CN114238533A - User commuting path planning method and device, computer equipment and storage medium - Google Patents
User commuting path planning method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a user commuting path planning method, a user commuting path planning device, computer equipment and a storage medium. The method comprises the following steps: acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user; extracting a residence point of a user by adopting a layered residence algorithm according to the measurement report data; matching the residence points with the traffic cells, judging the job and live characteristics of the residence points, and determining the job and live residence points of the users according to the matching results and the job and live characteristics; and extracting the commuting track of the user according to the job stop point, and planning the commuting path of the user according to the commuting track. According to the technical scheme provided by the embodiment of the invention, the problem of boundary spreading of the traditional algorithm is reduced by using the layered resident algorithm, the accuracy of commuting data is improved, the operation complexity and time consumption of path matching and planning are reduced on the whole, and the layered algorithm can be used for performing layered adjustment on parameters, so that the problem of overlarge influence on parameter adjustment is reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of data mining, in particular to a user commuting path planning method and device, computer equipment and a storage medium.
Background
At present, basic people in most areas of China have mobile phones, the coverage rate of 4G users exceeds 80% of the total population of the whole country, huge user groups can generate huge mobile phone signaling data, the mobile phone signaling data are used for acquiring the positions of the users and have the characteristics of real-time performance, direct connection with the users, low cost and the like, MR data have the characteristic of periodic measurement, and the mobile phone signaling data have the terminal longitude and latitude based on a preposed positioning algorithm. The analysis of user's orbit, job and live and commute etc. can understand resident's demand of going on a journey, provide data support to traffic system model, traffic planning, urban management etc..
The analysis method based on the dbscan algorithm is provided in the prior art, and compared with a traditional investigation mode, the analysis method is richer in means and quicker in timeliness, but the problems of resident point boundary spreading and the like exist, so that the analysis result has larger error with the reality and lower accuracy.
Disclosure of Invention
The embodiment of the invention provides a user commuting path planning method, a user commuting path planning device, computer equipment and a storage medium, which are used for reducing the problem of boundary spreading of a traditional algorithm and improving the accuracy of commuting data.
In a first aspect, an embodiment of the present invention provides a method for planning a commute path of a user, where the method includes:
acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user;
extracting a residence point of a user by adopting a layered residence algorithm according to the measurement report data;
matching the residence point with the traffic cell, judging the job and residence characteristics of the residence point, and determining the job and residence stop point of the user according to the matching result and the job and residence characteristics;
and extracting the commuting track of the user according to the job stop point, and planning the commuting path of the user according to the commuting track.
Optionally, the extracting the residence point of the user by using a hierarchical residence algorithm according to the measurement report data includes:
determining user track points according to the measurement report data;
merging the user track points based on a fine-grained spatio-temporal dbscan density aggregation algorithm to obtain initial stop points;
merging the preliminary stop points based on a coarse granularity space dbscan density aggregation algorithm to obtain basic stop points;
and judging the basic stopping point according to a preset rule so as to determine the stopping point.
Optionally, the determining the basic stop point according to a preset rule to determine the stop point includes:
extracting dwell point features in the basic dwell points, wherein the dwell point features comprise intra-point time intervals, space distances, speeds, and directions, time intervals and space distances between adjacent points;
and based on the preset rule, filtering and combining the basic stop points according to the stop point characteristics to obtain the stop points.
Optionally, the matching the residence point with the traffic cell, and determining the occupation feature of the residence point to determine the occupation residence point of the user according to the matching result and the occupation feature include:
dividing the occupation characteristics of the residence points according to a preset time period rule;
respectively merging the residence points matched with the same traffic cell, and determining the occupation traffic cells of the user according to the time ratio of the residence points in each traffic cell and the occupation characteristics;
and matching the residence points according to the occupational traffic cells to obtain the occupational residence points.
Optionally, after determining the occupation traffic cell of the user according to the time ratio of the residence point in each traffic cell and the occupation feature, the method further includes:
and determining the longitude and latitude of each working traffic cell according to the time duty ratio of each residence point in each working traffic cell, and updating the working information of the user according to the longitude and latitude.
Optionally, the extracting a commuting track of the user according to the job stop point and planning a commuting path of the user according to the commuting track includes:
and matching points on the commuting track to a road network graph according to the shortest path, and determining the commuting path according to the commuting track based on the Dijkstra algorithm.
