CN113065701B - Intelligent prediction method and device for rail transit passenger flow - Google Patents
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Abstract
The application discloses an intelligent prediction method and device for rail transit passenger flow volume, wherein the method comprises the following steps: acquiring a place corresponding to a subway station on a preset subway line; searching a data acquisition interface corresponding to the place, and acquiring information of an event through the data acquisition interface; predicting and acquiring normal passenger flow in a time period of event holding according to historical data, wherein the normal passenger flow is the passenger flow in the time period under the condition that any event is not held; generating a passenger flow alleviation plan of a subway station corresponding to a place in a time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number and is more than or equal to 1.5; the plan is sent to the manager's mobile terminal. The method solves the problem that the passenger flow variation method of the rail traffic caused by irregular large-scale events cannot be predicted in the prior art, and improves the accuracy of passenger flow prediction to a certain extent.
Description
Technical Field
The application relates to the field of rail transit, in particular to an intelligent prediction method and device for the passenger flow of rail transit.
Background
The subway passenger flow data is accurately predicted, and the subway passenger flow data has a vital effect on subway capacity arrangement, future development and construction and function planning. Therefore, accurate prediction of subway passenger flow volume becomes an important subject in subway operation management, and is a basis for effective configuration of subway resources.
To solve this problem, chinese patent publication CN107688873a discloses a subway passenger flow prediction method based on big data analysis, in which the following method is described: extracting passenger flow data from a passenger flow database and importing the passenger flow data into a big data storage system; reading daily time-sharing passenger flow data and/or daily accumulated passenger flow data from the big data storage system; carrying out passenger flow prediction modeling by utilizing an RNN model according to the daily time-sharing passenger flow data and/or the daily accumulated passenger flow data, and optimizing to obtain passenger flow prediction data; and continuously optimizing the prediction model parameters according to the daily time-sharing passenger flow data and/or the daily accumulated passenger flow data and passenger flow prediction data, so as to improve the accuracy of the next prediction.
It follows that historical data is used in this method, which is problematic for estimating traffic flow for daily traffic without incidents, but is ineffective for when a concert or large race occurs.
Disclosure of Invention
The embodiment of the application provides an intelligent prediction method and device for the passenger flow of rail transit, which at least solve the problem that the passenger flow variation of the rail transit caused by irregular large-scale events cannot be predicted in the prior art.
According to one aspect of the application, an intelligent prediction method for the passenger flow volume of rail transit is provided, which comprises the following steps: acquiring places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value; searching a data acquisition interface corresponding to the place, and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event; predicting and acquiring normal passenger flow in a time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow in the time period where any event is not held; generating a passenger flow alleviation plan of a subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5; and sending the plan to the mobile terminal of the manager.
Further, generating a passenger flow alleviation plan of the subway station corresponding to the place in the time period comprises: and when the N is more than 1.5 and less than or equal to 2, determining to increase the frequency of vehicles passing through the subway station corresponding to the place.
Further, generating a passenger flow alleviation plan of the subway station corresponding to the place in the time period comprises: and under the condition that N is larger than 2, determining the number of people entering the subway station corresponding to the place.
Further, the data acquisition interface corresponding to the location includes at least one of: and the data interface of the website corresponding to the place and the data interface of the software or the application corresponding to the place.
According to another aspect of the present application, there is also provided an intelligent prediction apparatus for rail transit passenger flow volume, including: the subway station acquisition module is used for acquiring places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value; the searching module is used for searching a data acquisition interface corresponding to the place and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event; the prediction module is used for predicting and acquiring normal passenger flow in a time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow of the time period where no event is held in the place; the generation module is used for generating a passenger flow alleviation plan of the subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5; and the sending module is used for sending the plan to the mobile terminal of the manager.
Further, the generating module is configured to: and when the N is more than 1.5 and less than or equal to 2, determining to increase the frequency of vehicles passing through the subway station corresponding to the place.
