Method, device and equipment for determining commuting site

Abstract

The invention provides a method, device and equipment for determining a commuting site, and the method comprises the steps: obtaining and processing the living position information of each commuting employee, so as to obtain the geographic position data of each commuting employee, and forming a data set; a density-based spatial clustering algorithm is adopted, clustering processing is carried out on the data set based on preset parameters, a plurality of point clusters are obtained, and one point in each point cluster corresponds to geographic position data of one commuting employee; and determining one or more center points of each point cluster, and taking the geographic positions corresponding to the center points as commuting sites. In the commuting site planning process, the commuting sites can be efficiently and accurately determined, the commuting sites can be dynamically adjusted according to the number or position change of the users, and the user experience can be improved.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a technology for determining commuting sites. Background Art

[0002] For commuter vehicle operators, it is important to arrange commuter stations reasonably to meet user needs. In the prior art, commuter bus operators mostly rely on manual experience to directly determine commuter stations in commuter station planning, or use GIS (Geographic Information System) tools to determine commuter stations. However, manual work requires a lot of time and manpower, and it is difficult to quickly respond to changes in the number and location of commuter users. The use of GIS data may not fully consider the density of commuter user distribution and actual commuting needs, and the determined commuter stations may not be accurate enough, resulting in unreasonable layout and insufficient coverage. Therefore, the existing technical solutions for determining commuter stations have the defects of low efficiency, lack of dynamic adjustment capabilities, and insufficient accuracy, which may cause inconvenience to users and reduce user experience. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device and equipment for determining commuting stations, so as to at least partially solve the technical problems existing in the prior art such as low efficiency, lack of dynamic adjustment capability and insufficient accuracy.

[0004] According to one aspect of the present invention, a method for determining a commuting station is provided, wherein the method comprises:

[0005] Obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set;

[0006] Using a density-based spatial clustering algorithm, based on preset parameters, the data set is clustered to obtain a number of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee;

[0007] One or more center points of each point cluster are determined, and the geographical locations corresponding to the center points are used as commuting stations.

[0008] Optionally, the density-based spatial clustering algorithm includes a DBSCAN algorithm.

[0009] Optionally, the preset parameters include: the maximum distance between the residence of the commuting employees and the commuting station and the minimum number of commuting employees covered by the same commuting station.

[0010] Optionally, the determining one or more center points of each point cluster comprises:

[0011] For each point cluster, the K-means algorithm is used to determine a center point of the point cluster, and the distance between each point of the point cluster and the center point is calculated, and it is judged whether the maximum distance meets the preset threshold. If not, the K value is incremented to determine K sub-point clusters of the point cluster and a center point of each sub-point cluster, and the distance between each point of each sub-point cluster and the center point of the sub-point cluster is calculated, and it is judged whether the maximum distance meets the preset threshold. The above operation is iterated until the maximum distance meets the preset threshold, and one or more center points of the point cluster are determined;

[0012] Traverse each point cluster and determine one or more center points of each point cluster.

[0013] Optionally, after determining one or more center points of each point cluster, the method includes:

[0014] The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

[0015] Optionally, the method for determining a commuting station further includes:

[0016] Determine the commuting route based on the commuting direction and each commuting stop.

[0017] According to another aspect of the present invention, a device for determining a commuting station is provided, wherein the device comprises:

[0018] The first module is used to obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set;

[0019] The second module is used to cluster the commuting data set using a density-based spatial clustering algorithm based on preset parameters to obtain a plurality of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee;

[0020] The third module is used to determine one or more center points of each point cluster, and use the geographical location corresponding to the center point as a commuting station.

[0021] Optionally, the third module is used for:

[0022] Determine one or more center points of each point cluster;

[0023] The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

[0024] Optionally, the device for determining a commuting station further includes:

[0025] The fourth module is used to determine the commuting route according to the commuting direction and each commuting station.

[0026] Compared with the prior art, the present invention provides a method, device and equipment for determining commuting stations, and the method includes: A. obtaining and processing the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set; B. using a density-based spatial clustering algorithm, based on preset parameters, clustering the data set to obtain a number of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee; C. determining one or more center points of each point cluster, and using the geographic location corresponding to the center point as the commuting station. In commuting station planning, the present invention can efficiently and accurately determine commuting stations, and can dynamically adjust commuting stations according to the number of users or location changes, which can improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0028] Figure 1 A schematic flow chart of a method for determining a commuting station according to one aspect of the present invention is shown;

[0029] Figure 2 A schematic diagram showing a device for determining a commuting station according to another aspect of the present invention;

[0030] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0031] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0032] In a typical configuration of each embodiment of the present invention, the execution subject of the method, each trusted party of the system and / or each module of the device includes one or more processors (CPU), input / output interface, network interface and memory.

