CN117973640A - Transfer subway station transfer personnel flow prediction method and device - Google Patents
Transfer subway station transfer personnel flow prediction method and device Download PDFInfo
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Abstract
The application provides a transfer subway station transfer personnel flow prediction method and a transfer subway station transfer personnel flow prediction device, wherein the method comprises the following steps: acquiring first passenger arrival information and first departure time intervals of each subway station at each time in a current time period, and acquiring first transfer passenger flow information of each subway station at each time in the current time period; determining respective corresponding first correlation matrixes at each moment in the current time period according to the pre-trained correlation matrix model; and determining the transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the station number of each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station, so that the transfer passenger flow of the transfer subway station in the future time period can be accurately predicted according to the pre-trained transfer passenger flow prediction model of the transfer subway station.
Description
Technical Field
The application relates to the technical field of transfer subway stations, in particular to a transfer personnel flow prediction method and device.
Background
Compared with a common station, the subway transfer station has the characteristics of complex structure, concentrated passenger flow, high risk degree, high early warning difficulty and the like, and at present, the accuracy of predicting the passenger flow of the subway transfer station can be related to various factors, such as the complicated passenger flow track distribution of the subway transfer station in the weekend and holiday time period, and the relationship between the passenger flow of the subway transfer station and the transfer passenger flow of the subway non-transfer station can be caused by the factors, so that the accuracy of predicting the passenger flow of the subway transfer station can be reduced.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present application is to provide a transfer subway station transfer traffic prediction method, so as to implement accurate prediction of the transfer subway station transfer traffic.
The second object of the application is to provide a transfer subway station transfer personnel flow prediction device.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
A fifth object of the application is to propose a computer programme product.
To achieve the above object, an embodiment of a first aspect of the present application provides a method, including: acquiring first passenger arrival information and first departure time intervals of each subway station in a subway network at each time in a current time period, and acquiring first transfer passenger flow information of transfer subway stations in the subway network at each time in the current time period; acquiring the station number from each subway station to the transfer subway station in the subway network; according to a pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at all times in the current time period, wherein the first correlation matrixes corresponding to the times comprise the probability that passengers entering the subway station at the times pass through the transfer subway station; and determining transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first sending time interval, the first transfer passenger flow information, the station number from each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station.
To achieve the above object, a second aspect of the present application provides a transfer passenger flow prediction device for a transfer subway station, including: the first acquisition module is used for acquiring first passenger arrival information and first sending time intervals of each subway station in the subway network at each time in the current time period and acquiring first transfer passenger flow information of the transfer subway stations in the subway network at each time in the current time period; the second acquisition module is used for acquiring the station number from each subway station to the transfer subway station in the subway network; the first determining module is used for determining first correlation matrixes corresponding to all moments in the current time period of the subway network according to the pre-trained correlation matrix model, wherein the first correlation matrixes corresponding to the moments comprise the probability that passengers entering the subway station at the moments pass through the transfer subway station; the second determining module is configured to determine transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the number of stations from each subway station to the transfer subway station, and the first correlation matrix by using a pre-trained transfer passenger flow prediction model of the transfer subway station.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer execution instructions stored in the memory to realize the transfer subway station transfer personnel flow prediction method disclosed by the embodiment of the application.
To achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, where the computer-executable instructions are used to implement the transfer subway station transfer personnel flow prediction method disclosed in the embodiments of the present application when executed by a processor.
To achieve the above object, a fifth aspect of the present application provides a computer program product, which includes a computer program, where the computer program when executed by a processor implements the transfer subway station transfer personnel flow prediction method disclosed in the embodiment of the present application.
According to the transfer subway station transfer passenger flow prediction method and device, first passenger arrival information and first sending time intervals of each subway station in a subway network at each moment in a current time period are obtained, and first transfer passenger flow information of each subway station in the subway network at each moment in the current time period is obtained; acquiring the station number from each subway station to a transfer subway station in a subway network; according to the pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at each moment in the current time period, wherein the first correlation matrixes corresponding to the moment comprise the probability of passengers entering the subway station at the moment passing through the transfer subway station; and determining the transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the station number of each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station, thereby accurately predicting the transfer passenger flow of the transfer subway station in the future time period according to the pre-trained transfer passenger flow prediction model of the transfer subway station.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
Fig. 1 is a schematic flow chart of a transfer subway station transfer personnel flow prediction method provided by an embodiment of the application;
fig. 2 is an example diagram of a metro network topology diagram corresponding to a metro network at a corresponding moment in a current time period according to an embodiment of the present application;
Fig. 3 is a second flow chart of a transfer subway station transfer personnel flow prediction method according to the embodiment of the application;
fig. 4 is a flow chart diagram III of a transfer subway station transfer personnel flow prediction method provided by the embodiment of the application;
fig. 5 shows a schematic structural diagram of a transfer passenger flow prediction device for a transfer subway station according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The technical scheme of the application is to acquire, store, use, process and the like data, which all meet the relevant regulations of national laws and regulations.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a transfer subway station transfer personnel flow prediction method according to an embodiment of the present application.
