CN113112050A - W-BilSTM-based short-time passenger flow prediction method for rail transit - Google Patents

W-BilSTM-based short-time passenger flow prediction method for rail transit Download PDF

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CN113112050A
CN113112050A CN202110263386.5A CN202110263386A CN113112050A CN 113112050 A CN113112050 A CN 113112050A CN 202110263386 A CN202110263386 A CN 202110263386A CN 113112050 A CN113112050 A CN 113112050A
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赵娜
崔婧
曹敏
张叶
聂永杰
刘斯扬
胡健
廖斌
胡昌斌
杨政
尹春林
魏龄
韩彤
肖华根
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Abstract

The application provides a rail transit short-time passenger flow prediction method based on W-BilSTM, wherein time series historical data of urban rail transit passenger flow is obtained as sample data; preprocessing sample data and normalizing; performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data; initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting training data to construct and train a prediction model; when the expected error or the preset iteration times are reached, selecting an optimal BilSTM neural network model to predict the test data to obtain a predicted value; analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes; the method can be used for capturing the change rule of the short-time passenger flow of the rail transit, more accurately predicting the speed of the future urban road, and can be applied to intelligent transportation and smart city construction. The data support is provided for avoiding travel congestion and guaranteeing the safety and efficiency of resident travel.