Optionally, the acquiring a traffic cell includes:
acquiring a boundary network map of the traffic cell;
and performing thinning on the boundary network map based on a Douglas algorithm to obtain the traffic cell.
In a second aspect, an embodiment of the present invention further provides a device for planning a commute path of a user, where the device includes:
the data acquisition module is used for acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user;
the resident point extracting module is used for extracting the resident points of the users by adopting a layered resident algorithm according to the measurement report data;
the system comprises a traffic community determining module, a job stop point determining module and a user position determining module, wherein the traffic community determining module is used for matching the residence point with the traffic community and judging the job characteristics of the residence point so as to determine the job stop point of the user according to the matching result and the job characteristics;
and the commuting path planning module is used for extracting the commuting track of the user according to the job stop point and planning the commuting path of the user according to the commuting track.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the user commuting path planning method provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the user commuting path planning method provided in any embodiment of the present invention.
The embodiment of the invention provides a user commuting path planning method, which comprises the steps of firstly obtaining a traffic cell and measurement report data reported by a mobile terminal used by a user, then extracting residence points of the user by adopting a layered residence algorithm according to the measurement report data, then matching the obtained residence points with the traffic cell, and judging the position characteristics of the residence points, thereby determining the position of the user according to the matching result and the position characteristics, finally extracting a commuting track of the user according to the obtained position of the residence points, and planning the commuting path of the user according to the commuting track. According to the user commuting path planning method provided by the embodiment of the invention, the problem of boundary spreading of the traditional algorithm is reduced by using the layered resident algorithm, the accuracy of commuting data is improved, meanwhile, the operation complexity and time consumption of path matching and planning are reduced on the whole, and the layered algorithm can be used for performing layered adjustment on parameters, so that the problem of overlarge influence on parameter adjustment is reduced.
Drawings
Fig. 1 is a flowchart of a user commuting path planning method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a user commuting path planning apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of a user commuting path planning method according to an embodiment of the present invention. The embodiment is applicable to the case of analyzing the user commuting data based on the measurement report data, and the method may be performed by the user commuting path planning apparatus provided in the embodiment of the present invention, which may be implemented by hardware and/or software, and may be generally integrated in a computer device. As shown in fig. 1, the method specifically comprises the following steps:
and S11, obtaining the measurement report data reported by the traffic cell and the mobile terminal used by the user.
Specifically, the traffic cell is a minimum unit of space defined for research and analysis of residents, vehicle traveling and distribution, and may be determined according to a traffic cell area map. In order to ensure mobility in a service connection state, the mobile terminal performs periodic Measurement to obtain a Measurement Report (MR) and reports the Measurement Report in real time, so that Measurement Report data reported by the mobile terminal can be obtained. Optionally, the acquiring a traffic cell includes: acquiring a boundary network map of the traffic cell; and performing thinning on the boundary network map based on a Douglas algorithm to obtain the traffic cell. Specifically, a traffic cell boundary network map of an area (such as a city, an area and the like) where a user is located can be taken from existing data, then a proper rarefying threshold (threshold) is selected to rarefy the boundary network map based on a douglas algorithm, so that a required traffic cell is obtained.
And S12, extracting the residence point of the user by adopting a layered residence algorithm according to the measurement report data.
Specifically, after measurement report data is acquired, a layered residence algorithm can be adopted to extract residence points of a user, wherein the layers are extracted for multiple times, specifically, outlier track points can be filtered firstly, and then adjacent track points are combined to further judge and obtain the residence points.