[0033] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0034] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0035] When planning commuter routes, commuter vehicle operators that provide commuter services first need to determine the commuter stations on the commuter routes, which usually requires a lot of manpower and time to determine. In addition, when the number of commuter users or their locations change, it is difficult to respond quickly and make timely adjustments. Alternatively, GIS tools are used to determine the routes, but this may fail to fully consider the density of commuter user distribution and actual commuting needs, and the determined commuter stations may not be accurate enough, resulting in unreasonable layout and insufficient coverage.

[0036] The present invention provides a method for determining commuting stations. First, the residential location information of all commuting users with commuting needs is collected. Then, a density-based spatial clustering algorithm is used for preliminary classification to obtain a number of point clusters. Further clustering processing is performed on each point cluster to determine one or more center points in each point cluster. The geographical location corresponding to the center point is determined as the commuting station. The commuting station can be determined efficiently and accurately, and the commuting station can be dynamically adjusted according to changes in the number or location of commuting users.

[0037] In order to further illustrate the technical means adopted by the present invention and the effects achieved, the technical scheme of the present invention is clearly and completely described below in conjunction with the accompanying drawings and various embodiments.

[0038] Figure 1 A schematic diagram of a method for determining a commuting station according to one aspect of the present invention is shown, wherein the method of one embodiment includes:

[0039] S101 obtains and processes the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set;

[0040] S102 uses a density-based spatial clustering algorithm to cluster the data set based on preset parameters to obtain a plurality of point clusters, wherein a point in each point cluster corresponds to geographic location data of a commuting employee;

[0041] S103 determines one or more center points of each point cluster, and uses the geographical location corresponding to the center point as a commuting station.

[0042] The method of this embodiment is implemented through the server 100 of the commuting service system deployed by the enterprise providing commuting services. The commuting service system generally includes the server 100, an APP or a client 200. The server 100 includes a computer device with the necessary software and hardware environment, and the client 200 is generally installed and run on the user's smart terminal. Among them, the computer device includes but is not limited to a personal computer, a laptop computer, an industrial computer, a server, a network host, a single network server or a network server cluster. Here, the computer device is only an example, and other existing or future devices and / or resource platforms that may appear should also be included in the scope of protection of the present invention if they are applicable to the present invention, and are included herein by reference.

[0043] Commuter users with commuting needs usually complete registration in the commuting service system through the APP or client 200 in advance. The registration information at least includes the residential location information of the commuting employees, such as: No. XX, XX Road, XX Street, XX District. After successfully registered users log in to the commuting service system, they enter the user's commuting information. The server 100 will match the user's commuting information in the opened commuting routes. If it exists, it will return the matching opened commuting routes (different commuting routes, or the same commuting route at different times) to the APP or client 200. If there are multiple routes, the user can choose the most suitable commuting route (which can be specific to the shift) to book and ride. The server 100 will collect and summarize the commuting information of users who have no matching commuting routes, and / or the commuting information of similar users collected by other means, to determine a new commuting route and provide commuting services to these users.

[0044] In this embodiment, in step S101, the server 100 may obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set.

[0045] Among them, the server 100 can obtain the residential location information of each successfully registered commuting user from the database or relevant files of the commuting service system, and can use LBS (Location Based Services) service to convert the residential location information into longitude and latitude data to obtain the geographic location data of each commuting user, and form a data set with the acquired geographic location data of all commuting users.

[0046] After obtaining the data set including the geographic location data of each commuting user, continuing in this embodiment, in step S102, the server 100 can use a density-based spatial clustering algorithm to cluster the data set based on preset parameters to obtain a number of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee.

[0047] Among them, the server 100 can adopt a density-based spatial clustering algorithm, take the geographic location data of each commuting employee in the data set as a spatial data point, and perform initial clustering processing on each spatial data point in the data set according to preset parameters to obtain a number of point clusters, wherein each point cluster includes a number of points, and each point corresponds to a spatial data point, i.e., the geographic location data of a commuting employee.

[0048] Optionally, in step S102, the density-based spatial clustering algorithm includes a DBSCAN algorithm.