Description

W-BilSTM-based short-time passenger flow prediction method for rail transit
Technical Field
The application relates to the field of short-time passenger flow prediction of rail transit, in particular to a short-time passenger flow prediction method of rail transit based on W-BilSTM.
Background
The accurate grasp of urban public transport demand is the key point of realizing wisdom city management, and to the higher city of population density, the track traffic among the urban public transport is urban public transport's backbone, has born a large amount of trip demands in rush hour. The change increase of the passenger flow in a short period can cause overlarge bearing pressure of the rail transit, and brings great problems to operation scheduling and management of the rail transit, so that the short-time prediction of the passenger flow of the rail transit has a vital role in guaranteeing the rapid operation of an intelligent transportation system.
The existing methods applied to short-time traffic flow prediction include historical average, time sequence, Kalman filtering, neural network, support vector machine, nonparametric regression, wavelet neural network and the like. However, in the actual railway traffic flow prediction, the subway arrival passenger flow in different periods is fluctuated, and the single model has no memory unit, lacks the consideration of time correlation of time sequence data, and has limited mathematical modeling capability on a complex system.
Disclosure of Invention
The application provides a W-BilSTM-based short-time passenger flow prediction method for rail transit, which aims to solve the technical problem that the passenger flow is difficult to be predicted accurately due to the influence of various environments on the abnormal passenger flow of the rail transit.
In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:
the rail transit short-time passenger flow prediction method based on the W-BilSTM comprises the following steps:
acquiring time series historical data of urban rail transit passenger flow as sample data;
preprocessing the sample data and normalizing;
performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data;
initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting the training data to construct and train a prediction model;
when the expected error or the preset iteration times are reached, selecting an optimal BilSTM neural network model;
predicting the test data through the optimal BilSTM neural network model to obtain a predicted value;
and analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes.
In one possible embodiment, the pre-processing comprises removing erroneous data and redundant data in the sample data.
In one possible implementation, the wavelet neural network selects a mother wavelet as a basis function and a decomposition scale, and performs wavelet decomposition and single-branch reconstruction.
In one possible implementation, the obtained prediction model updates the weights by taking the mean square error as a loss function and network training by using an Adam optimization algorithm.
In one possible embodiment, the predictive model comprises:
the rail transit short-time passenger flow data input layer is the training data and the test data;
the BilSTM hidden layer is combined with the forward LSTM and the backward LSTM to form the BilSTM;
the full connection layer reduces the dimension of the result, selects the ReLU as an activation function, and performs nonlinear mapping on the output data;
and the output layer obtains a final output result.
In one possible embodiment, obtaining the predicted value includes:
predicting the test data through the optimal BilSTM neural network model;
and superposing the predicted subsequences to obtain the predicted value.
In a possible embodiment, the root mean square error RMSE and the mean absolute percentage error MAPE are used as evaluation indicators, including:
Figure BDA0002971026040000021
Figure BDA0002971026040000022
in the formula, yiWhich represents the actual value of the measured value,
Figure BDA0002971026040000023
representing the predicted value and n representing the number of predicted samples.
The application provides a rail transit short-time passenger flow prediction method based on W-BilSTM, which comprises the steps of obtaining time series historical data of urban rail transit passenger flow as sample data; preprocessing the sample data and normalizing; performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data; initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting the training data to construct and train a prediction model; when the expected error or the preset iteration times are reached, selecting an optimal BilSTM neural network model; predicting the test data through the optimal BilSTM neural network model to obtain a predicted value; and analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes. The method and the device can capture the change rule of the short-time passenger flow volume of the rail transit, can accurately predict the speed of the future urban road, and can be applied to intelligent transportation and smart city construction. The data support is provided for avoiding travel congestion and guaranteeing the safety and efficiency of resident travel.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a method for predicting short-term passenger flow of rail transit based on W-BilSTM according to an embodiment of the present application;
FIG. 2 is a diagram illustrating a flow of a method for predicting short-term passenger traffic volume of rail transit based on W-BilSTM according to another embodiment of the present disclosure;
FIG. 3 is a basic structure diagram of a wavelet neural network of a rail transit short-time passenger flow prediction method based on W-BilSTM according to an embodiment of the present application;
FIG. 4 is a model architecture diagram established based on BilSTM for the W-BilSTM-based rail transit short-time passenger flow prediction method in the embodiment of the present application;
FIG. 5 is a schematic structural diagram of the BilSTM of the method for predicting the short-term passenger flow of rail transit based on W-BilSTM in the embodiment of the present application;
fig. 6 is a schematic structural diagram of a typical LSTM structure of a rail transit short-time passenger flow prediction method based on W-BiLSTM in the embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application adopts a W-BILSTM model, and comprises a Wavelet Neural Network (WNN), a Long Short Term Memory artificial Neural Network (LSTM), and a bidirectional cyclic Neural Network (Bi-directional Long Short Term Memory) to predict the Short-time passenger flow of the rail transit. The present application is described in further detail below with reference to the attached drawing figures:
the embodiment of the application provides a rail transit short-time passenger flow prediction method based on W-BilSTM, which is shown in a reference figure 1 and a reference figure 2 and comprises the following steps:
s100, acquiring time series historical data of urban rail transit passenger flow as sample data;
s200, preprocessing the sample data and normalizing; the pre-processing includes removing erroneous data and redundant data in the sample data.
The normalization process is as follows:
Figure BDA0002971026040000041
in the formula, w' is a new feature after normalization, w represents an old feature before fine normalization, and wmax、wminRespectively, the maximum and minimum values of the column in which the selected data is located.
S300, performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data; the wavelet neural network selects a mother wavelet as a basis function and a decomposition scale, performs wavelet decomposition and single-branch reconstruction, and realizes optimized extraction of trend information.
FIG. 3 shows the basic mechanism of a wavelet neural network, X1、X2、…、XnInput parameters of the wavelet neural network; y is1、…、YmIs the output parameter of the wavelet neural network; w is aij、wjkIs the network weight.
Signal sequence xi(i ═ 1,2, …, n) input, the hidden layer output calculation formula is as follows:
Figure BDA0002971026040000042
wherein h (j) is the network output value of the hidden layer; w is aijThe weight from the input layer to the hidden layer; a isj、bjThe shift factor and the scale factor of the wavelet basis function; h isjIs a wavelet basis function.
The function used in this embodiment is a Morlet mother wavelet basis function, and its expression is as follows:
Figure BDA0002971026040000043
the wavelet neural network output layer calculation formula is as follows:
Figure BDA0002971026040000044
in the formula, wikThe weight from the hidden layer to the output layer; h (i) an output layer which is a hidden layer; l is the number of nodes in the hidden layer.
S400, initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting the training data to construct and train a prediction model; the obtained prediction model updates the weight by taking a tanh function and a sigmoid function as excitation functions, taking Mean Square Error (MSE) as a loss function and adopting an Adam optimization algorithm in network training.
And S500, when the expected error or the preset iteration number is reached, selecting the optimal BilSTM neural network model, and stopping training.
S600, predicting the test data through the optimal BilSTM neural network model, and overlapping the predicted subsequences to obtain a predicted value.
As shown in fig. 4, the prediction model includes a rail transit short-time passenger flow data input layer, a BiLSTM hidden layer, a full connection layer, and an output layer.
The rail transit short-time passenger flow data input layer is sample data of time series historical data of urban rail transit passenger flow after preprocessing, and wavelet decomposition and single reconstruction are carried out on the sample data through a wavelet neural network to obtain the training data and the test data;
the BilSTM hidden layer is combined with the forward LSTM and the backward LSTM to form the BilSTM; as shown in fig. 5, in comparison with the standard LSTM in which the state is transmitted unidirectionally from front to back, the BiLSTM considers the data of the preceding time interval and the following time interval to be different from the training prediction, so that the training prediction result is more accurate.
With the network architecture shown in fig. 6, the typical LSTM in the BiLSTM layer solves the RNN long term dependence problem. LSTM has a similar structure to RNN, except that the hidden layer of LSTM contains the input gate itAnd an output gate otForgetting door ftAnd cell state CtAllowing information to easily flow down unchanged, thereby allowing LSTM to remember information for a longer period of time.
ft＝σ(Wfxt+Ufht-1+bf)
it＝σ(Wixt+Uiht-1+bi)
Figure BDA0002971026040000051
Figure BDA0002971026040000052
ot＝σ(Woxt+Uoht-1+bo)
Figure BDA0002971026040000053
Wherein, W is an input weight matrix of the hiding unit; u is an output weight matrix; b is a bias vector; subscripts f, i and o represent a forgetting gate, an input gate and an output gate;
Figure BDA0002971026040000054
representing a point-by-point product operation; σ () is the activation function.
To enhance the nonlinear function of the network, the following 2 activation functions are set:
Figure BDA0002971026040000055
Figure BDA0002971026040000056
the full connection layer reduces the dimension of the result, selects the ReLU as an activation function, and performs nonlinear mapping on the output data;
and the output layer obtains a final output result.
And S700, analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes. The root mean square error RMSE and the mean absolute percentage error MAPE are used as evaluation indexes and comprise:
Figure BDA0002971026040000057
Figure BDA0002971026040000058
in the formula, yiWhich represents the actual value of the measured value,
Figure BDA0002971026040000061
representing the predicted value and n representing the number of predicted samples.
The application provides a rail transit short-time passenger flow prediction method based on W-BilSTM, which comprises the steps of obtaining time series historical data of urban rail transit passenger flow as sample data; preprocessing the sample data and normalizing; performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data; initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting the training data to construct and train a prediction model; when the expected error or the preset iteration times are reached, selecting an optimal BilSTM neural network model; predicting the test data through the optimal BilSTM neural network model to obtain a predicted value; and analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes. The method and the device can capture the change rule of the short-time passenger flow volume of the rail transit, can accurately predict the speed of the future urban road, and can be applied to intelligent transportation and smart city construction. The data support is provided for avoiding travel congestion and guaranteeing the safety and efficiency of resident travel.
The above-mentioned contents are only for explaining the technical idea of the present application, and the protection scope of the present application is not limited thereby, and any modification made on the basis of the technical idea presented in the present application falls within the protection scope of the claims of the present application.
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Claims (7)

