CN116112873A

CN116112873A - Travel OD segmentation method and device based on mobile positioning

Info

Publication number: CN116112873A
Application number: CN202310118511.2A
Authority: CN
Inventors: 由守峰; 鲁啸
Original assignee: Beijing Dongtu Tuoming Technology Co ltd
Current assignee: Beijing Dongtu Tuoming Technology Co ltd
Priority date: 2023-02-03
Filing date: 2023-02-03
Publication date: 2023-05-12

Abstract

The application provides a travel OD segmentation method and device based on mobile positioning, wherein the method comprises the following steps: constructing a road network base station matching model according to a target base station in the road network and nodes arranged in the road network; acquiring real-time mobile signaling data of a user, and analyzing and extracting base station switching sequence information according to the real-time mobile signaling data; performing positioning matching and path fitting on the base station switching sequence information and a road network base station matching model to obtain a base station positioning data sequence in the user moving process; and carrying out OD track segmentation processing on the base station positioning data sequence to obtain a travel OD track of the user. According to the method and the device, the OD track of the user is screened and segmented through algorithms such as a road network base station matching model, track fitting and OD segmentation, so that an accurate user travel OD track is obtained.

Description

Travel OD segmentation method and device based on mobile positioning

Technical Field

The application relates to the technical field of traffic big data analysis, in particular to a travel OD segmentation method and device based on mobile positioning.

Background

An OD (Origin) survey, i.e., a traffic start-stop survey, is also called an OD traffic volume survey, and the OD traffic volume refers to the volume of traffic between start-stop points. The OD investigation is mainly used for investigating the starting point and the end point of individual travel in a certain area, and belongs to a special investigation in traffic investigation. With the gradual perfection of road networks and the rapid increase of road traffic demands, the mobile phone signaling big data are used as an important means for traffic OD investigation, are increasingly widely applied, and simultaneously, higher requirements are put forward on the accuracy of resident trip OD.

In recent years, with the continuous popularization of mobile phone terminals, traffic information acquisition technologies based on mobile network mobile phone signaling data are increasingly being valued and applied by various levels of traffic departments. With the update of 4G and 5G mobile networks, wireless positioning technology is attracting more and more attention, and high-precision positioning methods and applications are necessarily key requirements of future mobile positioning technology. Meanwhile, based on mass user coverage and data resources, through mathematical model analysis, obtaining the resident trip OD with higher precision becomes a main development direction of the mobile positioning technology in recent years.

Disclosure of Invention

In view of this, the present application proposes a travel OD segmentation method and apparatus, device, and storage medium based on mobile positioning, where the OD trajectory of the user is screened and segmented by an algorithm such as a road network base station matching model, a trajectory fitting and OD segmentation, so as to obtain an accurate user travel OD trajectory.

In a first aspect, the present application provides a travel OD segmentation method based on mobile positioning, including:

constructing a road network base station matching model according to a target base station in the road network and nodes arranged in the road network;

acquiring real-time mobile signaling data of a user, and analyzing and extracting base station switching sequence information according to the real-time mobile signaling data;

Performing positioning matching and path fitting on the base station switching sequence information and a road network base station matching model to obtain a base station positioning data sequence in the user moving process;

and carrying out OD track segmentation processing on the base station positioning data sequence to obtain a travel OD track of the user.

By the method, the base station positioning data sequence of the user moving process can be obtained by acquiring the target base station in the road network and the key nodes arranged in the road network, combining the target base station with the nodes to construct a road network base station matching model, then acquiring real-time mobile signaling data generated in the user moving process, analyzing and extracting base station switching sequence information connected with the mobile terminal of the user, performing positioning matching and path fitting on the base station switching sequence information and the constructed road network base station matching model, and performing OD track segmentation on the base station positioning data sequence. According to the method and the device, based on the mobile positioning technology, the positioning precision of the user in the moving process is improved, so that when the OD track of the user is cut, the more accurate OD track of the user can be obtained.