[0049] The density-based spatial clustering algorithm can use the DBSCAN algorithm. The DBSCAN algorithm is a density-based spatial clustering algorithm that finds high-density areas in the data point space, takes these areas as point clusters, and classifies isolated data points as noise. It can effectively identify dense areas where commuting employees are distributed, obtain preliminary clustering results, and form several point clusters.

[0050] Optionally, in step S102, when the DBSCAN algorithm is used, the preset parameters include:

[0051] The maximum distance between a commuting employee's residence and the commuting station and the minimum number of commuting employees covered by the same commuting station.

[0052] Among them, the maximum distance requirements of commuting users for their residences to commuting stations can be collected. Combined with the requirements of operating cost management or other actual business scenarios, at least two parameters of the DBSCAN algorithm can be pre-set: the neighborhood radius (corresponding to the maximum distance between the employee's residence and the commuting station and the minimum number of neighborhood spatial data points (corresponding to the minimum number of commuting employees covered by the same commuting station). Based on the preset neighborhood radius and the minimum number of domain spatial data points, the DBSCAN algorithm is used to perform preliminary clustering processing on the above data set to obtain several point clusters, wherein the points in each point cluster are within a range whose radius is within the range of the neighborhood radius, and the number of points in each point cluster is not less than the minimum number of domain spatial data points. In order to make the point cluster cover as many employees as possible, the preset neighborhood radius of the point cluster can be larger. Among them, the use of DBSC The AN algorithm performs preliminary clustering processing on the above data set. There may be points (noise points or isolated points) that are not covered by any point cluster. Therefore, in order to make the points corresponding to as many employees as possible covered by the point cluster, the preset point cluster neighborhood radius should be larger. For example, if the neighborhood radius is 500 meters, so that all point clusters cover 85% of commuting users, and the neighborhood radius is 1000 meters, so that all point clusters cover 95% of commuting users, then the neighborhood radius can be preset to 1000 meters, and the minimum number of neighborhood spatial data points can be preset to 3. Then, the DBSCAN algorithm is used to perform clustering processing on the above data set, and several point clusters can be obtained, where the geographical locations of the commuting employees corresponding to the points in each point cluster are within a radius of no more than 1000 meters, and the number of points in each point cluster is not less than 3, that is, each point cluster covers at least 3 commuting employees.

[0053] Continuing with this embodiment, in step S103, the server 100 may determine one or more center points of each point cluster, and use the geographical location corresponding to the center point as a commuting station.

[0054] Among them, for the several point clusters obtained in step S102, the server 100 can process each point cluster, determine one or more center points of each point cluster, and use the geographical location corresponding to each center point as a commuting station.

[0055] Optionally, in step S103, determining one or more center points of each point cluster includes:

[0056] For each point cluster, the K-means algorithm is used to determine a center point of the point cluster, and the distance between each point of the point cluster and the center point is calculated, and it is judged whether the maximum distance meets the preset threshold. If not, the K value is incremented to determine K sub-point clusters of the point cluster and a center point of each sub-point cluster, and the distance between each point of each sub-point cluster and the center point of the sub-point cluster is calculated, and it is judged whether the maximum distance meets the preset threshold. The above operation is iterated until the maximum distance meets the preset threshold, and one or more center points of the point cluster are determined;

[0057] Traverse each point cluster and determine one or more center points of each point cluster.

[0058] Among them, through step 102, the server 100 can perform preliminary clustering processing on the above data set to obtain several point clusters. In step S103, the K-means algorithm can be used to further cluster each point cluster to determine one or more center points of each point cluster.

[0059] Among them, for each point cluster, first, the K value is set to 1, and the K-means algorithm is used to determine a center point of the point cluster, where the sum of the distances from the center point to each point in the point cluster is the shortest; then the maximum distance between each point in the point cluster and the center point is determined, and it is judged whether the maximum distance meets the preset threshold: if it does, it means that the docking (walking / cycling) distance of each commuting employee covered by the point cluster can meet the preset docking distance (for example, 500 meters), and the geographical location corresponding to the center point can be set as the commuting point. station; if it is not satisfied, it means that among the commuting employees covered by the point cluster, there are employees whose docking distance exceeds the preset docking distance, then the K value is increased and set to 2, and then the K-means algorithm is used to cluster the point cluster, and the point cluster is divided into 2 sub-point clusters, and a center point of each sub-point cluster is determined respectively, wherein the sum of the distances from the center point of each sub-point cluster to each point in the sub-point cluster is the shortest; then the maximum distance between each point of the sub-point cluster and the center point of the sub-point cluster is determined, and it is determined whether the maximum distance meets the preset threshold. Iterate the above operation until the maximum distance between each point of the point cluster or each sub-point cluster under the point cluster and the center point of the point cluster or each sub-point cluster under the point cluster meets the preset threshold, and obtain one or more center points of the point cluster, so that the docking distance of each commuting employee covered by the point cluster to the geographical location corresponding to the nearest center point can meet the preset docking distance.

