CN111882114B - Short-time traffic flow prediction model construction method and prediction method - Google Patents
Short-time traffic flow prediction model construction method and prediction method Download PDFInfo
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Abstract
The application discloses a short-time traffic flow prediction model construction method and a short-time traffic flow prediction method. The influence on the prediction result of the short-time traffic flow is obvious aiming at the training set selection of the neural network, the K-Means clustering algorithm is adopted to cluster the historical short-time traffic flow data, the prediction is carried out in a targeted manner, and the accuracy of the prediction result is improved.
Description
Technical Field
The application belongs to the field of intelligent traffic systems, and particularly relates to a short-time traffic flow prediction model construction method and a prediction method.
Background
The intelligent traffic system applies modern technical achievements to traffic planning and management, an intelligent traffic management system is established, the intelligent traffic system is accurate, efficient and real-time, through feedback of the intelligent traffic system, related departments can timely master traffic conditions of concerned areas, traffic operation is effectively guided, occurrence rate of traffic jams is reduced or even avoided, and information support is provided for urban road planning, energy conservation and emission reduction.
With the rise of artificial intelligence research hot flashes, neural network models have achieved remarkable results in short-term traffic flow predictions. However, because factors such as different road conditions, different weather, different dates and the like have great influence on the time series distribution of the short-time traffic flow, the existing neural network model cannot accurately predict the traffic flow.
Disclosure of Invention
Aiming at the defects existing in the prior art, the application aims to provide a short-time traffic flow prediction method based on K-Means clustering and GRU network, which solves the technical problem that the prior art cannot accurately predict traffic flow.
In order to solve the technical problems, the application adopts the following technical scheme:
a short-time traffic flow prediction model construction method comprises the following steps:
step 1, acquiring traffic flow data in a period of time to obtain a traffic flow data set; preprocessing the obtained traffic flow data set to obtain a preprocessed traffic flow data set;
step 2, clustering the data in the preprocessed traffic flow data set obtained in the step 1 by a K-Means algorithm to obtain short-time traffic flow mode types, and establishing a short-time traffic flow mode library;
step 3, determining a prediction date, acquiring short-time traffic flow data of the first N moments of the prediction date, sequentially determining feature vectors of the N moment short-time traffic flow modes, respectively determining short-time traffic flow modes corresponding to the N moment feature vectors by using a classification method, and selecting traffic flow data corresponding to a traffic flow mode with highest occurrence frequency of the short-time traffic flow modes corresponding to the N moments to form a training data set;
step 4, training the training data set formed in the step 3 by using the GRU neural network model, and obtaining a short-time traffic flow prediction model after training is completed;
the GRU neural network model adopts a stack structure and comprises an input layer, a first GRU unit layer, a second GRU unit layer and a third GRU unit layer which are sequentially connected, and a dropout layer for preventing overfitting is connected behind the third GRU unit layer.
Specifically, the preprocessing in step 1 includes data deletion, data interpolation, data denoising and normalization.
Specifically, clustering the short-term traffic flow data obtained in the step 1 by using a K-Means algorithm to obtain short-term traffic flow categories, and forming a short-term traffic flow category mode library specifically includes: k points are selected as initial clustering centers for clustering, wherein the value range of K is 2-7.
Specifically, the parameter setting of the GRU network in step 3 includes: prediction step size 8, hidden layer neuron number 12, learning rate 0.02 and iteration number 800.
A short-term traffic flow prediction method, the method comprising the steps of:
step 1, obtaining a traffic flow data set to be predicted and preprocessing the traffic flow data set to obtain a preprocessed traffic flow data set to be predicted;
step 2, inputting the preprocessed traffic flow data set to be predicted into the short-time traffic flow prediction model obtained by the short-time traffic flow prediction model construction method according to any one of claims 1-5, and obtaining the class of the short-time traffic flow to be predicted.
Compared with the prior art, the application has the beneficial technical effects that:
according to the application, the K-Means clustering method is combined with the GRU neural network, the influence on the prediction result of the short-time traffic flow is obvious aiming at the training set selection of the neural network, the K-Means clustering algorithm is adopted to cluster the historical short-time traffic flow data, the prediction is carried out in a targeted manner, and the accuracy of the prediction result is improved.
Drawings
FIG. 1 is a flow chart of a process of the present application;
FIG. 2 is a graph of K-means clustering results obtained in example 1 of the present application;
FIG. 3GRU prediction model flow diagram.
Fig. 4 is a graph of the short-term traffic flow prediction result obtained in embodiment 1 of the present application, wherein the solid line represents the actual short-term traffic flow and the broken line represents the predicted value;
FIG. 5 is a graph of the prediction error versus KMeas-GRU model, conventional GRU network model, ARIMA model and SAEs model.
The application is described in detail below with reference to the drawings and the detailed description.
Detailed Description
The whole technical conception and the technical principle of the application are as follows: clustering traffic flow data in a period of time, fully extracting data information, inputting the data information into the GRU neural network, training the GRU neural network model by taking the short-time traffic flow mode type as output, and obtaining a short-time traffic flow prediction model after training is completed so as to realize accurate prediction of short-time traffic flow.