Optionally, before the OD trajectory splitting process is performed on the base station positioning data sequence, the method further includes:

and performing data desensitization and sequencing on the base station positioning data sequences in the user moving process according to the time and the distance in the base station positioning data sequences, performing de-duplication on continuous base stations and performing pruning on the base stations switched by ping pong.

By taking the time and the distance in the base station positioning data sequence as the prefabrication threshold, the obtained base station positioning data sequence is subjected to user information desensitization, the base station positioning data sequence is sequenced according to the time, then successive base stations in the base station positioning data sequence are subjected to de-duplication, the base stations switched by ping pong are subjected to deletion and other treatments, so that an effective base station positioning data sequence can be obtained, data noise reduction is realized, and the accuracy of subsequent calculation is ensured.

Optionally, the performing OD trajectory segmentation processing on the base station positioning data sequence includes:

according to a plurality of positioning points of the base station positioning data sequence, a first OD starting point and a first OD precursor point of a user trip OD track are respectively determined, wherein the first OD precursor point is a positioning point with definite position positioned at a moment before a positioning point at the current moment of a user;

Respectively calculating the time interval from the positioning point of the current time of the user to the first OD starting point, the distance interval from the positioning point of the current time of the user to the first OD front point and the time interval from the positioning point of the current time of the user to the first OD front point;

determining behavior states of a user at the current moment according to the calculated time interval and distance interval, wherein the behavior states comprise a stagnation state, a moving state and a free state;

when the current moment of the user is in a stagnation state and the minimum length threshold of the moving OD track is met, the positioning point sequence from the first OD starting point to the first OD precursor point is segmented into a first trip OD track of the user.

From the above, since the obtained base station positioning data sequence is actually a plurality of positioning points in time sequence, the OD starting point and the OD precursor point of the user's travel OD track can be respectively determined according to the plurality of positioning points, the OD starting point and the OD precursor point are not fixed positioning points, but positioning points which can be adjusted in real time according to the behavior state of the user, and the OD precursor point is a positioning point with definite position adjacent to the positioning point at the current moment of the user, so that when the OD track is segmented, definite positioning data can be provided. The method comprises the steps of respectively calculating the time interval and the distance interval between a locating point at the current moment of a user and a determined OD starting point and an OD precursor point, comparing the time interval and the distance interval with a set time interval threshold and a set distance interval threshold, determining that the current behavior state of the user is in a stagnation state, a moving state or a free state according to a comparison result, and dividing a locating point sequence between the determined OD starting point and the determined OD precursor point into travel OD tracks of the user when the current moment of the user is in the stagnation state.

Optionally, the determining the behavior state of the user according to the calculated time interval and distance interval includes:

when the time interval from the positioning point of the current time of the user to the first OD preamble point is larger than or equal to a time interval threshold, or when the distance interval from the positioning point of the current time of the user to the first OD starting point is smaller than the distance interval threshold and the time interval from the positioning point of the current time of the user to the first OD starting point is larger than or equal to the time interval threshold, judging that the current time of the user is in a stagnation state;

when the distance interval from the positioning point at the current time of the user to the first OD starting point is larger than the distance interval from the positioning point at the current time of the user to the first OD preamble point and is larger than the minimum unit threshold of the moving distance, judging that the current time of the user is in a moving state;

when the user does not meet the judging conditions of the stagnation state and the moving state, the user is judged to be in a free state at the current moment.

By calculating the time interval and the distance interval between the positioning point at the current moment of the user and the determined OD starting point and OD preamble point respectively, and comparing the time interval and the distance interval with the set time interval threshold and the distance interval threshold, the current behavior state of the user can be determined to be in a stagnation state, a moving state or a free state according to the comparison result, when the current moment of the user is in the stagnation state, the travel OD track of the user can be obtained by segmentation, and when the user is in the moving state or the free state, the OD track segmentation is not performed temporarily until the behavior state of the user is in the stagnation state.

