Method and system for identifying multivariate relationship between shared bicycle and public transport system

Abstract

The invention discloses a method and a system for identifying the multivariate relationship between a shared bicycle and a public transport system, which are used in the field of intelligent transportation, and the method comprises the following steps: defining the multivariate relationship between the shared bicycle and the public transport; defining the type of the subway station; a regression model is utilized to analyze a regression relationship between subway passenger flow and the number of shared bicycle users in different connection buffer areas in different time periods, and threshold values of the connection buffer areas in different time periods of different subway stations are obtained; and according to the threshold values of the connection buffer areas at different time periods of different subway stations, the multivariate relationship between the shared bicycles and the public transport is judged. According to the method, the relationship between the subway passenger flow and the number and distribution of the shared bicycle users is deeply analyzed, the connection characteristics of the subway passenger flow and the shared bicycle users in different time periods are discussed, a reasonable connection buffer area range threshold value is determined, the overall efficiency of an urban traffic system is improved, and the service life of the urban traffic system is prolonged. And important reference can be provided for urban traffic planning and formulation of a shared bicycle putting strategy.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and more specifically, to a method and system for identifying multiple relationships between shared bicycles and public transportation systems. Background Art

[0002] Public transportation, with its high reliability, convenience, affordability, energy saving and environmental protection, has become one of the preferred modes of transportation for urban residents' daily travel. However, due to the fixed stations and routes, the public transportation system has brought many restrictions to travelers. In particular, when the starting point or destination of travelers is far away from the public transportation station, they will face the "last mile" connection problem when using public transportation. This problem has long plagued urban transportation planners and travelers because it directly affects the attractiveness and usage rate of public transportation.

[0003] In order to improve the travel experience of the "last mile", the urban transportation system urgently needs to find an efficient and flexible connection method. With the emergence of new travel modes such as shared bicycles, especially the popularity of dockless shared bicycles, this problem has been solved in a new way. Shared bicycles can be unlocked, ridden, parked and paid anywhere through mobile phone software and QR code technology, providing travelers with a high degree of freedom and convenience. It can not only effectively solve the connection problem of the "last mile", but also alleviate urban traffic congestion to a certain extent and improve the overall efficiency of the urban transportation system.

[0004] However, the connection between shared bikes and public transportation is very complex. Travelers of different time periods, locations, and types will be affected by many factors when choosing shared bikes or public transportation. Therefore, how to scientifically and reasonably determine the connection threshold range of subway stations to maximize the synergy between shared bikes and public transportation has become an important issue that needs to be solved in the field of urban transportation planning.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to overcome the above problems, the present invention aims to propose a method and system for identifying the multi-dimensional relationship between shared bicycles and public transportation systems, with the aim of scientifically and reasonably determining the connection threshold range of subway stations.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, a method for identifying a multivariate relationship between a shared bicycle and a public transportation system is provided. The method for identifying a multivariate relationship between a shared bicycle and a public transportation system comprises the following steps:

[0009] S1. Define the multi-faceted relationship between shared bikes and public transportation;

[0010] S2. Define the categories of subway stations and divide them into residential areas and large commercial areas;

[0011] S3. Use the regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffers at different time periods, and obtain the thresholds of docking buffers at different subway stations at different time periods;

[0012] S4. Determine the multi-dimensional relationship between shared bicycles and public transportation based on the thresholds of the docking buffer zones at different subway stations at different times.

[0013] Optionally, the multiple relationships between shared bikes and public transportation include:

[0014] Shared bicycles connecting to public transportation refers to the situation where travelers use shared bicycles to connect to public transportation;

[0015] Shared bikes replace public transportation, which means that travelers use shared bikes instead of public transportation;

[0016] Shared bikes complement public transportation, indicating that travelers use shared bikes in areas where public transportation coverage is incomplete.

[0017] Optionally, using a regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffers at different time periods, and obtaining thresholds of docking buffers at different subway stations at different time periods includes the following steps:

[0018] S31. According to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the range of the buffer zone for connecting to different subway stations is obtained;

[0019] S32. Use the regression model to analyze the relationship between the number of subway passengers and the number of shared bicycle users in different time periods, and obtain the thresholds of the buffer zones for different subway stations in different time periods.

