle trajectory data to be processed. This includes:

[0029] 131) For any current trajectory point in the original freight vehicle trajectory data after data cleaning, obtain the previous trajectory point corresponding to the current trajectory point; the data corresponding to the current trajectory point includes the preliminary observation value of the current direction angle state and the preliminary observation value of the current speed state, and the data corresponding to the previous trajectory point includes the estimated value of the previous direction angle state and the estimated value of the previous speed state.

[0030] 132) Using the first prediction equation, the predicted value of the current heading angle state is calculated based on the estimated value of the previous heading angle state and the preliminary observation value of the current heading angle state. The first prediction equation considers the dynamic model of the freight vehicle's motion.

[0031] 133) The predicted value of the current orientation angle state and the preliminary observed value of the current orientation angle state are weighted and fused to obtain the optimized orientation angle state. Specifically, weighting coefficients are calculated based on the covariance matrix of the predicted value of the current orientation angle state and the preliminary observed value of the current orientation angle state, and then the weighted fusion is performed.

[0032] 134) Using the second prediction equation, the predicted value of the current speed state is calculated based on the previous speed state estimate and the preliminary observation value of the current speed state. The second prediction equation considers the dynamic model of the freight vehicle's motion.

[0033] 135) The predicted current speed state value and the preliminary observed current speed state value are weighted and fused to obtain the optimized speed state. The specific weighting process is similar to the process in step 133) above. In practical applications, the preliminary observed current speed state value corresponding to the current trajectory point is the average speed of the freight vehicle.

[0034] 136) Based on the optimized direction angle state and the optimized speed state, determine the optimized data corresponding to the current trajectory point; the optimized data corresponding to all trajectory points constitute the trajectory data of the freight vehicle to be processed.

[0035] Steps 132)-136) above optimize the current trajectory point. When optimizing the next trajectory point, the current trajectory point is taken as the corresponding previous trajectory point, the optimized direction angle state corresponding to the current trajectory point is taken as the estimated value of the previous direction angle state, and the optimized velocity state corresponding to the current trajectory point is taken as the estimated value of the previous velocity state. Then, steps 132)-136 above are repeated.

[0036] Step 202: Based on the trajectory data of the freight vehicles to be processed and the regional road network, determine the driving area of ​​the freight vehicles; the driving area of ​​the freight vehicles includes multiple road segments.

[0037] In one application example, firstly, a spatial grid index of the regional road network is established, forming a boundary and index ID system to facilitate subsequent data retrieval and processing; secondly, the mapping relationship between the regional road grid and road segments is established, and a road segment-grid relationship table is generated; then, based on the GPS latitude and longitude coordinates of each trajectory point in the freight vehicle trajectory data to be processed, the actual road segment where the freight vehicle is located is determined, and then, based on the road segment-grid relationship table, the actual grid where the freight vehicle is located is determined, and the map in that grid is used as the freight vehicle's driving area.

[0038] Step 203: Based on the trajectory data of the freight vehicles to be processed, determine the set of parking nodes for the freight vehicles.

[0039] In one application example, step 203 includes:

[0040] (31) The trajectory data of the freight vehicles to be processed is divided according to a preset duration to obtain multiple vehicle trajectory segments; in practical applications, the preset duration can be set to 20 minutes, and multiple continuous vehicle trajectory segments are obtained after division.

[0041] (32) For any of the vehicle trajectory segments, the driving distance within the vehicle segment is calculated by the accumulation method; that is, the driving distance between consecutive trajectory points is calculated and recorded, and the driving distance within each vehicle trajectory segment is accumulated.

[0042] (33) Calculate the average speed within the vehicle segment based on the distance traveled within the vehicle segment and the preset duration.

[0043] (34) Based on the average driving speed within each vehicle trajectory segment, determine the set of parking nodes for freight vehicles. In practical applications, step (34) specifically includes:

[0044] 341) Based on the average driving speed within each vehicle trajectory segment, the quantile method is used to determine the stopping speed threshold. For example, after arranging the average driving speeds within each vehicle trajectory segment in ascending order, ten quantiles are set. The tenth quantile can then be selected as the stopping speed threshold. In this case, the average driving speed within each vehicle segment corresponding to 90% of the vehicle trajectory segments is higher than the stopping speed threshold, and the average driving speed within each vehicle segment corresponding to 10% of the vehicle trajectory segments is lower than the stopping speed threshold.

[0045] 342) From the multiple vehicle trajectory segments, select vehicle trajectory segments with an average driving speed lower than the parking speed threshold and mark them as parking trajectory segments. Corresponding to step 341) above, if there are 10% of the vehicle trajectory segments with an average driving speed lower than the parking speed threshold, then these 10% of vehicle trajectory segments can be regarded as parking trajectory segments, in which freight vehicles are parked for a long time.