[0060] After clustering the above data set using the DBSCAN algorithm, if only one center point of each point cluster is determined by the K-means algorithm, some points in the point cluster, such as points near the edge of the point cluster coverage, may be far from the center point, and the corresponding commuting user's docking distance to the commuting station is far, which cannot meet the commuting user's requirements. In this optional embodiment, the K-means algorithm is used to iteratively perform clustering processing, and the preliminary clustering processing results of the DBSCAN algorithm are refined, wherein, by dynamically adjusting the K value in the K-means algorithm and iteratively using the K-means algorithm, one or more center points can be obtained for each point cluster, and accordingly, it can be ensured that the docking distance of each commuting user meets the requirements, so that the planned commuting station can ensure that the docking distance of each commuting user covered by each point cluster meets the requirements, which can further improve the accuracy of commuting station planning and user experience.

[0061] Optionally, after determining one or more center points of each point cluster, the method includes:

[0062] The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

[0063] Among them, the geographical location corresponding to each center point obtained through the above-mentioned embodiments and / or optional embodiments may not be suitable for parking and picking up passengers. GIS services can be used, for example, calling the POI (Point of Interest) data interface to obtain the geographical location information of the bus stop suitable for parking and picking up passengers that is closest to the geographical location corresponding to each center point, and use the bus stop as a commuting stop to ensure feasibility and convenience of employee commuting.

[0064] After the existing commuter stations are determined manually or using GIS tools, the commuter routes can generally only be planned for the case where the number of users and commuter routes are small. When the number of users is large and the demand for commuter routes is high, it is difficult to meet the operator's global cost optimization needs.

[0065] Optionally, the method for determining a commuting station further includes:

[0066] S104 determines a commuting route according to the commuting direction and each commuting station.

[0067] Among them, after determining all commuting stations (corresponding to all point clusters and / or sub-point clusters), one or more commuting routes can be determined according to the commuting direction and each commuting station, which can improve the overall operating results of the operator, especially when the number of users is large and the demand for commuting routes is high, it can better optimize operating costs.

[0068] Among them, commuting shifts can be planned based on commuting routes and combined with the commuting time information of each commuting employee, which can further improve the overall operational performance of the operator.

[0069] Figure 2 A device for determining a commuting station according to another aspect of the present invention is shown, wherein the device of one embodiment includes:

[0070] The first module 210 is used to obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set;

[0071] The second module 220 is used to perform clustering processing on the commuting data set based on preset parameters using a density-based spatial clustering algorithm to obtain a plurality of point clusters, wherein a point in each point cluster corresponds to geographic location data of a commuting employee;

[0072] The third module 230 is used to determine one or more center points of each point cluster, and use the geographical location corresponding to the center point as a commuting station.

[0073] The device of this embodiment is integrated into the aforementioned server 100 .

[0074] In this embodiment, through the first module 210 of the device, the residential location information of each successfully registered commuting user can be obtained from the database or related files of the commuting service system, and the LBS service can be used to convert the residential location information into longitude and latitude data to obtain the geographic location data of each commuting user, and the acquired geographic location data of all commuting users can be combined into a data set.

[0075] Continuing with this embodiment, through the second module 220 of the device, Coco uses a density-based spatial clustering algorithm to take the geographic location data of each commuting employee in the data set as a spatial data point, and performs initial clustering processing on each spatial data point in the data set according to preset parameters to obtain a number of point clusters, wherein each point cluster includes a number of points, and each point corresponds to a spatial data point, i.e., the geographic location data of a commuting employee.

[0076] Continuing with this embodiment, each point cluster can be processed by the third module 230 of the device to determine one or more center points of each point cluster, and the geographical location corresponding to each center point is used as a commuting station.

[0077] Optionally, the third module 230 is used for:

[0078] Determine one or more center points of each point cluster;

[0079] The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

[0080] In this optional embodiment, each point cluster can be processed by the third module 230 of the device to determine one or more center points of each point cluster. However, the geographical location corresponding to each center point may not be suitable for parking and boarding and dropping off passengers. GIS services can be used, for example, calling the POI data interface to obtain the geographical location information of the bus stop suitable for parking and boarding and dropping off passengers that is closest to the geographical location corresponding to each center point, and use the bus stop as a commuting stop to ensure feasibility and convenience of employee commuting.