Further, the generating module is configured to: and under the condition that N is larger than 2, determining the number of people entering the subway station corresponding to the place.
Further, the data acquisition interface corresponding to the location includes at least one of: and the data interface of the website corresponding to the place and the data interface of the software or the application corresponding to the place.
According to another aspect of the present application, there is also provided a memory for storing software for performing the above-described method.
According to another aspect of the present application, there is also provided a processor for running software for performing the above method.
In the embodiment of the application, the method comprises the steps of acquiring places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value; searching a data acquisition interface corresponding to the place, and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event; predicting and acquiring normal passenger flow in a time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow in the time period where any event is not held; generating a passenger flow alleviation plan of a subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5; and sending the plan to the mobile terminal of the manager. The method solves the problem that the passenger flow variation method of the rail traffic caused by irregular large-scale events cannot be predicted in the prior art, and improves the accuracy of passenger flow prediction to a certain extent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
fig. 1 is a flowchart of an intelligent prediction method for rail transit passenger flow according to an embodiment of the present application.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
In this embodiment, an intelligent prediction method for rail transit passenger flow is provided, fig. 1 is a flowchart of an intelligent prediction method for rail transit passenger flow according to an embodiment of the present application, as shown in fig. 1, and the method includes the following steps:
step S102, obtaining places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value;
step S104, searching a data acquisition interface corresponding to the place, and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event;
for example, a stadium may have its own website, or its own APP, through which the physical activities held by the stadium may be obtained.
As an alternative embodiment, the maximum number of persons admitted to the event may be the maximum number of persons admitted to the venue, or the maximum number of persons admitted to the venue on the day of the event's occurrence predicted from the current ticket sales situation. A relationship function between sales of event tickets of the same type historically held at the venue and time may be obtained, e.g., five historical relationship functions of sales of the same type held at the venue may be obtained, and a predictive function may be generated based on a weighted average of the five relationship functions, wherein the time is approximately closer to the present weight. After the prediction function is generated, according to the first time when the information of the event is acquired and the number of tickets sold at the first time, the sales number of tickets on the same day of the event is obtained through the prediction function, and the sales number of tickets on the same day of the event is taken as the maximum number of people admitted in the event.
Step S106, predicting and acquiring normal passenger flow in the time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow in the time period where any event is not held;
there are many such methods of predicting normal passenger flow. For example, the passenger flow volume at the same time 10 weeks before can be counted, and then the average value can be taken. Or some other approach may be used, such as:
extracting time, weather conditions, historical passengers, faithful passengers and site function features. Wherein, time division: dividing a day into n time slices in units of one hour from 6:00 a.m. to 22:00 a.m.; extracting weather features, and dividing the weather features into three types: a sunny day, a rainy day and cloudy weather, and performing standardization treatment, namely, taking a value of 1 in the sunny day, taking a value of 0 in the rainy day and taking a value of 0.5 in the cloudy day; faithful number of passengers: in a certain time slice, passengers with card swiping records every day are faithful passengers of the time slice; according to the card swiping information of subway passengers, dividing the subway passengers into time slices according to steps, and extracting the number of faithful passengers in each time slice; the subway stations are classified according to functions: scenic spots, points of interest ancient areas, business areas, residential areas, high and new technology areas and leisure and entertainment areas; historical number of passengers: according to the divided time slices, averaging the number of passengers in the time slice of all the predicted days before a certain time slice of a certain day, namely defining the number as the historical number of passengers in the time slice;
and establishing a multiple linear regression model. Four influencing factors, namely time, weather conditions, historical passengers and faithful passengers, are selected as input variables, and only one factor, namely the actual passengers of the subway station in a certain time slice, is selected as output variables; training set and test set partitioning: taking the data three days after the extracted data set as a test set and the rest data as a training set; data normalization of training and test sets: respectively carrying out standardization treatment on the training set and the testing set, wherein the treated data accords with standard normal distribution, namely, the mean value is 0, and the standard deviation is 1; s3.4: establishing a multiple linear regression model: the subway passenger flow is influenced by a plurality of factors such as time, weather conditions, historical passengers and faithful passengers, a multi-linear regression model is used for establishing a regression model between the subway passenger flow and the factors according to training set data, quantitative analysis of the relation between the passenger flow and the factors is realized, and the relation between the passenger flow and the factors is intuitively reflected; inputting the standardized training set data into a multiple linear regression model for training and learning; predicting the number of passengers: and inputting the standardized test set data into a trained multiple linear regression model, predicting the number of passengers of the subway station, and outputting a predicted result value by the multiple linear regression model.