Step 101, obtaining first passenger arrival information and first sending time intervals of each subway station in the subway network at each time in the current time period, and obtaining first transfer personnel flow information of the transfer subway stations in the subway network at each time in the current time period.
It should be noted that, the transfer subway station transfer traffic prediction method provided by the embodiment of the application may be performed by a transfer subway station transfer traffic prediction device (may also be referred to as a transfer subway station transfer traffic predictor), where the transfer subway station transfer traffic prediction device may be implemented by software and/or hardware. The transfer subway station transfer personnel flow prediction device can be electronic equipment or be configured in the electronic equipment so as to realize a transfer subway station transfer personnel flow prediction function.
The embodiment of the application is described by taking an example that a transfer subway station transfer personnel flow prediction method is configured in electronic equipment.
The electronic device may be any device with computing capability, for example, may be a personal computer, a mobile terminal, a server, etc., and the mobile terminal may be, for example, a vehicle-mounted device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, etc., which have various operating systems, touch screens, and/or display screens.
Among them, it is understood that the transfer subway station and the non-transfer subway station are included in all subway stations of the subway network.
It should be noted that, the value of the time span of the current time period may be determined according to actual requirements, for example, within the current 5 minutes in the subway network, and the embodiment does not specifically limit the value of the time span of the current time period.
Among them, it is understood that the first passenger arrival information is used to indicate the number of passengers who are standing in the subway station.
The first departure time interval is used for indicating the time of the subway station from the next subway to reach the same subway station.
It is understood that the first transfer traffic information is used to indicate traffic of people who perform subway transfer at the transfer subway station.
It should be noted that, the first passenger arrival information, the first departure time interval, and the first transfer traffic information may be acquired from a central control data center of the subway network, which is not described herein.
Step 102, obtaining the station number from each subway station to the transfer subway station in the subway network.
Here, it should be noted that, in each subway station, there are a transfer subway station and a non-transfer subway station, and there are two cases, i.e., a case of a non-transfer subway station to a transfer subway station and a case of a transfer subway station to a transfer subway station, in which the two transfer subway stations are not identical.
Step 103, according to the pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at all times in the current time period, wherein the first correlation matrixes corresponding to the times comprise the probability of passengers entering the subway station at the time to pass through the transfer subway station.
And 104, determining transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the station number from each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station.
It is understood that the future time period is adjacent to the current time period, and the time span of the future time period and the current time period have the same value.
In this example, for each moment in the current time period, constructing a subway network topological graph corresponding to each subway station at the corresponding moment in the current time period according to first passenger arrival information of each subway station at the corresponding moment in the current time period, first transfer personnel flow information of each transfer subway station at the corresponding moment in the current time period, a first correlation matrix corresponding to the corresponding moment in the current time period, the number of stations from each subway station to the transfer subway station and a first transmission time interval, wherein the subway network topological graph comprises a plurality of nodes and directed edges, each node corresponds to one subway station in the subway network, and when the subway station corresponding to the node is the transfer subway station, the node attribute of the node comprises first passenger arrival information of the transfer subway station and first transfer personnel flow information, and when the subway station corresponding to the node is the non-transfer subway station, the node attribute of the node is first passenger arrival information of the non-transfer subway station; the directional edge points to the destination node from the starting node, the directional edge represents that the subway station corresponding to the starting node can reach the subway station corresponding to the destination node, the edge attribute of the directional edge is used for representing the first sending time interval of the subway station corresponding to the starting node and the station number of the subway station corresponding to the starting node reaching the transfer subway station, and the edge weight of the directional edge is used for representing the probability of the transfer of passengers in the transfer subway station through the subway station corresponding to the starting node at the corresponding moment in the current time period.
For example, an example diagram of a metro network topology map corresponding to a time point corresponding to a current time period of a metro network is shown in fig. 2, where it should be noted that in fig. 2、/>、/>、/>、/>、/>And/>For representing nodes in a metro network topology, wherein/>、/>、/>、/>、/>For representing non-transfer subway stations,/>And/>For representing the transfer subway station, the subway station corresponding to the node is/>And/>In the case of subway stations,/>And/>The node attributes of the two nodes are the first passenger arrival information and the first transfer traffic information of the subway station corresponding to each node, and the subway station corresponding to the node is/>、/>、/>、/>、/>In the case of non-transfer subway stations,/>、/>、/>、/>、/>The node attributes of the five nodes are the first passenger arrival information of the subway station corresponding to each node, and in fig. 2, the directed edges are respectively provided with the slave start nodes/>Pointing to destination node/>From/>Direction/>From/>Direction/>From/>Direction/>From/>Direction/>From/>Direction/>From/>Direction/>For example, directed edges are derived from the starting node/>Pointing to destination node/>Representing slave start node/>The corresponding subway station can reach the destination node/>Corresponding subway station, directed edge slave/>Direction/>Is used to represent/>First departure time interval of corresponding subway station/>The corresponding subway station arrives at the station number of the transfer subway station, and the directed edge is from/>Direction/>Is used to represent the warp/>, at the corresponding moment in the current time periodPassenger at corresponding subway station/>Probability of transfer.