[0081] Optionally, the device for determining a commuting station further includes:

[0082] The fourth module 240 is used to determine the commuting route according to the commuting direction and each commuting station.

[0083] In this optional embodiment, through the fourth module 240 of the device, one or more commuting routes can be determined according to the commuting direction and each commuting station, which can improve the overall operating performance of the operator.

[0084] In the various embodiments and / or optional embodiments of the above-mentioned system, the matters not mentioned in the method steps executed by each module are the same as those of the aforementioned related method embodiments and / or optional embodiments, and will not be repeated here.

[0085] According to yet another aspect of the present invention, a computer-readable medium is provided, wherein the computer-readable medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to implement part or all of the above method.

[0086] It should be noted that each method embodiment and / or optional embodiment of the present invention may be partially or completely implemented in software and / or a combination of software and hardware. The software program involved in the present invention may be executed by a processor to implement some or all of the steps or functions of the above-mentioned embodiments and / or optional embodiments. Similarly, the software program of the present invention (including related data structures) may be stored in a computer-readable recording medium.

[0087] In addition, a part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide part or all of the method and / or technical solution according to the present invention through the operation of the computer. The program instruction for calling the method of the present invention may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in a working memory of a computer device that runs according to the program instruction.

[0088] According to another aspect of the present invention, a device for determining a commuting station is provided, wherein the device comprises: a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute part or all of the methods and / or technical solutions of the aforementioned embodiments and / or optional embodiments.

[0089] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments and / or optional embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software and / or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for determining a commuting station, characterized in that: The method comprises: Obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set; Using a density-based spatial clustering algorithm, based on preset parameters, clustering the data set to obtain a number of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee; One or more center points of each point cluster are determined, and the geographical locations corresponding to the center points are used as commuting stations.

2. The method according to claim 1, characterized in that The density-based spatial clustering algorithm includes the DBSCAN algorithm.

3. The method according to claim 2, characterized in that The preset parameters include: The maximum distance between a commuting employee's residence and the commuting station and the minimum number of commuting employees covered by the same commuting station.

4. The method according to claim 1, characterized in that: Determining one or more center points of each point cluster comprises: For each point cluster, the K-means algorithm is used to determine a center point of the point cluster, and the distance between each point of the point cluster and the center point is calculated, and it is judged whether the maximum distance meets the preset threshold. If not, the K value is increased to determine K sub-point clusters of the point cluster and a center point of each sub-point cluster, and the distance between each point of each sub-point cluster and the center point of the sub-point cluster is calculated, and it is judged whether the maximum distance meets the preset threshold. The above operation is iterated until the maximum distance meets the preset threshold, and one or more center points of the point cluster are determined; Traverse each point cluster and determine one or more center points of each point cluster.

5. The method according to claim 1, characterized in that After determining one or more center points of each point cluster, the method includes: The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

6. The method according to claim 1, characterized in that The method further comprises: Determine the commuting route based on the commuting direction and each commuting stop.

7. A device for determining commuting stations, characterized in that: The device comprises: The first module is used to obtain and process the residential location information of each commuting employee to obtain the geographic location data of each commuting employee and form a data set; The second module is used to cluster the commuting data set using a density-based spatial clustering algorithm based on preset parameters to obtain a plurality of point clusters, wherein a point in each point cluster corresponds to the geographic location data of a commuting employee; The third module is used to determine one or more center points of each point cluster, and use the geographical location corresponding to the center point as a commuting station.

8. The device according to claim 7, characterized in that The third module is used for: Determine one or more center points of each point cluster; The information of a bus stop closest to the geographical location corresponding to each center point is obtained, and the bus stop is used as a commuting stop.

9. The device according to claim 7, characterized in that The device also includes: The fourth module is used to determine the commuting route according to the commuting direction and each commuting station.

10. A computer-readable medium having computer-readable instructions stored thereon, wherein the computer-readable instructions can be executed by a processor to implement part or all of the method according to any one of claims 1 to 6.

11. A device for determining a commuting station, characterized in that: The device comprises: one or more processors; and A memory storing computer-readable instructions, wherein when the computer-readable instructions are executed, the processor is caused to perform part or all of the operations of the method according to any one of claims 1 to 6.

Images