Step S108, generating a passenger flow alleviation plan of the subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5;
in the case where the maximum number does not exceed the normal passenger flow by much, there is no need to adjust the capacity, and therefore, N is defined as 1.5 at this time as the boundary point where the capacity needs to be adjusted.
And step S110, the plan is sent to the mobile terminal of the manager. Optionally, the passenger flow alleviation technology further includes a capacity adjustment plan of intersecting lines of the lines where the subway stations corresponding to the sites are located, and capacity adjustment of the intersecting lines is synchronized with capacity adjustment of the lines where the subway stations are located, that is, the same percentage is increased together.
Optionally, as an implementation manner that can be increased, after the passenger flow alleviation plan is sent to the mobile terminal of the manager, the manager displays the passenger flow alleviation plan through software on the mobile terminal, then the software receives the determination information sent by the manager, and the software sends a regulation command to the subway regulation system according to the confirmation information, wherein the regulation command is used for regulating trains according to the passenger flow alleviation plan.
And judging whether the regulation scheme indicated by the regulation command reaches the maximum capacity of the subway, and if so, sending a coordination command to an application of a traffic management department, wherein the coordination command is used for indicating the traffic management department to coordinate traffic in the time period of the event.
As another alternative embodiment, after the generated traffic mitigation plan is executed, the type and time period of the event and the information of the venue (for example, the size of the venue, the distance of the venue from the subway) and the normal traffic during the time period in which the event occurs may be stored in correspondence with the generated traffic mitigation plan as a set of training data for training the model for machine learning. Judging whether the stored training data exceeds a preset data amount, and if so, training. When the trained model is to be used in the future, information of the type and time period of the event and the place (for example, the size of the place, the distance of the place from the subway) and normal passenger flow in the time period when the event occurs are input into the model, and the model outputs a passenger flow alleviation plan.
The method solves the problem that the passenger flow variation of the rail traffic caused by irregular large-scale events cannot be predicted in the prior art, and improves the accuracy of passenger flow prediction to a certain extent.
Preferably, generating the passenger flow alleviation plan of the subway station corresponding to the place in the time period includes: and when the N is more than 1.5 and less than or equal to 2, determining to increase the frequency of vehicles passing through the subway station corresponding to the place.
Preferably, generating the passenger flow alleviation plan of the subway station corresponding to the place in the time period includes: and under the condition that N is larger than 2, determining the number of people entering the subway station corresponding to the place.
Preferably, the data acquisition interface corresponding to the location includes at least one of: and the data interface of the website corresponding to the place and the data interface of the software or the application corresponding to the place.
In this embodiment, there is provided an electronic device including a memory in which a computer program is stored, and a processor configured to run the computer program to perform the method in the above embodiment.
In this embodiment, there is also provided an intelligent predicting apparatus for rail transit passenger flow, which may be understood as a computer program, and these computer programs may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer implemented process, so that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and the corresponding steps may be implemented by different modules.
The device comprises: the subway station acquisition module is used for acquiring places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value; the searching module is used for searching a data acquisition interface corresponding to the place and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event; the prediction module is used for predicting and acquiring normal passenger flow in a time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow of the time period where no event is held in the place; the generation module is used for generating a passenger flow alleviation plan of the subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5; and the sending module is used for sending the plan to the mobile terminal of the manager.