In some examples, after a subway network topological graph corresponding to a corresponding moment of a subway network in a current time period is constructed, sequencing the subway network topological graph corresponding to each moment in the current time period according to the time sequence of each moment in the current time period to obtain a graph sequence, and inputting the graph sequence into a pre-trained transfer personnel flow prediction model of a transfer subway station to obtain transfer personnel flow information of the transfer subway station in a future time period after the current time period.
According to the transfer subway station transfer passenger flow prediction method provided by the embodiment of the application, first passenger arrival information and first sending time intervals of each subway station in a subway network at each moment in a current time period are obtained, and first transfer passenger flow information of each subway station in the subway network at each moment in the current time period is obtained; acquiring the station number from each subway station to a transfer subway station in a subway network; according to the pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at each moment in the current time period, wherein the first correlation matrixes corresponding to the moment comprise the probability of passengers entering the subway station at the moment passing through the transfer subway station; and determining the transfer passenger flow information of the transfer subway station in the future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the station number of each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station, thereby accurately realizing the prediction of the transfer passenger flow of the transfer subway station in the future time period according to the pre-trained transfer passenger flow prediction model of the transfer subway station.
In order to clearly understand how the pre-trained correlation matrix model is obtained in the embodiment of the present application, one possible implementation of the pre-trained correlation matrix model is described below with reference to fig. 3. Fig. 3 is a flowchart two of a transfer subway station transfer personnel flow prediction method provided by an embodiment of the application.
As shown in fig. 3, the transfer subway station transfer personnel flow prediction method may include the steps of:
Step 301, obtaining second correlation matrixes corresponding to the subway network at each moment in the first sample time period.
It is understood that the first sample period may be any period of time during operation of the subway network.
In some examples, passenger trajectory information of a subway network at each time point in a first sample time period is acquired, first numbers of passengers who are entered by a subway station at the corresponding time points in the first sample time period and second numbers of passengers who are passed by a transfer subway station through the subway station are determined according to the passenger trajectory information of the subway network at the corresponding time points in the first sample time period for each time point in the first sample time period, the probability that the passengers who are entered by the subway station at the corresponding time points in the first sample time period are passed by the transfer subway station is determined according to the first numbers of the passengers and the second numbers of the passengers, and a second correlation matrix at the corresponding time points in the first sample time period is determined according to the probability that the passengers who are entered by the subway station at the corresponding time points in the first sample time period are passed by the transfer subway station.
The passenger riding track information is used for indicating the action track of the passenger at the subway station.
For example, according to the passenger riding track information at the corresponding time in the first sample time period, assuming that the first number of passengers who are taken in by the subway station at a certain time in the first sample time period is 100, and the second number of passengers who are taken in by the subway station through the transfer subway station is 40, the probability that the passengers who are taken in by the subway station at a certain time in the first sample time period are taken in by the transfer subway station is determined to be 0.4, and according to the probability 0.4, the second correlation matrix of the subway network at a certain time in the first sample time period is determined.
Step 302, obtaining a third correlation matrix corresponding to each time of the subway network in a second sample time period, wherein the second sample time period is a time period after the first sample time period.
The time span of the first sample period is the same as that of the second sample period.
Step 303, ordering the second correlation matrix according to the time sequence at each time in the first sample time period, so as to obtain a correlation matrix sequence.
It is understood that the correlation matrix sequence includes a second correlation matrix at each time instant in the first sample period.
Step 304, inputting the correlation matrix sequence into the initial correlation matrix model to obtain a fourth correlation matrix corresponding to each time in the second sample period.
In some examples, the correlation matrix sequence is input into an initial correlation matrix model to obtain a predicted correlation matrix sequence over the second sample period, and a fourth correlation matrix corresponding to each time instant in the second sample period is obtained according to the predicted correlation matrix sequence.
It can be understood that the predicted correlation matrix sequence includes fourth correlation matrices at each time point in the second sample period, where the fourth correlation matrices at each time point are ordered according to the chronological order.
Step 305, training the initial correlation matrix model according to the third correlation matrix and the fourth correlation matrix to obtain a pre-trained correlation matrix model.