Optionally, when the current time of the user is in a free state, performing OD trajectory segmentation processing on the base station positioning data sequence further includes:

returning to the step of determining a first OD starting point and a first OD precursor point of the user travel OD track, re-determining the first OD precursor point determined previously as the first OD starting point of the user travel OD track, and re-determining the positioning point at the current moment of the user as the first OD precursor point of the user travel OD track;

and determining the behavior state of the next moment of the user according to the time interval and the distance interval between the positioning point of the next moment of the user and the redetermined first OD starting point and the first OD precursor point.

When the user does not meet the judging conditions of the stagnation state and the moving state, the user can be judged to be in a free state at the current moment, at the moment, the OD starting point and the OD precursor point of the user trip OD track are required to be determined again, the calculating step and the behavior state judging step are repeated, and the time interval and the distance interval between the positioning point of the user at the next moment and the redetermined OD starting point and OD precursor point are calculated so as to judge the behavior state of the user at the next moment until the user is in the stagnation state, and then the segmentation of the OD track can be started.

Optionally, after the splitting to obtain the first OD track of the user, the method further includes:

and determining a second OD starting point and a second OD precursor point of a second travel OD track of the user according to the positioning point of the user at the current moment, and determining the second travel OD track of the user according to the positioning point of the user at the next moment.

From the above, after the segmentation of the first travel OD track of the user is completed, the positioning point of the user at the current moment can be determined to be the second OD starting point and the second OD precursor point of the second travel OD track of the user, and then the segmentation process of the second travel OD track is executed.

Optionally, the constructing the road network base station matching model includes:

and constructing a road network directed graph adjacency matrix by taking key points and road sampling points in a road network as nodes, and associating the nodes with the target base station to obtain the road network base station matching model.

By the above, through regard key point and road sampling point in the road network as the node, construct the directed graph adjacent matrix, and carry on 1 with the base station that screens each node: n is associated, so that a road network base station matching model is obtained, the directed graph adjacent matrix is of a linear structure and consists of a plurality of points and a plurality of edges, any two points can be connected by connecting wires, and the plurality of nodes are constructed into the directed graph adjacent matrix so as to facilitate the follow-up path fitting algorithm to fit paths of any two nodes.

Optionally, performing positioning matching and path fitting on the base station switching sequence information and the road network base station matching model includes:

obtaining two nodes adjacent in time matched in the moving process of a user according to the base station switching sequence information and a road network base station matching model;

and obtaining the path information between the two nodes through fitting by a path fitting algorithm so as to obtain the moving path track of the user.

And according to the constructed road network base station matching model, namely the directed graph adjacency matrix, the path information between the two adjacent nodes in time is obtained through fitting by a path fitting algorithm, and the shortest path information is selected as a moving path track of the user, wherein the moving path track is the base station positioning data sequence.

In a second aspect, the present application provides a travel OD segmentation device based on mobile positioning, including:

the construction module is used for constructing a road network base station matching model according to a target base station in the road network and nodes arranged in the road network;

The analysis module is used for acquiring real-time mobile signaling data of a user and analyzing and extracting base station switching sequence information according to the real-time mobile signaling data;

the matching module is used for carrying out positioning matching on the base station switching sequence information and the road network base station matching model to obtain a base station positioning data sequence in the user moving process;

and the segmentation module is used for carrying out OD track segmentation processing on the base station positioning data sequence to obtain a travel OD track of the user.

In a third aspect, the present application provides a computing device comprising:

a processor;

a memory for storing one or more programs;

and when the one or more programs are executed by the processor, the processor is caused to implement the travel OD segmentation method based on mobile positioning.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a computer, implements the travel OD segmentation method based on mobile positioning described above.