Preferably, the generating module is configured to: and when the N is more than 1.5 and less than or equal to 2, determining to increase the frequency of vehicles passing through the subway station corresponding to the place.
Preferably, the generating module is configured to: and under the condition that N is larger than 2, determining the number of people entering the subway station corresponding to the place.
Preferably, the data acquisition interface corresponding to the location includes at least one of: and the data interface of the website corresponding to the place and the data interface of the software or the application corresponding to the place.
The above-described programs may be run on a processor or may also be stored in memory (or referred to as computer-readable media), including both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technique. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (4)
1. An intelligent prediction method for the passenger flow of rail transit is characterized by comprising the following steps:
acquiring places corresponding to subway stations on a preset subway line, wherein the distance from one or more places corresponding to each subway station to the subway station is smaller than or equal to a threshold value;
searching a data acquisition interface corresponding to the place, and acquiring information of an event through the data acquisition interface, wherein the event is an event held in the place, and the information of the event comprises: a time period during which the event occurs, and a maximum number of people the venue receives during the event; wherein the maximum number of admission persons for the event is the maximum number of persons entering the place on the day of occurrence of the event predicted from the current ticket sales situation, wherein a relation function between the sales situation and time of event tickets of the same type held historically in the place is obtained, wherein a relation function of sales situations of the same type held historically in five times is obtained, and a prediction function is generated according to a weighted average of the relation functions of the five times, wherein the closer the time is to the current weight; after the prediction function is generated, according to the first time when the information of the event is acquired and the number of tickets sold at the first time, the sales number of tickets on the same day of the event is obtained through the prediction function, and the sales number of tickets on the same day of the event is used as the maximum number of people admitted in the event;
predicting and acquiring normal passenger flow in a time period where the event is held according to the historical data, wherein the normal passenger flow is the passenger flow in the time period where any event is not held;
generating a passenger flow alleviation plan of a subway station corresponding to the place in the time period under the condition that the maximum number is N times of the normal passenger flow, wherein N is a positive number, and N is more than or equal to 1.5;
the plan is sent to a mobile terminal of a manager;
after the passenger flow relief plan is sent to the mobile terminal of the manager, the passenger flow relief plan is displayed through software on the mobile terminal, the software receives the determination information sent by the manager, and the software sends a regulation command to a subway regulation and control system according to the determination information, wherein the regulation and control command is used for regulating and controlling a train according to the passenger flow relief plan; judging whether a regulation scheme indicated by the regulation command reaches the maximum capacity of the subway, and if so, sending a coordination command to an application of a traffic management department, wherein the coordination command is used for indicating the traffic management department to coordinate traffic in a time period of the event;
after the generated passenger flow relief plan is executed, storing the information of the type and time period of the event and the place and the normal passenger flow in the time period of the event as a group of training data corresponding to the generated passenger flow relief plan, wherein the group of training data is used for training a model for machine learning; judging whether the stored training data exceeds a preset data amount, and if so, training; when the trained model is used in the future, information of the type and time period of the event and the place and normal passenger flow in the time period of the event are input into the model, and the model outputs a passenger flow alleviation plan.
2. The method of claim 1, wherein generating a passenger flow mitigation plan for the subway station corresponding to the venue over the period of time comprises:
and when the N is more than 1.5 and less than or equal to 2, determining to increase the frequency of vehicles passing through the subway station corresponding to the place.
3. The method of claim 1, wherein generating a passenger flow mitigation plan for the subway station corresponding to the venue over the period of time comprises:
and under the condition that N is larger than 2, determining the number of people entering the subway station corresponding to the place.
4. A method according to any one of claims 1 to 3, wherein the venue-specific data acquisition interface comprises at least one of:
and the data interface of the website corresponding to the place and the data interface of the software or the application corresponding to the place.
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| CN114463054B (en) * | 2022-01-21 | 2023-07-18 | 浪潮卓数大数据产业发展有限公司 | Method and system for predicting park store passenger flow based on one-card communication |
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