According to the transfer subway station transfer personnel flow prediction method provided by the embodiment of the application, the second correlation matrix corresponding to each moment of the subway network in the first sample time period is obtained, the third correlation matrix corresponding to each moment of the subway network in the second sample time period is obtained, wherein the second sample time period is a time period after the first sample time period, the second correlation matrix is ordered according to the time sequence of each moment in the first sample time period, so as to obtain a correlation matrix sequence, the correlation matrix sequence is input into an initial correlation matrix model, so as to obtain the fourth correlation matrix corresponding to each moment in the second sample time period, the initial correlation matrix model is trained according to the third correlation matrix and the fourth correlation matrix, so that the correlation matrix corresponding to each moment of the subway network in any time period can be conveniently determined according to the pre-trained correlation matrix model, and a large amount of time is saved.
In order to clearly understand how the transfer subway station transfer traffic prediction model is obtained, one possible implementation of the transfer subway station transfer traffic prediction model is described in the following with reference to fig. 4. Fig. 4 is a flowchart III of a transfer subway station transfer personnel flow prediction method provided by an embodiment of the application.
As shown in fig. 4, the transfer subway station transfer personnel flow prediction method may include the steps of:
step 401, obtaining second passenger arrival information and second departure time intervals of each subway station at each time in a third sample time period.
Wherein, it is understood that the third sample period may be any period of time during operation of the subway network.
Step 402, obtaining third transfer people flow information of the transfer subway station at each time point in a third sample time period and a transfer people flow actual value in a fourth sample time period, wherein the fourth sample time period is a time period after the third sample time period.
It is understood that the third sample period is the same as the fourth sample period in terms of time span.
Step 403, determining respective corresponding second correlation matrixes at different time points in the third sample time period according to the pre-trained correlation matrix model.
It should be noted that, for the specific description of the steps 401 to 403, reference may be made to the related description in the embodiment of the present application, and the detailed description is omitted herein.
And step 404, determining a transfer passenger flow prediction value of the transfer subway station in a fourth sample time period according to the second passenger arrival information, the second departure time interval, the third transfer passenger flow information, the station number of each subway station to the transfer subway station and the second correlation matrix by adopting an initial transfer passenger flow prediction model of the transfer subway station.
In some examples, for each moment in the third sample period, a subway network topology map corresponding to the subway network at the corresponding moment in the third sample period is constructed according to the second passenger arrival information, the second departure time interval, the third transfer traffic information, the station number of each subway station to the transfer subway station and the second correlation matrix, and the subway network topology map corresponding to the corresponding moment in the third sample period is input into the initial transfer traffic prediction model of the transfer subway station to obtain a transfer traffic prediction value of the transfer subway station in the fourth sample period.
In some examples, for each moment in the third sample period, constructing a subway network topological graph corresponding to each moment in the third sample period according to second passenger arrival information of each subway station at the corresponding moment in the third sample period, third transfer person flow information of the transfer subway station at the corresponding moment in the third sample period, a second correlation matrix corresponding to the corresponding moment in the third sample period, the number of stations of each subway station to the transfer subway station and a second departure time interval, wherein the subway network topological graph comprises a plurality of nodes and directed edges, each node corresponds to one subway station in the subway network, node attributes of the nodes comprise second passenger arrival information of the transfer subway station and third transfer person flow information when the subway station corresponding to the node is the transfer subway station, and node attributes of the nodes are second passenger arrival information of the non-transfer subway station when the subway station corresponding to the node is the non-transfer subway station; the directional edge points to the destination node from the starting node, the directional edge represents that the subway station corresponding to the starting node can reach the subway station corresponding to the destination node, the edge attribute of the directional edge is used for representing the second departure time interval of the subway station corresponding to the starting node and the station number of the subway station corresponding to the starting node reaching the transfer subway station, and the edge weight of the directional edge is used for representing the probability of the transfer of passengers passing through the subway station corresponding to the starting node in the transfer subway station at the corresponding moment in the third sample time period.
It should be noted that, for the specific description of step 404, reference may be made to the related description of step 104 in the embodiment of the present application, which is not repeated here.
And step 405, training an initial transfer subway station transfer traffic prediction model according to the actual transfer traffic value and the transfer traffic prediction value to obtain a pre-trained transfer subway station transfer traffic prediction model.
The transfer subway station transfer traffic prediction method provided by the embodiment of the application obtains second passenger arrival information and second departure time intervals of each subway station at each moment in a third sample time period, obtains third transfer traffic information of each moment in the third sample time period of the transfer subway station and a transfer traffic actual value in a fourth sample time period, wherein the fourth sample time period is a time period after the third sample time period, a second correlation matrix corresponding to each moment in different time periods in the third sample time period is determined according to a pre-trained correlation matrix model, an initial transfer subway station transfer traffic prediction model is adopted, a transfer subway station transfer traffic predicted value of each transfer subway station in the fourth sample time period is determined according to the second passenger arrival information, the second departure time interval, the third transfer traffic information, the station number of each transfer subway station to the transfer subway station and the second correlation matrix, the transfer subway station transfer traffic actual value of the transfer subway station in the fourth sample time period is determined according to the transfer subway station transfer traffic actual value and the first correlation matrix, the transfer subway station transfer traffic prediction model of each first sample time period is trained according to the first transfer traffic of the second passenger arrival information, the second transfer subway station transfer traffic prediction model is realized in the first transfer subway station and the second transfer subway station actual value of the first transfer subway station, the first transfer subway station transfer traffic prediction model is realized according to the first transfer traffic of the first transfer subway station in the first sample time period, the accuracy of the transfer subway station transfer personnel flow prediction model for predicting the transfer subway station transfer personnel flow in the future time period is improved.