Optionally, the extracting the residence point of the user by using a hierarchical residence algorithm according to the measurement report data includes: determining user track points according to the measurement report data; merging the user track points based on a fine-grained spatio-temporal dbscan density aggregation algorithm to obtain initial stop points; merging the preliminary stop points based on a coarse granularity space dbscan density aggregation algorithm to obtain basic stop points; and judging the basic stopping point according to a preset rule so as to determine the stopping point. Specifically, after the measurement report data is acquired, the measurement report data may be preprocessed to extract key fields therein and group the key fields according to users, and then the internet of things network card users, drift data, ping-pong data and the like are removed, wherein the position information in the measurement report data uploaded each time may be a track point. Then, tracks with higher time-space polymerization degree based on fine-granularity space-time dbscan density polymerization algorithmAnd combining the points to obtain initial stay points, filtering outlier track points, combining adjacent track points based on a coarse granularity space dbscan density aggregation algorithm to obtain basic stay points, and finally judging the basic stay points according to a preset rule to determine stay points with commuting significance. The space-time dbscan density aggregation algorithm can be realized by the time distance and the space distance between the trace points at the same time, the space dbscan density aggregation algorithm can be realized by the space distance between the trace points only, and the trace points P ═ { P ═ P1,p2,…,pi,…,pn}，pi(userid, lon, lat, timestamp), wherein piFor the ith track point, userid is the user identifier, lon is longitude, lat is latitude, timestamp is timestamp, then each track point can be ordered according to time, and the time distance and the space distance between two track points can be calculated through the following formula:
C＝sin(lon1)*sin(lon2)*cos(lon1-lon2)+cos(lat1)*cos(lat2)
Sdis＝R*Arccos(C)*PI/180
Tdis＝time2-time1
wherein, C represents a distance intermediate parameter, lon1 represents the longitude of the first track point, lon2 represents the longitude of the second track point, lat1 represents the latitude of the first track point, lat2 represents the latitude of the second track point, Sdis represents a spatial distance, R represents the earth radius, 6371 km can be taken, PI represents the circumference ratio, Tdis represents the time distance, time1 represents the time stamp of the first track point, and time2 represents the time stamp of the second track point. The dbscan density aggregation algorithm can be adjusted by setting a space distance parameter, wherein the space distance parameter can be selected according to a preset proportion based on the maximum value of the distance between the initial stopping points of a single user on the same day, the dbscan density aggregation algorithm can determine the track points which satisfy the condition that the surrounding distance is less than or equal to the set space distance parameter and the number of the track points is a point parameter (MinPts) as core points, the track points which have the distance from the core points less than or equal to the set space distance parameter are determined as reachable points, and the core points and the corresponding reachable points can be aggregated into the same cluster. The different particle sizes are mainly embodied in the selection of space distance parameters and point number parameters, the fine-grained space distance parameters are small, the point number parameters are large, the coarse-grained space distance parameters are large, and the point number parameters are small. When the spatial dbscan density aggregation algorithm is adopted for merging, aggregation cannot be carried out across time so as to ensure the continuity in time.
On the basis of the above technical solution, optionally, the determining the basic stop point according to a preset rule to determine the stop point includes: extracting dwell point features in the basic dwell points, wherein the dwell point features comprise intra-point time intervals, space distances, speeds, and directions, time intervals and space distances between adjacent points; and based on the preset rule, filtering and combining the basic stop points according to the stop point characteristics to obtain the stop points. Specifically, after the basic stopping points are obtained, the stopping point features can be extracted, then the basic stopping points can be filtered according to the intra-point features and the inter-point direction features based on the preset rules, and the basic stopping points are merged according to the inter-point features, so that the stopping points are obtained.
S13, matching the residence point with the traffic cell, judging the occupation feature of the residence point, and determining the occupation residence point of the user according to the matching result and the occupation feature.
Specifically, after the stay points are obtained, the stay points and the traffic cells can be matched by adopting a horizontal ray method, and meanwhile, the stay characteristics of the stay points can be judged according to daily rules, so that the stay points of the users can be determined according to the matching results and the stay characteristics.
Optionally, the matching the residence point with the traffic cell, and determining the occupation feature of the residence point to determine the occupation residence point of the user according to the matching result and the occupation feature include: dividing the occupation characteristics of the residence points according to a preset time period rule; respectively merging the residence points matched with the same traffic cell, and determining the occupation traffic cells of the user according to the time ratio of the residence points in each traffic cell and the occupation characteristics; and matching the residence points according to the occupational traffic cells to obtain the occupational residence points. Specifically, for each residence point, the residence characteristics may be divided according to a preset time period rule, for example, eight to eighteen hours per day are defined as a working time period, twenty to six hours on the second day are defined as a residence time period, and the like, after matching between the residence points and the traffic cells is completed, residence points corresponding to each traffic cell may be merged, and the residence characteristic with the largest time ratio among the residence points is determined as the residence characteristic of the corresponding traffic cell according to the residence characteristics of each residence point, so as to determine to obtain each residence traffic cell, and then the residence of each residence point is determined according to the determined residence traffic cell, so as to obtain the residence. The accuracy of the track lines of the jobs and the dwellings and the commutes can be improved by combining the residence points by taking the traffic districts as units.