The definition or concept of the present application is described below:
euclidean metric (also known as euclidean distance) is a commonly used distance definition that refers to the true distance between two points in m-dimensional space, or the natural length of a vector (i.e., the distance of the point from the origin). The euclidean distance in two and three dimensions is the actual distance between two points.
Two norms: the 2-norm of the matrix A is the square root of the maximum eigenvalue of the product of the transposed conjugate matrix of A and the matrix A, and the linear distance of two vector matrices in space.
In order that the objects and advantages of the application will become more apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings and which are appended to illustrate by way of a comparative example. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The principle of the K-means clustering algorithm is as follows: and k is taken as a parameter, n objects are divided into k clusters, so that the clusters have higher similarity, and the similarity among the clusters is lower. The processing procedure of the K-means clustering algorithm is as follows: first, randomly selecting k objects as centroids, each object initially representing the average or center of a cluster; for each object that remains, it is assigned to the nearest cluster based on its distance from the center of the cluster, and the algorithm iterates between the data allocation step and the centroid update step until the stopping criteria are met (i.e., no data point changes, or the sum of cluster distances is minimized, or some maximum number of iterations is reached).
KNN classification: the core idea of the KNN model is very simple, euclidean distance is calculated between each test set sample point and each sample in the training set, K points with the nearest Euclidean distance are taken (K is the number of neighbor candidates which can be defined artificially, and the determination of K can influence algorithm results), the category frequency of the K training set sample points is counted, and the category with the highest frequency is regarded as the prediction category of the test sample point.
The application also adopts a plurality of model evaluation index comparisons to evaluate the quality of the predicted result, including square percent error (MAPE), square absolute error (MAE) and Root Mean Square Error (RMSE).
The smaller the error, the better the pseudo-predictive effect for several errors, square percent error, square absolute error and root mean square error.
Examples:
this embodiment
Step 1: and acquiring the traffic flow data in a period of time and in a short time, and then carrying out data filling, deleting or data denoising and normalization related preprocessing to obtain a preprocessed traffic flow data set.
As a specific embodiment of the present application, the acquisition of traffic flow data may be directly downloaded to an official database or independently detected by itself. The historical traffic flow data in this embodiment is downloaded from the us PeMS database, and includes traffic flow data obtained by counting 5 minutes from 1 st 2018 to 31 st 2018, for a total of 8928 pieces of data.
Step 11: for the input short-time traffic flow historical data, firstly judging whether the data is null, if so, executing deleting operation, and if the null ratio of the traffic flow data in one day is more than 20%, deleting the whole filled traffic flow data;
step 12: if not, it is determined whether the data is abnormal according to the expression (1).
Wherein q represents short-time traffic flow, C represents maximum road traffic capacity, T represents flow data acquisition time interval, f c Representing the correction factor. If the data is abnormal data, deleting and then interpolating by using the average value of the previous value and the next value.
Step 13: the min-max normalization method processes the short-time traffic flow data as shown in the formula (2):
wherein X is i Represents the i-th sample value, X i ' represents the normalized value of the ith sample, X max Represents the maximum value in the sample, X min Representing the minimum in the sample.
Step 2: clustering the data in the preprocessed traffic flow data set obtained in the step 1 by using a K-Means algorithm to obtain short-time traffic flow mode types, and establishing a short-time traffic flow mode library.
Step 21: k points are firstly selected as initial clustering centers (the value range of K is generally [2-7 ]). In this example k=3 is chosen.
Step 22: and distributing the data in the preprocessed traffic flow data set.
Each centroid defines a cluster. In this step, use is made of the 2-range-basedThe Euclidean distance of the numbers calculates the distance between the data points, assigning each data point to the centroid nearest to it, as in equation (3). If c i Is a centroid set in set C, then points x in the dataset will all be assigned to a centroid-based C i Is a cluster of classes.
Where dist () is the euclidean distance at 2 norms.
Step 23: centroid update
In this step, the mass center is updated by calculating the mean of all data points as in equation (4):
the iteration of steps 1 and 2 is continued until no data points change the cluster, the sum of the distances from each data point in the cluster to the mass is minimized, or the maximum number of iterations is reached, as shown in fig. 2.
Step 3: and (2) selecting short-time traffic flow of 24 time points (the first 2 hours) of the date to be predicted as a state vector, and taking Euclidean distance between the short-time traffic flow of the 24 time points and the short-time traffic flow in the short-time traffic flow mode library obtained in the step (2) as an index for measuring data similarity.
Step 31: and calculating Euclidean distance between all data points obtained in a period from 0 point on 1 month and 1 point in 2018 to 24 points on 1 month and 30 points in 2018 and data obtained 24 times before 0 point on 31 month and 1 point in 2018.
Step 32: the Euclidean distance of 24 moments before the 0 point of 31 days of 2018, 1 month is calculated from the small to large arrangement.