[0020] Optionally, according to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the ranges of the buffer zones for connecting to different subway stations include:

[0021] Determine each starting and ending point pair of the shared bicycle trip, select the nearest subway station and measure the distance to each starting and ending point pair of the shared bicycle trip. When the number of starting points or ending points reaches a preset value, the range of the connection buffer zone of different subway stations is obtained.

[0022] Optionally, the regression model is expressed as:

[0023] Y t =β0+β1X t +γt +ε t ;

[0024] Where Y t represents the number of subway passengers in time period t; β0 represents the regression coefficient; β1 represents the regression coefficient; X t represents the number of shared bicycle users in time period t; γ t represents the time period effect, which indicates the additional impact of different time periods on the number of subway passengers; ε t represents the random error term.

[0025] Optionally, the basis for dividing the time period effect includes:

[0026] Subway passenger flow and shared bicycle user data are uniformly divided on an hourly basis.

[0027] Optionally, judging the multivariate relationship between shared bicycles and public transportation according to the thresholds of the connection buffer zones at different subway stations at different time periods includes the following steps:

[0028] S41, determining a connection buffer range threshold according to the range of the connection buffers of different subway stations in different time periods;

[0029] S42. Determine the multivariate relationship between shared bicycles and the public transportation system based on the subordinate relationship between the starting and ending points of shared bicycles and the threshold value of the connection buffer zone.

[0030] Optionally, determining the connection buffer range threshold according to the range of the connection buffers of different subway stations in different time periods includes:

[0031] The accuracy of the time-period-sensitive regression model between subway passenger flow and the number of shared bicycles in different ranges near subway stations is compared. The subway station connection range with the highest determination coefficient value is selected based on the determination coefficient value, and the subway station connection range with the highest determination coefficient value is confirmed as the threshold of the connection buffer zone.

[0032] Optionally, judging the multivariate relationship between shared bicycles and the public transportation system according to the subordinate relationship between the starting and ending points of shared bicycles and the range threshold of the docking buffer zone includes:

[0033] If the starting point or end point of a shared bicycle is within the buffer zone of a subway station or bus station, then the multi-dimensional relationship between shared bicycles and the public transportation system is judged to be a shared bicycle connecting to public transportation;

[0034] If the starting and ending points of shared bicycles are both located in the service buffer zone of subway stations or bus stations, then the multivariate relationship between shared bicycles and the public transportation system is judged to be that shared bicycles replace public transportation;

[0035] If the starting and ending points of shared bicycles are both outside the service buffer zone of subway stations or bus stops, then it is judged that the multi-dimensional relationship between shared bicycles and the public transportation system belongs to shared bicycles supplementing public transportation.

[0036] According to another aspect of the present invention, there is also provided a system for identifying multivariate relationships between shared bicycles and public transportation systems, the system comprising: a multivariate relationship definition module, a subway station type classification module, a regression relationship analysis module and a multivariate relationship identification module;

[0037] Multivariate relationship definition module, used to define the multivariate relationship between shared bicycles and public transportation;

[0038] The subway station type classification module is used to define the subway station categories and divide the subway stations into residential areas and large commercial areas;

[0039] The regression relationship analysis module is used to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffer zones at different time periods using a regression model;

[0040] The multivariate relationship identification module is used to determine the multivariate relationship between shared bicycles and public transportation based on the thresholds of the docking buffer zones at different subway stations at different time periods.

[0041] Compared with the prior art, the present invention has the following beneficial effects: the present invention deeply analyzes the relationship between subway passenger flow and the number and distribution of shared bicycle users, explores the connection characteristics of the two in different time periods, determines a reasonable connection buffer zone range threshold, and deeply understands the synergy between shared bicycle public transportation systems, promotes the efficient complementarity between the two to achieve the overall optimization of the urban transportation system, which not only helps to improve the overall efficiency of the urban transportation system, but also provides an important reference for urban transportation planning, public transportation facility layout and the formulation of shared bicycle deployment strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown:

[0043] Figure 1 is a flow chart of a method for identifying a multivariate relationship between a shared bicycle and a public transportation system according to an embodiment of the present invention;

[0044] Figure 2 is a principle block diagram of a system for identifying multiple relationships between shared bicycles and public transportation systems according to an embodiment of the present invention;

[0045] Figure 3It is a judgment diagram of the multi-dimensional relationship between shared bicycles and public transportation in a method for identifying the multi-dimensional relationship between shared bicycles and public transportation systems according to an embodiment of the present invention.