These and other aspects of the application will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

Drawings

Fig. 1 is a flowchart of a travel OD segmentation method based on mobile positioning according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a directed graph adjacency matrix;

fig. 3 is a schematic diagram of a communication structure of a plurality of nodes according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a table of storage structures of adjacent nodes according to an embodiment of the present application;

fig. 5 is a schematic diagram of an OD trajectory splitting process according to an embodiment of the present application;

fig. 6 is a block diagram of a travel OD segmentation device based on mobile positioning according to the embodiment of the present application;

fig. 7 is a block diagram of a computing device according to an embodiment of the present application.

It should be understood that in the foregoing structural schematic diagrams, the sizes and forms of the respective block diagrams are for reference only and should not constitute an exclusive interpretation of the embodiments of the present application. The relative positions and inclusion relationships between the blocks presented by the structural diagrams are merely illustrative of structural relationships between the blocks, and are not limiting of the physical connection of the embodiments of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings.

The embodiment of the application provides a travel OD segmentation method based on mobile positioning, which screens and segments an OD track of a user through algorithms such as a road network base station matching model, track fitting, OD segmentation and the like so as to obtain an accurate travel OD track of the user. As shown in fig. 1, the travel OD segmentation method based on mobile positioning provided in the embodiment of the present application includes the steps of:

S110: constructing a road network base station matching model according to a target base station in the road network and nodes arranged in the road network;

in this embodiment, the road network base station signal acquisition terminal may be used to acquire base station coverage information and positioning longitude and latitude information of the road network, and select the target base station in the road network according to the base station coverage information and positioning longitude and latitude information. The positioning longitude and latitude information is used for determining the specific longitude and latitude of each base station of the acquired base station coverage information. The base station in this embodiment may specifically be a base station supporting mobile communications, such as a 4G base station or a 5G base station.

Before the acquired base station coverage information is processed in the next step, firstly screening the acquired base station coverage information, and screening out base stations with good signal coverage and strong signal quality according to the base station direction angle, the base station position, the distance between the base station and the road network acquisition point and the base station transmitting power in the base station coverage information; then further screening out base stations with strong signal indexes from the base stations with good signal coverage according to the signal indexes of the base stations; and obtaining the base stations with good signal coverage and strong signal indexes through screening to be used for constructing a road network base station matching model. The base station signal index in this embodiment may specifically include one or more of SINR (Signal to Interference plus Noise Ratio, signal-to-interference plus noise ratio), RSRP (Reference Signal Receiving Power, reference signal received power), RSRQ (Reference Singnal Received Quality, reference signal received quality), RSSI (Received Singnal Strengthen Indicator, received signal strength indication).

Constructing a directed graph adjacency matrix by taking key points (malls, parks, intercommunication intersections and the like) in a road network and road sampling points with different distances as nodes, and carrying out 1 on each node and a screened base station: and N association is carried out, so that a road network base station mapping relation is obtained and used as a road network base station matching model, and the road network base station matching model is imported into a road network model database to provide data structure support for a subsequent path fitting algorithm.

As shown in fig. 2, the directed graph adjacency matrix constructed in this embodiment is a linear structure, and is composed of m nodes (key points and road sampling points) and n sides (road sections and roads), any two points can be connected by a connecting line, and the multiple nodes are constructed into the directed graph adjacency matrix, so that a subsequent path fitting algorithm can be used for fitting paths of any two nodes. The weight of the edge in the initially constructed directed graph adjacent matrix (namely the average running time weight of the adjacent nodes) is 1, and the weight can be updated periodically along with the number of the fitted path tracks and the running time between the adjacent nodes so as to perform deep learning on the directed graph adjacent matrix and optimize model precision.

S120: acquiring real-time mobile signaling data of a user, and analyzing and extracting base station switching sequence information according to the real-time mobile signaling data;

When a user moves in a road network, a mobile terminal carried by the user can send real-time mobile signaling data to a data acquisition end through the mobile network, and the data acquisition end can analyze and extract base station switching sequence information from the received real-time mobile signaling data.

The real-time mobile signaling data may be specifically core network data of an S1-MME and an S1-U interface generated at the 4G side, or core network data of an N1 interface generated at the 5G side.