1. A rail transit short-time passenger flow prediction method based on W-BilSTM is characterized by comprising the following steps:
acquiring time series historical data of urban rail transit passenger flow as sample data;
preprocessing the sample data and normalizing;
performing wavelet decomposition and single-branch reconstruction on the sample data through a wavelet neural network to obtain training data and test data;
initializing a BilSTM neural network model, setting a mechanism and a hyper-parameter of the BilSTM neural network model, inputting the training data to construct and train a prediction model;
when the expected error or the preset iteration times are reached, selecting an optimal BilSTM neural network model;
predicting the test data through the optimal BilSTM neural network model to obtain a predicted value;
and analyzing the error of the predicted value according to the root mean square error and the average absolute percentage error as evaluation indexes.
2. The W-BilSTM-based rail transit short-time passenger flow prediction method according to claim 1, wherein the preprocessing comprises removing error data and redundant data from the sample data.
3. The method for predicting the short-time passenger flow of the rail transit based on the W-BilSTM as claimed in claim 1, wherein the wavelet neural network selects a mother wavelet as a basis function and a decomposition scale to perform wavelet decomposition and single-branch reconstruction.
4. The W-BilSTM-based rail transit short-time passenger flow prediction method as claimed in claim 1, wherein the obtained prediction model updates the weight by taking mean square error as a loss function and network training by adopting an Adam optimization algorithm.
5. The W-BilSTM-based rail transit short-time passenger flow prediction method according to claim 1, wherein the prediction model comprises:
the rail transit short-time passenger flow data input layer is the training data and the test data;
the BilSTM hidden layer is combined with the forward LSTM and the backward LSTM to form the BilSTM;
the full connection layer reduces the dimension of the result, selects the ReLU as an activation function, and performs nonlinear mapping on the output data;
and the output layer obtains a final output result.
6. The W-BilSTM-based rail transit short-time passenger flow prediction method according to claim 1, wherein obtaining the predicted value comprises:
predicting the test data through the optimal BilSTM neural network model;
and superposing the predicted subsequences to obtain the predicted value.
7. The method for predicting the short-time passenger flow of rail transit based on W-BilSTM according to claim 1, wherein the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) are used as evaluation indexes, and the method comprises the following steps:
Figure FDA0002971026030000021
Figure FDA0002971026030000022
in the formula, yiWhich represents the actual value of the measured value,
Figure FDA0002971026030000023
representing the predicted value and n representing the number of predicted samples.
CN202110263386.5A 2021-03-11 2021-03-11 W-BilSTM-based short-time passenger flow prediction method for rail transit Withdrawn CN113112050A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113570867A (en) * 2021-09-26 2021-10-29 西南交通大学 Urban traffic state prediction method, device, equipment and readable storage medium
CN115389812A (en) * 2022-10-28 2022-11-25 国网信息通信产业集团有限公司 Artificial neural network short-circuit current zero prediction method and prediction terminal

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113570867A (en) * 2021-09-26 2021-10-29 西南交通大学 Urban traffic state prediction method, device, equipment and readable storage medium
CN113570867B (en) * 2021-09-26 2021-12-07 西南交通大学 Urban traffic state prediction method, device, equipment and readable storage medium
CN115389812A (en) * 2022-10-28 2022-11-25 国网信息通信产业集团有限公司 Artificial neural network short-circuit current zero prediction method and prediction terminal

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