[0046] In the figure:

[0047] 1. Multivariate relationship definition module; 2. Subway station type classification module; 3. Regression relationship analysis module; 4. Multivariate relationship identification module. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] According to an embodiment of the present invention, a method and system for identifying multi-dimensional relationships between shared bicycles and public transportation systems are provided.

[0050] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the method for identifying the multivariate relationship between shared bicycles and public transportation systems in an embodiment of the present invention, the method for identifying the multivariate relationship between shared bicycles and public transportation systems includes the following steps:

[0051] S1. Define the multi-faceted relationship between shared bikes and public transportation.

[0052] Preferably, the multiple relationships between shared bicycles and public transportation include:

[0053] Shared bicycles connecting to public transportation refers to situations where travelers use shared bicycles to connect to public transportation;

[0054] Shared bikes replacing public transportation refers to situations where travelers use shared bikes instead of public transportation;

[0055] Shared bikes supplement public transportation, which means that travelers use shared bikes in areas where public transportation coverage is incomplete.

[0056] It needs to be explained that the definition of the multi-dimensional relationship between shared bicycles and public transportation divides the multi-dimensional relationship into shared bicycles connecting to public transportation, shared bicycles replacing public transportation, and shared bicycles supplementing public transportation.

[0057] The "shared bicycles connecting to public transportation" relationship refers to the situation where travelers use shared bicycles to connect to public transportation. This definition requires that the starting point or end point of the shared bicycle is located within the buffer zone of the subway station / bus station. In this case, the shared bicycle riding record is considered to be in the "shared bicycles connecting to public transportation" category;

[0058] The "shared bicycles replacing public transportation" relationship refers to the situation where a shared bicycle trip replaces a public transportation trip. For a shared bicycle trip, if there are public transportation stations near the starting point and the end point of the trip, that is, the starting and ending points of the shared bicycle are both located in the service buffer zone of the subway station / bus station, in this case, the shared bicycle riding record is regarded as the "shared bicycle replacing public transportation" category;

[0059] The "shared bicycles supplement public transportation" relationship means that travelers use shared bicycles in areas with incomplete public transportation coverage. If the departure point or destination, or both, are far away from the bus stop, that is, the starting and ending points of the shared bicycles are both outside the service buffer zone of the subway station / bus stop, then the shared bicycle riding record is regarded as the "shared bicycles supplement public transportation" category.

[0060] S2. Define the categories of subway stations and divide them into residential areas and large commercial areas.

[0061] It should be explained that subway stations are divided into different types. Here we mainly discuss the connection relationship between subway stations and shared bicycles near residential areas and large commercial areas. According to daily surveys, the shared bicycle data appearing near large commercial areas may only have the destination of the shopping mall and does not involve subway connection, while the shared bicycle data appearing near subway stations in residential areas is mostly for subway connection.

[0062] S3. Use the regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffers at different times, and obtain the thresholds of docking buffers at different subway stations at different times.

[0063] Preferably, using a regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffers at different time periods, and obtaining the thresholds of docking buffers at different subway stations at different time periods includes the following steps:

[0064] S31. According to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the range of the buffer zone for connecting to different subway stations is obtained;

[0065] S32. Use the regression model to analyze the relationship between the number of subway passengers and the number of shared bicycle users in different time periods, and obtain the thresholds of the buffer zones for different subway stations in different time periods.

[0066] Preferably, according to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the ranges of the buffer zones for connecting to different subway stations include:

[0067] Determine each starting and ending point pair of the shared bicycle trip, select the nearest subway station and measure the distance to each starting and ending point pair of the shared bicycle trip. When the number of starting points or ending points reaches a preset value, the range of the connection buffer zone of different subway stations is obtained.

[0068] Preferably, the regression model is expressed as:

[0069] Y t =β0+β1X t +γ t +ε t ;

[0070] Where Y t represents the number of subway passengers in time period t; β0 represents the regression coefficient; β1 represents the regression coefficient; X t represents the number of shared bicycle users in time period t; γ t represents the time period effect, which indicates the additional impact of different time periods on the number of subway passengers; ε t represents the random error term.