S130: performing positioning matching and path fitting on the base station switching sequence information and a road network base station matching model to obtain a base station positioning data sequence in the user moving process;

in this step, according to the above extracted base station switching sequence information and the road network base station matching model, two nodes with adjacent time matched in the moving process of the user are obtained by using the road network base station matching model, all paths between the two nodes are obtained by using a path fitting algorithm according to the road network directed graph adjacent matrix where the two nodes with adjacent time are located, when the two nodes are continuous in the road network directed graph adjacent matrix, the shortest path can be directly selected as the moving track (base station positioning data sequence) of the user from all paths obtained by the fitting, when the two nodes are discontinuous in the road network directed graph adjacent matrix, similarity calculation, namely variance calculation, can be performed according to the moving time and average moving time weight between adjacent nodes of each path, and the path with the smallest variance is selected as the moving track of the user.

As shown in the schematic diagram of the communication structure of the plurality of nodes shown in fig. 3, the fig. 3 includes 8 nodes with serial numbers of 0-7, and when it is assumed that all paths from node 3 to node 6 in the fig. 3 need to be fitted, node 3 may be taken as a starting point, node 6 may be taken as an ending point, and an adjacent node storage structure table shown in fig. 4 may be created, where the storage structure table specifically includes: a stack for storing paths, an array for storing marked nodes;

based on the multiple nodes shown in fig. 3 and the contiguous node storage structure table shown in fig. 4, the step of fitting all paths from node 3 to node 6 using the depth-first traversal (Depth First Search, DFS) algorithm in this embodiment includes:

step 1: creating a stack structure of a storage node in the adjacent node storage structure table, stacking the node 3 serving as a starting point, and marking the node 3 as a stacking state;

step 2: starting from the node 3, finding a first adjacent node 1 in a non-stacking state of the node 3, and marking the node 1 as a stacking state;

step 3: starting from the node 1, finding a neighboring node 0 of the first non-stacking state of the node 1, and marking the node 0 as a stacking state;

step 4: starting from the node 0, finding a neighboring node 2 of the first non-stacking state of the node 0, and marking the node 2 as a stacking state;

Step 5: starting from the node 2, finding a neighboring node 5 of the first non-stacking state of the node 2, and marking the node 5 as a stacking state;

step 6: starting from the node 5, finding a neighboring node 6 of the first non-stacking state of the node 5, and marking the node 6 as a stacking state;

step 7: since the stack top node 6 is the end point, a path from the start point 3 to the end point 6 is found and stored;

step 8: ejecting the node 6 from the stack top, and marking the node 6 as a non-push state;

step 9: at present, the node at the stack top is 5, and the node 5 is not in a non-stacking state except for the end point 6, so the node 5 is popped from the stack top and marked as a non-stacking state;

step 10: at present, the node at the stack top is 2, and the node 2 is provided with a node 6 in a non-stacking state besides the node 5 just popped, so that the node 6 is stacked;

step 11: the node at the top of the stack is 6, so that a second path is found, the whole stack is output, and the path is stored;

step 12: repeating the steps 2-11 to find all paths from the starting point 3 to the end point 6;

step 13: when the stack is empty, the algorithm ends.

Based on the graphs shown in fig. 3 and fig. 4, the present embodiment can screen all paths between two adjacent nodes (node 3 and node 6) according to the obtained road network directed graph adjacent matrix where the two adjacent nodes are located, and when the two adjacent nodes are continuous adjacent nodes in the road network directed graph adjacent matrix, the shortest path can be selected from all paths as a final user movement track, so as to obtain a base station positioning data sequence in the user movement process.

S140: and carrying out OD track segmentation processing on the base station positioning data sequence to obtain a travel OD track of the user.