In order to achieve the above embodiment, the application further provides a transfer subway station transfer personnel flow prediction device.
Fig. 5 shows a schematic structural diagram of a transfer passenger flow prediction device for a transfer subway station according to an embodiment of the present application.
As shown in fig. 5, the transfer subway station transfer passenger flow rate prediction apparatus 500 includes: a first acquisition module 501, a second acquisition module 502, a first determination module 503, and a second determination module 504.
A first obtaining module 501, configured to obtain first passenger arrival information and a first departure time interval of each subway station in a subway network at each time in a current time period, and obtain first transfer passenger flow information of a transfer subway station in the subway network at each time in the current time period;
A second obtaining module 502, configured to obtain the number of stations from each subway station to a transfer subway station in the subway network;
a first determining module 503, configured to determine, according to the pre-trained correlation matrix model, respective first correlation matrices corresponding to respective times in the current time period of the subway network, where the first correlation matrices corresponding to the times include probabilities of passengers entering from the subway station at the times passing through the transfer subway station;
The second determining module 504 is configured to determine, using a pre-trained transfer passenger flow prediction model of the transfer subway station, transfer passenger flow information in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the number of stations from each subway station to the transfer subway station, and the first correlation matrix.
In one embodiment of the present application, the second determining module 504 is specifically configured to:
Aiming at each moment in the current time period, constructing a subway network topological graph corresponding to the moment in the current time period according to first passenger arrival information of each subway station at the corresponding moment in the current time period, first transfer personnel flow information of the transfer subway station at the corresponding moment in the current time period, a first correlation matrix corresponding to the corresponding moment in the current time period, the station number of each subway station to the transfer subway station and a first transmission time interval, wherein each node in the subway network topological graph comprises a plurality of nodes and directed edges, each node corresponds to one subway station in the subway network, the node attribute of the node comprises first passenger arrival information of the transfer subway station and first transfer personnel flow information when the subway station corresponding to the node is the transfer subway station, and the node attribute of the node is the first passenger arrival information of the non-transfer subway station when the subway station corresponding to the node is the non-transfer subway station; the directional edge points to the destination node from the starting node, the directional edge represents that the subway station corresponding to the starting node can reach the subway station corresponding to the destination node, the edge attribute of the directional edge is used for representing the first sending time interval of the subway station corresponding to the starting node and the station number of the subway station corresponding to the starting node reaching the transfer subway station, and the edge weight of the directional edge is used for representing the probability of the transfer of passengers in the transfer subway station through the subway station corresponding to the starting node at the corresponding moment in the current time period;
sequencing the subway network topological graphs corresponding to each moment in the current time period according to the time sequence of each moment in the current time period to obtain a graph sequence;
and inputting the graph sequence into a pre-trained transfer passenger flow rate prediction model of the transfer subway station to obtain transfer passenger flow rate information of the transfer subway station in a future time period after the current time period.
In one embodiment of the present application, the apparatus further comprises a correlation matrix model module 505 comprising:
A first obtaining unit 5051, configured to obtain second correlation matrices of the metro network at respective times in a first sample period;
a second obtaining unit 5052, configured to obtain a third correlation matrix corresponding to each time of the subway network in a second sample period, where the second sample period is a period after the first sample period;
A first determining unit 5053, configured to sort the second correlation matrix according to the chronological order at each time in the first sample period, so as to obtain a correlation matrix sequence;
A second determining unit 5054, configured to input the correlation matrix sequence into an initial correlation matrix model, so as to obtain a fourth correlation matrix corresponding to each moment in the second sample period;
The first training unit 5055 is configured to train the initial correlation matrix model according to the third correlation matrix and the fourth correlation matrix to obtain a pre-trained correlation matrix model.
In one embodiment of the present application, the first acquiring unit 5051 is specifically configured to:
acquiring passenger riding track information of a subway network at each moment in a first sample time period;
For each time in a first sample time period, determining a first number of passengers which are taken in by a subway station at the corresponding time in the first sample time period and a second number of passengers which are taken in by the subway station through the subway station and are transferred by the transfer subway station according to the passenger track information of the subway network at the corresponding time in the first sample time period;
Determining the probability of passengers entering from the subway station at corresponding moments in the first sample time period to pass through the transfer subway station according to the first number of people and the second number of people;
and determining a second correlation matrix of the subway network at the corresponding time in the first sample time period according to the probability that the passengers entering the subway station at the corresponding time in the first sample time period pass through the transfer subway station.