[0071] Preferably, the basis for dividing the time period effect includes:

[0072] The subway passenger flow and shared bicycle user data are unified and divided on an hourly basis.

[0073] It should be explained that the scope of subway station connection is preliminarily obtained based on the distribution of distances between the starting and ending points of shared bicycles and subway stations during peak hours in the morning and evening. The specific method is to select the nearest subway station and measure the distance to it for each starting and ending point pair of shared bicycle trips. As the distance between the starting point (end point) and the subway station increases, the number of starting points (end points) around the subway station decreases, indicating that the demand for joint travel between shared bicycles and subway stations decreases. When the number of starting points (end points) reaches a relatively small value, the scope of the service buffer zone of several subway stations can be preliminarily obtained.

[0074] In-depth exploration of the regression relationship between subway passenger flow and shared bicycle user data. Since the urban transportation system shows significant differences in different time periods, such as morning and evening peaks, off-peak periods, etc. These time differences have a direct impact on the number of subway passenger flow Y and the number of shared bicycle users X. In order to accurately capture this time characteristic, a time-sensitive regression model is constructed. On the basis of traditional linear regression, the model introduces time variables to capture the dynamic relationship between Y and X in different time periods. The basic form of the regression model is:

[0075] Y t=β0+β1X t +γ t +ε t ;

[0076] Where Y t represents the number of subway passengers in time period t; β0 represents the regression coefficient; β1 represents the regression coefficient; X t represents the number of shared bicycle users in time period t; γ t represents the time period effect, which indicates the additional impact of different time periods on the number of subway passengers; ε t represents the random error term.

[0077] The time-period-sensitive linear regression model is constructed using the training data set and verified using the test data set. During the verification process, the difference between the subway passenger flow predicted by the model and the actual observed value is compared to evaluate the model's predictive ability. At the same time, the time-period effect Y can also be analyzed. t The impact on subway passenger flow is analyzed to reveal the interaction between subways and shared bicycles at different times.

[0078] According to the accuracy of the regression model of subway passenger flow and the number of shared bicycles in different ranges near the subway station, the thresholds of the connection buffer zone at different subway stations at different time periods are obtained.

[0079] The time period characteristic is that the subway passenger flow and shared bicycle user data are divided according to the same hour, so as to analyze the connection characteristics during peak hours and off-peak hours.

[0080] The core of the constructed time-sensitive linear regression model is to introduce the time variable Y t , capturing the dynamic relationship between Y and X in different time periods. Time period variable Y t It can be regarded as a constant term that changes over time, which reflects the baseline level of subway passenger flow in different time periods. By introducing the time period variable, the model can more accurately describe the relationship between Y and X in different time periods. The estimation of model parameters can be achieved by the least squares method, that is, finding a set of regression coefficients β0, β1 and time period effect Y t , so that the difference between the subway passenger flow predicted by the model and the actual observed value (i.e., the residual sum of squares) is minimized.

[0081] S4. Determine the multi-dimensional relationship between shared bicycles and public transportation based on the thresholds of the docking buffer zones at different subway stations at different times.

[0082] Preferably, judging the multivariate relationship between shared bicycles and public transportation according to the thresholds of the connection buffer zones at different subway stations at different time periods includes the following steps:

[0083] S41, determining a connection buffer range threshold according to the range of the connection buffers of different subway stations in different time periods;

[0084] S42. Determine the multivariate relationship between shared bicycles and the public transportation system based on the subordinate relationship between the starting and ending points of shared bicycles and the threshold value of the connection buffer zone.

[0085] Preferably, determining the connection buffer range threshold according to the range of the connection buffers of different subway stations in different time periods includes:

[0086] The accuracy of the time-period-sensitive regression model between subway passenger flow and the number of shared bicycles in different ranges near subway stations is compared. The determination coefficient of the regression model is used to make a judgment, and the subway station connection range with the highest determination coefficient value is selected. The subway station connection range with the highest determination coefficient value is confirmed as the threshold of the connection buffer zone.