In the step S130, the user movement track obtained by performing positioning matching and path fitting on the base station switching sequence information and the road network base station matching model is actually a base station positioning data sequence formed by positioning points of a plurality of base stations accessed in the user movement process, and because part of road section base stations are densely distributed, a situation that a user accesses a plurality of base stations at the positioning points may occur, therefore, before performing OD track segmentation processing on the base station positioning data sequence, the embodiment performs user information desensitization on the obtained base station positioning data sequence by taking the time and the distance in the base station positioning data sequence as a pre-fabricated threshold, and performs sorting according to the time, then performs de-duplication on continuous base stations in the base station positioning data sequence, and performs deletion and other processing on the base stations switched by ping-pong, thereby obtaining the base station positioning data sequence after the noise reduction processing, and being convenient for improving the accuracy of subsequent calculation.

As shown in the schematic diagram of the OD trajectory splitting process in fig. 5, assuming that the base station positioning data sequence obtained in this embodiment is composed of 5 positioning points (P1-P5), it is necessary to find all the travel OD trajectories generated by the user during the movement of the 5 positioning points. Specifically, the OD trajectory splitting process for the base station positioning data sequence in this step may include:

S141: according to a plurality of positioning points (P1-P5) of the base station positioning data sequence, a first OD starting point and a first OD preamble point of a user trip OD track are respectively determined, for example, when a positioning point (cp) at the current moment of the user is a positioning point P2 of the base station LA2, the positioning point P1 of the base station LA1 can be set as a first OD starting point (hp), and the positioning point P1 of the base station LA1 can be set as a first OD preamble point (pp).

The OD starting point and the OD preamble point are not fixed positioning points, but positioning points which can be adjusted in real time according to the behavior state of the user, and the OD preamble point is a positioning point with a definite position adjacent to the positioning point at the current moment of the user, so that when the OD track is divided, explicit positioning data is provided, for example, when the user enters a mall or park, a plurality of base stations (for example, LA3, LA4, LA 5) may be accessed successively, and at this time, a positioning point (for example, P3 positioning point where LA3 base station is located) of the user in the mall or park may be used as the OD preamble point, without recording the positioning point of each base station of the user in the area.

And setting and judging a prefabricated threshold value for user movement and stagnation according to the interval position of the base station so as to determine the behavior state of the user, for example, setting a time interval threshold value as a stayT, setting a distance interval threshold value as a stayD, setting a minimum unit threshold value of the movement distance as a minMD and setting a minimum length threshold value of a movement OD track as a minOD.

S142: a time interval (hcT) from the user current time anchor point (cp) to the first OD starting point (hp), a distance interval (chD) from the user current time anchor point (cp) to the first OD starting point (hp), a distance interval (cpD) from the user current time anchor point (cp) to the first OD preamble point (pp), and a time interval (cpT) from the user current time anchor point (cp) to the first OD preamble point (pp) are calculated, respectively.

S143: and determining the behavior state of the user at the current moment according to the calculated time interval and distance interval, wherein the behavior state comprises a stagnation state, a moving state and a free state. The specific judging process is as follows:

determining that the user current time is in a stalled state when the time interval (cpT) from the user current time anchor point (cp) to the first OD preamble point (pp) is greater than or equal to the time interval threshold value stayT, or when the distance interval (chD) from the user current time anchor point (cp) to the first OD start point (hp) is less than the distance interval threshold value stayD and the time interval (hcT) from the user current time anchor point (cp) to the first OD start point (hp) is greater than or equal to the time interval threshold value;

when the current time of the user is in a stagnation state and the minimum length threshold minOD of the moving OD track is met, the positioning point sequence (p 1, p2, p 3) from the first OD starting point p1 to the first OD preamble point p3 is segmented into a first going OD track of the user. And then determining a second OD starting point (e.g. p 4) and a second OD precursor point of a second trip OD track of the user according to the positioning point of the current moment of the user.