In one embodiment of the present application, the apparatus further includes a transfer subway station transfer people flow prediction model module 506, including:
A third acquiring unit 5061, configured to acquire second passenger arrival information and a second departure time interval of each subway station at each time point in a third sample period;
A fourth obtaining unit 5062, configured to obtain third transfer passenger flow information of the transfer subway station at each time point in a third sample period and an actual transfer passenger flow value in a fourth sample period, where the fourth sample period is a period after the third sample period;
a third determining unit 5063, configured to determine, according to the pre-trained correlation matrix model, respective corresponding second correlation matrices at different respective moments in the third sample period;
A fourth determining unit 5064, configured to determine, using an initial transfer subway station transfer traffic prediction model, a transfer subway station transfer traffic prediction value in the fourth sample period according to second passenger arrival information, a second departure time interval, third transfer traffic information, a station number of each subway station to the transfer subway station, and a second correlation matrix;
The second training unit 5065 is configured to train the initial transfer subway station transfer traffic prediction model according to the actual transfer traffic value and the transfer traffic prediction value, so as to obtain a pre-trained transfer subway station transfer traffic prediction model.
According to the transfer subway station transfer passenger flow prediction device provided by the embodiment of the application, first passenger arrival information and first sending time intervals of each subway station in a subway network at each moment in a current time period are obtained, and first transfer passenger flow information of each subway station in the subway network at each moment in the current time period is obtained; acquiring the station number from each subway station to a transfer subway station in a subway network; according to the pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at each moment in the current time period, wherein the first correlation matrixes corresponding to the moment comprise the probability of passengers entering the subway station at the moment passing through the transfer subway station; and determining the transfer passenger flow information of the transfer subway station in the future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the station number of each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station, thereby accurately realizing the prediction of the transfer passenger flow of the transfer subway station in the future time period according to the pre-trained transfer passenger flow prediction model of the transfer subway station.
It should be noted that the explanation of the foregoing embodiment of the transfer passenger flow rate prediction method for the transfer subway station is also applicable to the transfer passenger flow rate prediction device for the transfer subway station in this embodiment, and will not be repeated here.
In order to achieve the above embodiment, the present application further provides an electronic device.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 6, the electronic device 600 may include: a processor 602, and a memory 603 communicatively coupled to the processor 602; memory 603 stores computer-executable instructions; the processor 602 executes computer-executable instructions stored in the memory 603 to implement the methods provided by the previous embodiments.
Further, the electronic device 600 further includes:
a transceiver 601 for communication between a memory 603 and a processor 602.
The memory 603 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
If the memory 603, the processor 602, and the transceiver 601 are implemented independently, the transceiver 601, the memory 603, and the processor 602 may be connected to each other and perform communication with each other through a bus. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (PERIPHERAL COMPONENT, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 603, the processor 602, and the transceiver 601 are integrated on a chip, the memory 603, the processor 602, and the transceiver 601 may communicate with each other through an internal interface.
The processor 602 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the application.
In order to implement the above embodiment, the present application also proposes a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor are configured to implement the method provided in the foregoing embodiment.
In order to implement the above embodiments, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above embodiments.
The processing of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user in the application accords with the regulations of related laws and regulations and does not violate the popular regulations of the public order.
It should be noted that personal information from users should be collected for legitimate and reasonable uses and not shared or sold outside of these legitimate uses. In addition, such collection/sharing should be performed after receiving user informed consent, including but not limited to informing the user to read user agreements/user notifications and signing agreements/authorizations including authorization-related user information before the user uses the functionality. In addition, any necessary steps are taken to safeguard and ensure access to such personal information data and to ensure that other persons having access to the personal information data adhere to their privacy policies and procedures.
The present application contemplates embodiments that may provide a user with selective prevention of use or access to personal information data. That is, the present disclosure contemplates that hardware and/or software may be provided to prevent or block access to such personal information data. Once personal information data is no longer needed, risk can be minimized by limiting data collection and deleting data. In addition, personal identification is removed from such personal information, as applicable, to protect the privacy of the user.
In the foregoing description of embodiments, reference has been made to the terms "one embodiment," "some embodiments," "example," "a particular example," or "some examples," etc., meaning that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (13)
1. The transfer passenger flow prediction method for the transfer subway station is characterized by comprising the following steps of:
Acquiring first passenger arrival information and first departure time intervals of each subway station in a subway network at each time in a current time period, and acquiring first transfer passenger flow information of transfer subway stations in the subway network at each time in the current time period;
acquiring the station number from each subway station to the transfer subway station in the subway network;
According to a pre-trained correlation matrix model, determining respective corresponding first correlation matrixes of the subway network at all times in the current time period, wherein the first correlation matrixes corresponding to the times comprise the probability that passengers entering the subway station at the times pass through the transfer subway station;
And determining transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first sending time interval, the first transfer passenger flow information, the station number from each subway station to the transfer subway station and the first correlation matrix by adopting a pre-trained transfer passenger flow prediction model of the transfer subway station.