[0087] Preferably, judging the multivariate relationship between shared bicycles and the public transportation system according to the subordinate relationship between the starting and ending points of shared bicycles and the threshold of the connection buffer zone includes:

[0088] If the starting point or end point of a shared bicycle is within the buffer zone of a subway station or bus station, then the multi-dimensional relationship between shared bicycles and the public transportation system is judged to be that shared bicycles connect to public transportation;

[0089] If the starting and ending points of shared bicycles are both located in the service buffer zone of subway stations or bus stations, then the multivariate relationship between shared bicycles and the public transportation system is judged to be that shared bicycles replace public transportation;

[0090] If the starting and ending points of shared bicycles are both outside the service buffer zone of subway stations or bus stops, then it is judged that the multi-dimensional relationship between shared bicycles and the public transportation system belongs to shared bicycles supplementing public transportation.

[0091] It should be explained that, based on the range of service buffers of several subway stations initially obtained from the statistical results, the threshold of the connection buffer range that best represents the connection relationship is selected. 2 The coefficient of determination is a key indicator for measuring the ability of a linear regression model to explain data changes, and its value is between 0 and 1. When comparing the accuracy of the linear regression model that is sensitive to the time period between subway passenger flow and the number of shared bicycles in different ranges near subway stations, we can observe the R 2 The value is used for judgment. 2 The closer the value is to 1, the more accurately the model can capture the changing trend in the data, that is, the fit between the model's predicted value and the actual observed value is higher, so the model's accuracy is also higher. 2 The connection range of the subway station with the highest value is the threshold of its connection buffer zone.

[0092] like Figure 3As shown, it is a flow chart for determining the multivariate relationship between shared bicycles and public transportation systems.

[0093] If the starting point or end point of a shared bicycle is within the buffer zone of a subway station or bus station, then the multi-dimensional relationship between shared bicycles and the public transportation system is judged to be a shared bicycle connecting to public transportation;

[0094] If the starting and ending points of shared bicycles are both located in the service buffer zone of subway stations or bus stations, then the multivariate relationship between shared bicycles and the public transportation system is judged to be that shared bicycles replace public transportation;

[0095] If the starting and ending points of shared bicycles are both outside the service buffer zone of subway stations or bus stops, then it is judged that the multi-dimensional relationship between shared bicycles and the public transportation system belongs to shared bicycles supplementing public transportation.

[0096] According to another embodiment of the present invention, Figure 2 As shown, a system for identifying the multivariate relationship between shared bicycles and public transportation systems is also provided, and the system for identifying the multivariate relationship between shared bicycles and public transportation systems includes: a multivariate relationship definition module 1, a subway station type classification module 2, a regression relationship analysis module 3 and a multivariate relationship identification module 4;

[0097] Multivariate relationship definition module 1, used to define the multivariate relationship between shared bicycles and public transportation;

[0098] Subway station type classification module 2, used to define the types of subway stations, and divide subway stations into residential areas and large commercial areas;

[0099] Regression relationship analysis module 3, used to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffer zones at different time periods using a regression model;

[0100] The multivariate relationship identification module 4 is used to determine the multivariate relationship between shared bicycles and public transportation according to the thresholds of the connection buffer zones at different subway stations at different time periods.

[0101] To sum up, with the help of the above-mentioned technical scheme of the present invention, the present invention deeply analyzes the relationship between subway passenger flow and the number and distribution of shared bicycle users, explores the connection characteristics of the two in different time periods, determines a reasonable connection buffer zone range threshold, and deeply understands the synergy between shared bicycle public transportation systems, promotes the efficient complementarity between the two to achieve the overall optimization of the urban transportation system, which not only helps to improve the overall efficiency of the urban transportation system, but also provides important reference for urban transportation planning, public transportation facility layout and the formulation of shared bicycle deployment strategies.

[0102] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A method for identifying multivariate relationships between shared bicycles and public transportation systems, characterized in that: The method for identifying the multivariate relationship between shared bicycles and public transportation systems comprises the following steps: S1. Define the multi-faceted relationship between shared bikes and public transportation; S2. Define the categories of subway stations and divide them into residential areas and large commercial areas; S3. Use the regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffers at different time periods, and obtain the thresholds of docking buffers at different subway stations at different time periods; S4. Determine the multi-dimensional relationship between shared bicycles and public transportation based on the thresholds of the docking buffer zones at different subway stations at different times.

2. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 1, characterized in that: The multiple relationships between shared bicycles and public transportation include: Shared bicycles connecting to public transportation refers to the situation where travelers use shared bicycles to connect to public transportation; Shared bikes replace public transportation, which means that travelers use shared bikes instead of public transportation; Shared bikes complement public transportation, indicating that travelers use shared bikes in areas where public transportation coverage is incomplete.

3. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 1, characterized in that: The method of using a regression model to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffer zones at different time periods, and obtaining the thresholds of docking buffer zones at different subway stations at different time periods includes the following steps: S31. According to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the range of the buffer zone for connecting to different subway stations is obtained; S32. Use the regression model to analyze the relationship between the number of subway passengers and the number of shared bicycle users in different time periods, and obtain the thresholds of the buffer zones for different subway stations in different time periods.

4. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 3, characterized in that: According to the distance distribution between the starting and ending points of shared bicycles and subway stations during the morning and evening peak hours, the range of the buffer zone for connecting to different subway stations includes: Determine each starting and ending point pair of the shared bicycle trip, select the nearest subway station and measure the distance to each starting and ending point pair of the shared bicycle trip. When the number of starting points or ending points reaches a preset value, the range of the connection buffer zone of different subway stations is obtained.

5. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 3, characterized in that: The expression of the regression model is: Y t =β0+β1X t +g t +e t ; Where Y t represents the number of subway passengers in time period t; β0 represents the regression coefficient; β1 represents the regression coefficient; X t represents the number of shared bicycle users in time period t; γ t represents the time period effect, which indicates the additional impact of different time periods on the number of subway passengers; ε t represents the random error term.

6. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 3, characterized in that: The basis for dividing the period effect includes: The subway passenger flow and shared bicycle user data are unified and divided on an hourly basis.

7. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 1, characterized in that: The method of judging the multivariate relationship between shared bicycles and public transportation according to the thresholds of the connection buffer zones at different subway stations at different time periods includes the following steps: S41, determining a connection buffer range threshold according to the range of the connection buffers of different subway stations in different time periods; S42. Determine the multivariate relationship between shared bicycles and the public transportation system based on the subordinate relationship between the starting and ending points of shared bicycles and the threshold value of the connection buffer zone.

8. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 7, characterized in that: Determining the range threshold of the docking buffer zone according to the range of the docking buffer zones of different subway stations in different time periods includes: The accuracy of the time-period-sensitive regression model between subway passenger flow and the number of shared bicycles in different ranges near subway stations is compared. The subway station connection range with the highest determination coefficient value is selected based on the determination coefficient value, and the subway station connection range with the highest determination coefficient value is confirmed as the threshold of the connection buffer zone.

9. The method for identifying the multivariate relationship between shared bicycles and public transportation systems according to claim 7, characterized in that: The determination of the multi-dimensional relationship between shared bicycles and the public transportation system based on the subordinate relationship between the starting and ending points of shared bicycles and the threshold of the connection buffer zone includes: If the starting point or end point of a shared bicycle is within the buffer zone of a subway station or bus station, then the multi-dimensional relationship between shared bicycles and the public transportation system is judged to be a shared bicycle connecting to public transportation; If the starting and ending points of shared bicycles are both located in the service buffer zone of subway stations or bus stations, then the multivariate relationship between shared bicycles and the public transportation system is judged to be that shared bicycles replace public transportation; If the starting and ending points of shared bicycles are both outside the service buffer zone of subway stations or bus stops, then it is judged that the multi-dimensional relationship between shared bicycles and the public transportation system belongs to shared bicycles supplementing public transportation.

10. A system for identifying a multivariate relationship between a shared bicycle and a public transportation system, used to implement the method for identifying a multivariate relationship between a shared bicycle and a public transportation system as described in any one of claims 1 to 9, characterized in that: The identification system of the multivariate relationship between shared bicycles and public transportation systems includes: a multivariate relationship definition module, a subway station type classification module, a regression relationship analysis module and a multivariate relationship identification module; Multivariate relationship definition module, used to define the multivariate relationship between shared bicycles and public transportation; The subway station type classification module is used to define the subway station categories and divide the subway stations into residential areas and large commercial areas; The regression relationship analysis module is used to analyze the regression relationship between subway passenger flow and the number of shared bicycle users in different docking buffer zones at different time periods using a regression model; The multivariate relationship identification module is used to determine the multivariate relationship between shared bicycles and public transportation based on the thresholds of the docking buffer zones at different subway stations at different time periods.

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