When the distance interval (chD) from the positioning point (cp) at the current time of the user to the first OD starting point (hp) is greater than the distance interval (cpD) from the positioning point (cp) at the current time of the user to the first OD leading point (pp) and greater than the minimum unit threshold value minMD of the moving distance, the step S141 is returned to, and the positioning point at the current time of the user is redetermined as the first OD leading point of the user, and then the behavior state of the next time of the user is continuously determined until the user is in a stagnation state, so that the segmentation of the OD track can be executed.

When the user does not meet the judging conditions of the stagnation state and the moving state, it is judged that the current time of the user is in the free state, at this time, step S141 can be returned, the previously determined first OD preamble point is redetermined as the first OD starting point of the user trip OD track, the current time of the user is redetermined as the first OD preamble point of the user trip OD track, and the behavior state of the next time of the user is continuously determined according to the time interval and the distance interval between the next time of the user and the redetermined first OD starting point and the first OD preamble point until the user is in the stagnation state, and then the segmentation of the OD track can be executed.

And repeating the steps S141-143, so that all the travel OD tracks actually generated by the user in the base station positioning data sequence can be obtained by segmentation.

In summary, according to the travel OD segmentation method based on mobile positioning provided by the embodiment of the application, the target base station in the road network and the key node set in the road network are obtained, the target base station and the node are combined and constructed to obtain the road network base station matching model, then the real-time mobile signaling data generated in the moving process of the user are obtained, the base station switching sequence information connected with the mobile terminal of the user is analyzed and extracted, the base station switching sequence information and the constructed road network base station matching model are subjected to positioning matching and path fitting, so that the base station positioning data sequence in the moving process of the user can be obtained, and each travel OD track generated in the moving process of the user can be obtained by carrying out OD track segmentation on the base station positioning data sequence. According to the method and the device, based on the mobile positioning technology, the positioning precision of the user in the moving process is improved, so that when the OD track of the user is cut, the more accurate OD track of the user can be obtained.

Fig. 6 shows a block diagram of a travel OD segmentation device based on mobile positioning according to the embodiment of the present application, where the device may be used to implement any step of the travel OD segmentation method based on mobile positioning and an alternative embodiment thereof. Referring to fig. 6, the apparatus includes a construction module 201, an analysis module 202, a matching module 203, and a segmentation module 204;

The construction module 201 is configured to construct a road network base station matching model according to a target base station in the road network and a node set in the road network; the analysis module 202 is used for acquiring real-time mobile signaling data of a user, and analyzing and extracting base station switching sequence information according to the real-time mobile signaling data; the matching module 203 is configured to perform positioning matching on the base station switching sequence information and a road network base station matching model, so as to obtain a base station positioning data sequence in a user moving process; the segmentation module 204 is configured to perform an OD track segmentation process on the base station positioning data sequence, so as to obtain a trip OD track of the user.

It should be understood that the apparatus or module in the embodiments of the present application may be implemented by software, for example, by a computer program or instruction having the functions described above, and the corresponding computer program or instruction may be stored in a memory inside the terminal, and the processor reads the corresponding computer program or instruction inside the memory to implement the functions described above. Alternatively, the apparatus or module of the embodiments of the present application may be implemented by hardware. Still further, an apparatus or module in an embodiment of the present application may also be implemented by a combination of a processor and software modules.

It should be understood that, for details of processing of the apparatus or the module in the embodiments of the present application, reference may be made to the embodiments shown in fig. 1 to 5 and related expressions of related extended embodiments, and the embodiments of the present application will not be repeated here.

Fig. 7 is a schematic diagram of a computing device 1000 provided by an embodiment of the present application. The computing device 1000 includes: processor 1010, memory 1020, communication interface 1030, bus 1040.

It should be appreciated that the communication interface 1030 in the computing device 1000 shown in fig. 7 may be used to communicate with other devices.

Wherein the processor 1010 may be coupled to a memory 1020. The memory 1020 may be used to store the program codes and data. Accordingly, the memory 1020 may be a storage unit internal to the processor 1010, an external storage unit independent of the processor 1010, or a component including a storage unit internal to the processor 1010 and an external storage unit independent of the processor 1010.