2. The method of claim 1, wherein said determining transfer traffic information for the transfer subway station in a future time period subsequent to the current time period using a pre-trained transfer subway station transfer traffic prediction model based on the first passenger arrival information, the first departure time interval, the first transfer traffic information, the number of stations of each subway station to the transfer subway station, and the first correlation matrix comprises:
Constructing a subway network topological graph corresponding to the subway network at the corresponding moment in the current time period according to the first passenger arrival information of each subway station at the corresponding moment in the current time period, the first transfer person flow information of the transfer subway station at the corresponding moment in the current time period, the first correlation matrix corresponding to the corresponding moment in the current time period, the number of stations from each subway station to the transfer subway station and the first transmission time interval, wherein the subway network topological graph comprises a plurality of nodes and directed edges, each node corresponds to one subway station in the subway network, the node attribute of the node comprises the first passenger arrival information of the transfer subway station and the first transfer person flow information when the subway station corresponding to the node is the transfer subway station, and the node attribute of the node is the first subway station information of the non-transfer subway station when the subway station corresponding to the node is the non-transfer subway station; the directional edge points to a destination node from a starting node, the directional edge represents that a subway station corresponding to the destination node can be reached from a subway station corresponding to the starting node, the edge attribute of the directional edge is used for representing a first sending time interval of the subway station corresponding to the starting node and the number of stations of the subway station corresponding to the starting node for reaching the transfer subway station, and the edge weight of the directional edge is used for representing the probability of a passenger passing through the subway station corresponding to the starting node in the transfer subway station at a corresponding moment in the current time period;
Sequencing subway network topological graphs corresponding to each moment in the current time period according to the time sequence of each moment in the current time period to obtain a graph sequence;
And inputting the graph sequence into a pre-trained transfer subway station transfer personnel flow prediction model to obtain transfer personnel flow information of the transfer subway station in a future time period after the current time period.
3. The method according to claim 1, wherein the pre-trained correlation matrix model is obtained by:
acquiring respective corresponding second correlation matrixes of the subway network at all moments in a first sample time period;
acquiring a third correlation matrix corresponding to each moment of the subway network in a second sample time period, wherein the second sample time period is a time period after the first sample time period;
sequencing the second correlation matrix according to the time sequence at each time in the first sample time period to obtain a correlation matrix sequence;
Inputting the correlation matrix sequence into an initial correlation matrix model to obtain a fourth correlation matrix corresponding to each moment in the second sample time period;
and training the initial correlation matrix model according to the third correlation matrix and the fourth correlation matrix to obtain the pre-trained correlation matrix model.
4. A method according to claim 3, wherein said obtaining a second correlation matrix for each of said subway networks at each instant in time within a first sample period comprises:
Acquiring passenger riding track information of the subway network at each moment in a first sample time period;
Determining, for each time in the first sample period, a first number of passengers arriving from the subway station at the corresponding time in the first sample period and a second number of passengers arriving from the subway station via the subway station and transferred via the transfer subway station according to passenger riding track information of the subway network at the corresponding time in the first sample period;
Determining the probability of passengers entering the subway station at corresponding moments in the first sample time period passing through the transfer subway station according to the first number of people and the second number of people;
And determining a second correlation matrix of the subway network at the corresponding moment in the first sample time period according to the probability that the passengers entering the subway station at the corresponding moment in the first sample time period pass through the transfer subway station.
5. The method according to claim 1, wherein the transfer subway station transfer people flow prediction model is obtained by the following steps:
acquiring second passenger arrival information and second departure time intervals of each subway station at each moment in a third sample time period;
Acquiring third transfer passenger flow information of the transfer subway station at each moment in the third sample time period and a transfer passenger flow actual value in a fourth sample time period, wherein the fourth sample time period is a time period after the third sample time period;
Determining respective corresponding second correlation matrixes at different moments in the third sample time period according to the pre-trained correlation matrix model;
Adopting an initial transfer subway station transfer traffic prediction model, and determining a transfer traffic prediction value of the transfer subway station in the fourth sample time period according to the second passenger arrival information, the second departure time interval, the third transfer traffic information, the station number from each subway station to the transfer subway station and the second correlation matrix;
And training the initial transfer subway station transfer personnel flow prediction model according to the transfer personnel flow actual value and the transfer personnel flow prediction value to obtain a pre-trained transfer subway station transfer personnel flow prediction model.