[0046] 343) Mark the trajectory points within the parking trajectory segment as freight vehicle parking nodes; all the freight vehicle parking nodes constitute a freight vehicle parking node set.

[0047] Step 204: Within the driving area of ​​the freight vehicle, perform road segment matching on the trajectory points in the trajectory data of the freight vehicle to be processed to obtain the driving path of the freight vehicle.

[0048] In one application example, step 204 includes:

[0049] (41) For each road segment within the driving area of ​​the freight vehicle, a preset scoring function is used to calculate the corresponding road segment score; the road segment score is used to characterize the road segment length, road segment type (such as main road, branch road, etc. with different weights) and the proximity between the road segment and the nodes in the set of parking nodes of the freight vehicle.

[0050] (42) Based on the Hidden Markov Model, the trajectory points in the freight vehicle trajectory data to be processed are searched and matched for road segments within the freight vehicle's driving area to obtain all possible paths, and the total road segment score corresponding to each possible path is calculated. Each possible path contains at least one road segment. Specifically, when traversing all road segments within the freight vehicle's driving area to determine all possible paths, a depth-first search or breadth-first search algorithm can be used.

[0051] (43) Mark the most likely path with the highest total road segment score as the freight vehicle's travel path. At this point, the matching of road segments and freight vehicle trajectory points is complete.

[0052] Step 205: Based on the freight vehicle's travel path and the freight vehicle's parking node set, determine the OD point set; the OD point set includes a subset of travel origins and a subset of travel destinations, and all points in the OD point set are located on road segments.

[0053] In one application example, step 205 includes:

[0054] (51) For any freight vehicle parking node in the set of freight vehicle parking nodes, if the freight vehicle parking node is not on the road where the freight vehicle is traveling, it indicates that the freight vehicle parking point and the road network have not been matched successfully, and the freight vehicle parking node is deleted at this time.

[0055] (52) If the freight vehicle parking node is located on the freight vehicle's driving road, it indicates that the freight vehicle parking point is successfully matched with the road network. At this time, the freight vehicle parking node is temporarily stored in the OD set to be sorted.

[0056] (53) Calculate the distance interval between any two adjacent freight vehicle parking nodes in the OD set to be sorted.

[0057] (54) Calculate the average distance interval based on all the distance intervals in the OD set to be sorted.

[0058] (55) When the distance between the i-th freight vehicle parking node and the (i-1)-th freight vehicle parking node is greater than the average distance interval, the i-th freight vehicle parking node is marked as the travel destination; multiple travel destinations constitute a subset of travel destinations.

[0059] (56) When the distance between the i-th freight vehicle parking node and the (i-1)-th freight vehicle parking node is less than the average distance interval, the i-th freight vehicle parking node is marked as the starting point of the trip; multiple starting points constitute a subset of starting points of the trip; the subset of starting points of the trip and the subset of ending points of the trip constitute an OD point set.

[0060] Compared with the prior art, this application has the following advantages:

[0061] (1) This application studies the technology of extracting and automatically generating freight OD data based on big data, which can effectively avoid the problems of low efficiency, high cost and high error rate faced by manual investigation.

[0062] (2) Based on freight trajectory data, this application studies freight OD data, accurately analyzes the spatial distribution of freight spatial demand, identifies major freight channels and key nodes, which is conducive to optimizing transportation and logistics planning methods and improving the scientific nature of planning.

[0063] (3) This application fully explores the application value of freight vehicle trajectory data. By analyzing the vehicle OD data, combined with the application of OD data, it can comprehensively judge the industry's operating rules and existing problems, and also provide quantitative support for the research and formulation of relevant industry plans, thereby improving the level and efficiency of industry governance.

[0064] (4) This application can be used to characterize the features of freight logistics. Combined with the layout of transportation facilities and industrial layout, it can further analyze the matching degree and support of transportation facilities with industries and urban functional areas, thereby driving the coordinated optimization of transportation facilities and industrial structure and achieving dynamic and high-level matching between the two.

[0065] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a freight OD data acquisition method.