Optionally, the computing device 1000 may also include a bus 1040. The memory 1020 and the communication interface 1030 may be connected to the processor 1010 through a bus 1040. The bus 1040 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus 1040 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one line is shown in fig. 7, but not only one bus or one type of bus.

It should be appreciated that in embodiments of the present application, the processor 1010 may employ a central processing unit (central processing unit, CPU). The processor may also be other general purpose processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), field programmable gate arrays (field programmable gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. Or the processor 1010 may employ one or more integrated circuits for executing associated programs to carry out the techniques provided by embodiments of the present application.

The memory 1020 may include read only memory and random access memory and provide instructions and data to the processor 1010. A portion of the processor 1010 may also include non-volatile random access memory. For example, the processor 1010 may also store information of the device type.

When the computing device 1000 is running, the processor 1010 executes computer-executable instructions in the memory 1020 to perform the operational steps of the methods described above.

It should be understood that the computing device 1000 according to the embodiments of the present application may correspond to a respective subject performing the methods according to the embodiments of the present application, and that the above-described other operations and/or functions of the respective modules in the computing device 1000 are respectively for implementing the respective flows of the methods of the embodiments, and are not described herein for brevity.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program for performing the above-described method when executed by a processor, the method comprising at least one of the aspects described in the above-described embodiments.

Any combination of one or more computer readable media may be employed as the computer storage media of the embodiments herein. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

It should be noted that the embodiments described in this application are only some embodiments of the present application, and not all embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures, may be arranged and designed in a wide variety of different configurations. Thus, the above detailed description of the embodiments of the present application, provided in the accompanying drawings, is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.

The terms first, second, third, etc. or module a, module B, module C, etc. in the description and in the claims, etc. are used solely for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, as may be appreciated, if permitted, to interchange particular orders or precedence orders to enable embodiments of the present application described herein to be implemented in orders other than those illustrated or described herein.

In the above description, reference numerals indicating steps are not necessarily meant to be performed as such, but intermediate steps or replaced by other steps may be included, and the order of the steps may be interchanged or performed simultaneously where permitted.

The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Thus, it should be interpreted as specifying the presence of the stated features, integers, steps or components as referred to, but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. Thus, the expression "a device comprising means a and B" should not be limited to a device consisting of only components a and B.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, in the various embodiments of the application, where no special description or logic conflicts exist, the terms and/or descriptions between the different embodiments are consistent and may be mutually referenced, the technical features of the different embodiments may be combined to form a new embodiment according to their inherent logic relationships.

Note that the above is only the preferred embodiments of the present application and the technical principles applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the present application has been described in connection with the above embodiments, the present invention is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present invention, and the present invention is also within the scope of protection.

Claims

1. The travel OD segmentation method based on mobile positioning is characterized by comprising the following steps of:

2. The method of claim 1, wherein prior to the OD trajectory splitting of the base station positioning data sequence, further comprising:

3. The method of claim 1, wherein the OD trajectory slicing the base station positioning data sequence comprises:

4. A method according to claim 3, wherein said determining the behavior state of the user based on the calculated time interval and distance interval comprises:

5. The method of claim 4, wherein performing an OD trajectory slicing process on the base station positioning data sequence when the user is currently in a free state further comprises:

6. The method according to any one of claims 3 to 5, wherein after the splitting to obtain the first occurrence OD trajectory of the user, further comprising:

7. The method of claim 1, wherein the constructing a road network base station matching model comprises:

8. Travel OD segmentation device based on mobile positioning, characterized by comprising:

9. A computing device, comprising:

a processor;

a memory for storing one or more programs;

the one or more programs, when executed by the processor, cause the processor to implement a mobile positioning based travel OD segmentation method as claimed in any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, which when executed by a computer implements a travel OD segmentation method based on mobile positioning according to any one of claims 1 to 7.