Further optionally, after determining the occupancy traffic cells of the user according to the time occupancy of the residence point in each of the traffic cells and the occupancy characteristics, the method further includes: and determining the longitude and latitude of each working traffic cell according to the time duty ratio of each residence point in each working traffic cell, and updating the working information of the user according to the longitude and latitude. Specifically, the longitude and latitude of the corresponding working and dwelling traffic districts can be determined according to the longitude and latitude of the dwelling points in each working and dwelling traffic district, the longitude and latitude with the longest time ratio can be determined as the longitude and latitude of the working and dwelling traffic districts, after the working and dwelling traffic districts and the longitude and latitude thereof are determined, the statistical personal working and dwelling information can be updated, and then the dwelling points can be matched according to the updated working and dwelling information to obtain the working and dwelling points. The specific update scheme may be: the method comprises the steps of storing the occupation traffic cells obtained by individuals for a plurality of times (such as 5 times) in a database, increasing the heat of the occupation traffic cell if the occupation traffic cells are matched with the stored occupation traffic cells when new data are obtained every time, removing the occupation traffic cell with the minimum currently stored heat if the occupation traffic cells are not matched, updating according to the new data, and simultaneously reducing the heat in the whole database at intervals of preset time.
And S14, extracting the commuting track of the user according to the job stop point, and planning the commuting path of the user according to the commuting track.
Specifically, after the job stay points are obtained, the time sequencing can be carried out on the job stay points, then the commuting track of the user can be extracted according to the sorted job stay points, the commuting track of the user can be extracted specifically according to the day, and the commuting track of the user is planned according to the commuting track.
Optionally, the extracting a commuting track of the user according to the job stop point and planning a commuting path of the user according to the commuting track includes: and matching points on the commuting track to a road network graph (namely, a road route in real life) according to the shortest path, and determining the commuting path according to the commuting track based on the Dijkstra algorithm.
According to the technical scheme provided by the embodiment of the invention, firstly, the traffic cell and the measurement report data reported by the mobile terminal used by the user are obtained, then the resident point of the user is extracted by adopting a layered resident algorithm according to the measurement report data, the obtained resident point is matched with the traffic cell, and the position feature of the resident point is judged, so that the position and the position stop point of the user are determined according to the matching result and the position feature, finally, the commuting track of the user can be extracted according to the obtained position and the position stop point, and the commuting path of the user is planned according to the commuting track. By using the layered resident algorithm, the problem of boundary spreading of the traditional algorithm is reduced, the accuracy of commuting data is improved, meanwhile, the operation complexity and time consumption of path matching and planning are reduced on the whole, and the layered algorithm can perform layered adjustment on parameters, so that the problem of overlarge influence on parameter adjustment is reduced.
Example two
Fig. 2 is a schematic structural diagram of a user commuting path planning apparatus according to a second embodiment of the present invention, where the apparatus may be implemented in a hardware and/or software manner, and may be generally integrated in a computer device, so as to execute the user commuting path planning method according to any embodiment of the present invention. As shown in fig. 2, the apparatus includes:
a data obtaining module 21, configured to obtain measurement report data reported by a mobile terminal used by a traffic cell and a user;
a resident point extracting module 22, configured to extract a resident point of the user by using a layered resident algorithm according to the measurement report data;
a job stop point determining module 23, configured to match the stop point with the traffic cell, and determine job stop features of the stop point, so as to determine a job stop point of the user according to a matching result and the job stop features;
and the commuting path planning module 24 is used for extracting the commuting path of the user according to the job stop point and planning the commuting path of the user according to the commuting path.