[0066] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0067] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0068] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0071] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for acquiring freight OD data, characterized in that, The method for acquiring freight OD data includes: Acquire the trajectory data of freight vehicles to be processed and the regional road network; Based on the trajectory data of the freight vehicles to be processed and the regional road network, the driving area of ​​the freight vehicles is determined; the driving area of ​​the freight vehicles includes multiple road segments. Based on the trajectory data of the freight vehicles to be processed, determine the set of parking nodes for the freight vehicles. Within the operating area of ​​the freight vehicle, segment matching is performed on the trajectory points in the trajectory data of the freight vehicle to be processed to obtain the freight vehicle's driving path. This includes: for each segment within the operating area of ​​the freight vehicle, a preset scoring function is used to calculate the corresponding segment score; the segment score is used to characterize the segment length, segment type, and proximity between the segment and nodes in the set of parking nodes of the freight vehicle; based on a hidden Markov model, segment search and matching are performed on the trajectory points in the trajectory data of the freight vehicle to be processed within the operating area of ​​the freight vehicle to obtain all possible paths, and the total segment score corresponding to each possible path is calculated; the possible path with the highest total segment score is marked as the freight vehicle's driving path. Based on the freight vehicle travel path and the freight vehicle parking node set, determine the OD (Original Dispatch Point) set, including: for any freight vehicle parking node in the freight vehicle parking node set, if the freight vehicle parking node is not on the freight vehicle travel path, delete the freight vehicle parking node; if the freight vehicle parking node is on the freight vehicle travel path, temporarily store the freight vehicle parking node in the pending sorting OD set; calculate the distance interval between any two adjacent freight vehicle parking nodes in the pending sorting OD set; and calculate the average distance interval based on all the distance intervals in the pending sorting OD set. When the distance between the i-th freight vehicle parking node and the (i-1)-th freight vehicle parking node is greater than the average distance interval, the i-th freight vehicle parking node is marked as the travel destination; multiple travel destinations constitute a subset of travel destinations; when the distance between the i-th freight vehicle parking node and the (i-1)-th freight vehicle parking node is less than the average distance interval, the i-th freight vehicle parking node is marked as the travel origin; multiple travel origins constitute a subset of travel origins; the subset of travel origins and the subset of travel destinations constitute an OD point set, and all points in the OD point set are located on road segments.

2. The freight OD data acquisition method according to claim 1, characterized in that, Obtain the trajectory data of the freight vehicles to be processed, including: Obtain raw freight vehicle trajectory data; The original freight vehicle trajectory data is cleaned. The original freight vehicle trajectory data, after data cleaning, is optimized using a Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed.

3. The freight OD data acquisition method according to claim 2, characterized in that, The original freight vehicle trajectory data, after data cleaning, is used to optimize the trajectory point positions using the Kalman filter algorithm to obtain the freight vehicle trajectory data to be processed, including: For any current trajectory point in the original freight vehicle trajectory data after data cleaning, obtain the previous trajectory point corresponding to the current trajectory point; the data corresponding to the current trajectory point includes the preliminary observation value of the current direction angle state and the preliminary observation value of the current speed state, and the data corresponding to the previous trajectory point includes the estimated value of the previous direction angle state and the estimated value of the previous speed state. Using the first prediction equation, the predicted value of the current orientation angle state is calculated based on the estimated value of the previous orientation angle state and the preliminary observed value of the current orientation angle state. The predicted value of the current orientation angle state and the preliminary observed value of the current orientation angle state are weighted and fused to obtain the optimized orientation angle state; Using the second prediction equation, the predicted value of the current velocity state is calculated based on the previous velocity state estimate and the preliminary observation value of the current velocity state; The predicted value of the current speed state and the preliminary observed value of the current speed state are weighted and fused to obtain the optimized speed state; Based on the optimized direction angle state and the optimized speed state, the optimized data corresponding to the current trajectory point is determined; the optimized data corresponding to all trajectory points constitute the trajectory data of the freight vehicle to be processed.

4. The freight OD data acquisition method according to claim 1, characterized in that, Based on the freight vehicle trajectory data to be processed, a set of freight vehicle parking nodes is determined, including: The trajectory data of the freight vehicles to be processed is segmented according to a preset duration to obtain multiple vehicle trajectory segments. For any of the vehicle trajectory segments, the distance traveled within the vehicle segment is calculated using the summation method; Calculate the average speed within the vehicle segment based on the distance traveled within the vehicle segment and the preset duration. Based on the average driving speed within each vehicle trajectory segment, the set of parking nodes for freight vehicles is determined.

5. The freight OD data acquisition method according to claim 4, characterized in that, Based on the average speed within each vehicle trajectory segment, a set of parking nodes for freight vehicles is determined, including: Based on the average driving speed within the vehicle segment corresponding to each vehicle trajectory segment, the quantile method is used to determine the parking speed threshold. From the multiple vehicle trajectory segments, vehicle trajectory segments with an average driving speed less than the parking speed threshold are selected and marked as parking trajectory segments; The trajectory points within the parking trajectory segment are marked as freight vehicle parking nodes; all the freight vehicle parking nodes constitute a freight vehicle parking node set.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the freight OD data acquisition method according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the freight OD data acquisition method according to any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the freight OD data acquisition method according to any one of claims 1-5.

Images