6. A transfer subway station transfer people flow prediction device, characterized by comprising:
The first acquisition module is used for acquiring first passenger arrival information and first sending time intervals of each subway station in the subway network at each time in the current time period and acquiring first transfer passenger flow information of the transfer subway stations in the subway network at each time in the current time period;
the second acquisition module is used for acquiring the station number from each subway station to the transfer subway station in the subway network;
the first determining module is used for determining first correlation matrixes corresponding to all moments in the current time period of the subway network according to the pre-trained correlation matrix model, wherein the first correlation matrixes corresponding to the moments comprise the probability that passengers entering the subway station at the moments pass through the transfer subway station;
The second determining module is configured to determine transfer passenger flow information of the transfer subway station in a future time period after the current time period according to the first passenger arrival information, the first departure time interval, the first transfer passenger flow information, the number of stations from each subway station to the transfer subway station, and the first correlation matrix by using a pre-trained transfer passenger flow prediction model of the transfer subway station.
7. The apparatus of claim 6, wherein the second determining module is specifically configured to:
Constructing a subway network topological graph corresponding to the subway network at the corresponding moment in the current time period according to the first passenger arrival information of each subway station at the corresponding moment in the current time period, the first transfer person flow information of the transfer subway station at the corresponding moment in the current time period, the first correlation matrix corresponding to the corresponding moment in the current time period, the number of stations from each subway station to the transfer subway station and the first transmission time interval, wherein the subway network topological graph comprises a plurality of nodes and directed edges, each node corresponds to one subway station in the subway network, the node attribute of the node comprises the first passenger arrival information of the transfer subway station and the first transfer person flow information when the subway station corresponding to the node is the transfer subway station, and the node attribute of the node is the first subway station information of the non-transfer subway station when the subway station corresponding to the node is the non-transfer subway station; the directional edge points to a destination node from a starting node, the directional edge represents that a subway station corresponding to the destination node can be reached from a subway station corresponding to the starting node, the edge attribute of the directional edge is used for representing a first sending time interval of the subway station corresponding to the starting node and the number of stations of the subway station corresponding to the starting node for reaching the transfer subway station, and the edge weight of the directional edge is used for representing the probability of a passenger passing through the subway station corresponding to the starting node in the transfer subway station at a corresponding moment in the current time period;
Sequencing subway network topological graphs corresponding to each moment in the current time period according to the time sequence of each moment in the current time period to obtain a graph sequence;
And inputting the graph sequence into a pre-trained transfer subway station transfer personnel flow prediction model to obtain transfer personnel flow information of the transfer subway station in a future time period after the current time period.
8. The apparatus of claim 6, further comprising a correlation matrix model module comprising:
The first acquisition unit is used for acquiring second correlation matrixes corresponding to the subway network at each moment in a first sample time period;
a second obtaining unit, configured to obtain third correlation matrices corresponding to respective times in a second sample period of time of the metro network, where the second sample period of time is a period of time after the first sample period of time;
The first determining unit is used for sequencing the second correlation matrix according to the time sequence at each time in the first sample time period to obtain a correlation matrix sequence;
The second determining unit is used for inputting the correlation matrix sequence into an initial correlation matrix model to obtain a fourth correlation matrix corresponding to each moment in the second sample time period;
And the first training unit is used for training the initial correlation matrix model according to the third correlation matrix and the fourth correlation matrix to obtain the pre-trained correlation matrix model.
9. The apparatus according to claim 8, wherein the first acquisition unit is specifically configured to:
Acquiring passenger riding track information of the subway network at each moment in a first sample time period;
Determining, for each time in the first sample period, a first number of passengers arriving from the subway station at the corresponding time in the first sample period and a second number of passengers arriving from the subway station via the subway station and transferred via the transfer subway station according to passenger riding track information of the subway network at the corresponding time in the first sample period;
Determining the probability of passengers entering the subway station at corresponding moments in the first sample time period passing through the transfer subway station according to the first number of people and the second number of people;
And determining a second correlation matrix of the subway network at the corresponding moment in the first sample time period according to the probability that the passengers entering the subway station at the corresponding moment in the first sample time period pass through the transfer subway station.
10. The apparatus of claim 6, further comprising a transfer subway station transfer people flow prediction model module comprising:
The third acquisition unit is used for acquiring second passenger arrival information and second departure time intervals of each subway station at each moment in a third sample time period;
a fourth obtaining unit, configured to obtain third transfer person flow information of the transfer subway station at each time point in the third sample period and an actual transfer person flow value in a fourth sample period, where the fourth sample period is a period after the third sample period;
The third determining unit is used for determining respective corresponding second correlation matrixes at different moments in the third sample time period according to the pre-trained correlation matrix model;
A fourth determining unit, configured to determine a transfer passenger flow prediction value of the transfer subway station in the fourth sample period according to the second passenger arrival information, the second departure time interval, the third transfer passenger flow information, the number of stations from each subway station to the transfer subway station, and the second correlation matrix by using an initial transfer passenger flow prediction model;
The second training unit is used for training the initial transfer subway station transfer personnel flow prediction model according to the transfer personnel flow actual value and the transfer personnel flow prediction value so as to obtain a pre-trained transfer subway station transfer personnel flow prediction model.
11. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-5.
12. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-5.
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