According to the technical scheme provided by the embodiment of the invention, firstly, the traffic cell and the measurement report data reported by the mobile terminal used by the user are obtained, then the resident point of the user is extracted by adopting a layered resident algorithm according to the measurement report data, the obtained resident point is matched with the traffic cell, and the position feature of the resident point is judged, so that the position and the position stop point of the user are determined according to the matching result and the position feature, finally, the commuting track of the user can be extracted according to the obtained position and the position stop point, and the commuting path of the user is planned according to the commuting track. By using the layered resident algorithm, the problem of boundary spreading of the traditional algorithm is reduced, the accuracy of commuting data is improved, meanwhile, the operation complexity and time consumption of path matching and planning are reduced on the whole, and the layered algorithm can perform layered adjustment on parameters, so that the problem of overlarge influence on parameter adjustment is reduced.
On the basis of the above technical solution, optionally, the dwell point extracting module 22 includes:
the track point determining unit is used for determining user track points according to the measurement report data;
a preliminary dwell point obtaining unit, configured to combine the user trace points based on a fine-grained spatiotemporal dbscan density aggregation algorithm to obtain a preliminary dwell point;
a basic stopping point obtaining unit, configured to combine the preliminary stopping points based on a coarse-grained space dbscan density aggregation algorithm to obtain basic stopping points;
and the residence point determining unit is used for judging the basic residence point according to a preset rule so as to determine the residence point.
On the basis of the foregoing technical solution, optionally, the residence point determining unit includes:
the characteristic extraction subunit is used for extracting the characteristic of the resident point in the basic resident point, wherein the characteristic of the resident point comprises the time interval, the space distance, the speed in the point, the direction between adjacent points, the time interval and the space distance;
and the resident point extracting subunit is used for filtering and combining the basic resident points according to the resident point characteristics based on the preset rule so as to obtain the resident points.
On the basis of the above technical solution, optionally, the job stop point determining module 23 includes:
the job feature dividing unit is used for dividing the job features of the residence points according to a preset time period rule;
the position traffic cell determining unit is used for respectively merging the residence points matched with the same traffic cell and determining position traffic cells of the users according to the time ratios of the residence points in the traffic cells and the position characteristics;
and the job stop point matching unit is used for matching the stop points according to the job traffic cell to obtain the job stop points.
On the basis of the above technical solution, optionally, the module 23 for determining a job stop point further includes:
and the position information updating unit is used for determining the position traffic cells of the users according to the time ratios of the residence points in the traffic cells and the position characteristics, respectively determining the longitude and the latitude of each position traffic cell according to the time ratios of the residence points in the traffic cells, and updating the position information of the users according to the longitude and the latitude.
On the basis of the above technical solution, optionally, the commuting path planning module 24 is specifically configured to:
and matching points on the commuting track to a road network graph according to the shortest path, and determining the commuting path according to the commuting track based on the Dijkstra algorithm.
On the basis of the above technical solution, optionally, the data obtaining module 21 includes:
the boundary network map acquisition unit is used for acquiring a boundary network map of the traffic cell;
and the boundary network map thinning unit is used for thinning the boundary network map based on the Douglas algorithm so as to obtain the traffic cell.
The user commuting path planning device provided by the embodiment of the invention can execute the user commuting path planning method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the user commuting path planning apparatus, each included unit and module are only divided according to functional logic, but are not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a computer device provided in the third embodiment of the present invention, and shows a block diagram of an exemplary computer device suitable for implementing the embodiment of the present invention. The computer device shown in fig. 3 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present invention. As shown in fig. 3, the computer apparatus includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of the processors 31 in the computer device may be one or more, one processor 31 is taken as an example in fig. 3, the processor 31, the memory 32, the input device 33 and the output device 34 in the computer device may be connected by a bus or in other ways, and the connection by the bus is taken as an example in fig. 3.
The memory 32 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the user commuting path planning method in the embodiment of the present invention (for example, the data acquisition module 21, the resident point extraction module 22, the job stop point determination module 23, and the commuting path planning module 24 in the user commuting path planning apparatus). The processor 31 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the memory 32, that is, implements the user commuting path planning method described above.
The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 32 may further include memory located remotely from the processor 31, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 33 may be used to obtain measurement report data reported by the traffic cells and the mobile terminals used by the users, and to generate key signal inputs related to user settings and function controls of the computer devices, etc. The output device 34 may include a display screen or the like, which may be used to present the final planning results to the user.
Example four
A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for planning a commuting path of a user, the method including:
acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user;
extracting a residence point of a user by adopting a layered residence algorithm according to the measurement report data;
matching the residence point with the traffic cell, judging the job and residence characteristics of the residence point, and determining the job and residence stop point of the user according to the matching result and the job and residence characteristics;
and extracting the commuting track of the user according to the job stop point, and planning the commuting path of the user according to the commuting track.
The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lambda (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the user commuting path planning method provided by any embodiment of the present invention.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. 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, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A user commuting path planning method is characterized by comprising the following steps:
acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user;
extracting a residence point of a user by adopting a layered residence algorithm according to the measurement report data;
matching the residence point with the traffic cell, judging the job and residence characteristics of the residence point, and determining the job and residence stop point of the user according to the matching result and the job and residence characteristics;
and extracting the commuting track of the user according to the job stop point, and planning the commuting path of the user according to the commuting track.
2. The method for planning a commuting path of a user according to claim 1, wherein the extracting a resident point of the user by using a hierarchical resident algorithm according to the measurement report data comprises:
determining user track points according to the measurement report data;
merging the user track points based on a fine-grained spatio-temporal dbscan density aggregation algorithm to obtain initial stop points;
merging the preliminary stop points based on a coarse granularity space dbscan density aggregation algorithm to obtain basic stop points;
and judging the basic stopping point according to a preset rule so as to determine the stopping point.
3. The method for planning a commuting path of a user according to claim 2, wherein the determining the basic stopping point according to a preset rule to determine the stopping point comprises:
extracting dwell point features in the basic dwell points, wherein the dwell point features comprise intra-point time intervals, space distances, speeds, and directions, time intervals and space distances between adjacent points;
and based on the preset rule, filtering and combining the basic stop points according to the stop point characteristics to obtain the stop points.
4. The method of claim 1, wherein the step of matching the residence point with the traffic cell and determining the occupational characteristics of the residence point to determine the occupational residence point of the user according to the matching result and the occupational characteristics comprises:
dividing the occupation characteristics of the residence points according to a preset time period rule;
respectively merging the residence points matched with the same traffic cell, and determining the occupation traffic cells of the user according to the time ratio of the residence points in each traffic cell and the occupation characteristics;
and matching the residence points according to the occupational traffic cells to obtain the occupational residence points.
5. The method of claim 4, wherein after determining the occupational traffic cells of the user according to the time aspect ratio of the residence point in each of the traffic cells and the occupational characteristics, further comprising:
and determining the longitude and latitude of each working traffic cell according to the time duty ratio of each residence point in each working traffic cell, and updating the working information of the user according to the longitude and latitude.
6. The method of claim 1, wherein the extracting the commute trajectory of the user according to the job stop point and planning the commute path of the user according to the commute trajectory comprises:
and matching points on the commuting track to a road network graph according to the shortest path, and determining the commuting path according to the commuting track based on the Dijkstra algorithm.
7. The method of claim 1, wherein the obtaining a traffic cell comprises:
acquiring a boundary network map of the traffic cell;
and performing thinning on the boundary network map based on a Douglas algorithm to obtain the traffic cell.
8. A user commute path planning apparatus, comprising:
the data acquisition module is used for acquiring measurement report data reported by a traffic cell and a mobile terminal used by a user;
the resident point extracting module is used for extracting the resident points of the users by adopting a layered resident algorithm according to the measurement report data;
the system comprises a traffic community determining module, a job stop point determining module and a user position determining module, wherein the traffic community determining module is used for matching the residence point with the traffic community and judging the job characteristics of the residence point so as to determine the job stop point of the user according to the matching result and the job characteristics;
and the commuting path planning module is used for extracting the commuting track of the user according to the job stop point and planning the commuting path of the user according to the commuting track.
9. A computer device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the user commute path planning method as recited in any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for planning a commute path for a user according to any one of claims 1 to 7.
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| CN116456281A (en) * | 2023-05-12 | 2023-07-18 | 中国电信股份有限公司广东研究院 | Method for determining co-located user based on seed user track and related equipment |
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| CN116456281A (en) * | 2023-05-12 | 2023-07-18 | 中国电信股份有限公司广东研究院 | Method for determining co-located user based on seed user track and related equipment |
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