Step 33: n sample points with the smallest distance to the unknown sample are determined and selected (the selection range is [3-7] in the embodiment).
Step 34: the occurrence frequencies of the categories to which the selected N points belong are counted, and n=5 is selected in this embodiment.
Step 35: the category with the highest occurrence frequency is taken as the category of the current sample point.
Step 4: data in a category most similar to the traffic flow pattern of 2018, 1 and 31 is selected as training data for the GRU network, and then prediction is performed by using the designed GRU network.
The GRU network comprises a 1-layer input layer, a 3-layer GRU unit layer, a 1-layer dropout layer and a 1-layer output layer, and the structure is shown in figure 3. The GRU prediction model structure adopts a stack structure, and the multi-layer architecture can be used for deeper expression of data. As shown in fig. 4.5, the 3 parameters none, 12, 1 of input in gru _1_input: input layer represent the number of samples, time steps and variable dimensions of the input data of the input layer of the network, and the parameters of the output of the upper layer must be the same as those of the input of the next layer. The Dropout layer (parameter value range is 0-0.2) connected behind the 3-layer GRU network is used for reducing the overfitting of the GRU network, and the final Dense layer converts the multidimensional data output by the GRU into one-dimensional output.
The super parameter settings for the GRU network are shown in table 1.
Table 1 GRU network superparameter settings
Fig. 4 is a diagram of a predicted result using the method, in which a blue solid line represents an actual short-time traffic flow and an orange solid line represents a predicted value.
FIG. 5 is a graph comparing errors of KMeas-GRU model, conventional GRU network model, ARIMA model and SAEs model.
Error pairs of kmens-GRU prediction model with traditional GRU network model, ARIMA model and SAEs model are shown in table 2:
table 2 comparative evaluation index table of model
Claims (3)
1. The short-time traffic flow prediction model construction method is characterized by comprising the following steps of:
step 1, acquiring traffic flow data in a period of time to obtain a traffic flow data set; preprocessing the obtained traffic flow data set to obtain a preprocessed traffic flow data set;
step 1.1: for the input short-time traffic flow historical data, firstly judging whether the data is null, if so, executing deleting operation, and if the null ratio of the traffic flow data in one day is more than 20%, deleting the whole filled traffic flow data;
step 1.2: if the value is not null, judging whether the value is abnormal data according to the formula (1);
wherein q represents short-time traffic flow, C represents maximum road traffic capacity, T represents flow data acquisition time interval, f c Representing the correction coefficient;
if the data is abnormal data, deleting the data firstly and then interpolating by using the average value of the previous value and the next value;
step 1.3: the min-max normalization method processes the short-time traffic flow data as shown in the formula (2):
wherein X is i Represents the i-th sample value, X i ' represents the normalized value of the ith sample, X max Represents the maximum value in the sample, X min Representing the minimum value in the sample;
step 2, clustering the data in the preprocessed traffic flow data set obtained in the step 1 by a K-Means algorithm to obtain short-time traffic flow mode types, and establishing a short-time traffic flow mode library;
step 2.1: firstly, K points are selected as initial clustering centers, and the value range of K is 2-7;
step 2.2: distributing data in the preprocessed traffic flow data set;
each centroid defines a cluster; in this step, the distance between data points is calculated using euclidean distance based on 2 norms, and each data point is assigned to the centroid nearest to it, as shown in the following formula (3); if c i Is a centroid set in set C, then points x in the dataset will all be assigned to a centroid-based C i Is in a cluster of classes (1);
wherein dist () is a euclidean distance at 2 norms;
step 2.3: centroid update
In this step, the centroid is updated by calculating the mean of all data points, as shown in equation (4):
iterating the step 1 and the step 2 continuously until no data point changes the class cluster, and the sum of the distances from each data point in the cluster to the quality is minimum or the maximum iteration times are reached;
step 3, determining a prediction date, acquiring short-time traffic flow data of the first N moments of the prediction date, sequentially determining feature vectors of the N moment short-time traffic flow modes, respectively determining short-time traffic flow modes corresponding to the N moment feature vectors by using a classification method, and selecting traffic flow data corresponding to a traffic flow mode with highest occurrence frequency of the short-time traffic flow modes corresponding to the N moments to form a training data set;
step 4, training the training data set formed in the step 3 by using the GRU neural network model, and obtaining a short-time traffic flow prediction model after training is completed;
the GRU neural network model adopts a stack structure and comprises an input layer, a first GRU unit layer, a second GRU unit layer and a third GRU unit layer which are sequentially connected, and a dropout layer for preventing overfitting is connected behind the third GRU unit layer;
the parameter setting of the GRU network model comprises the following steps: prediction step size 8, hidden layer neuron number 12, learning rate 0.02 and iteration number 800.
2. The short-term traffic flow prediction model construction method according to claim 1, wherein the preprocessing in step 1 includes data deletion, data interpolation, data denoising and normalization.
3. A short-term traffic flow prediction method, characterized in that the method comprises the steps of:
step 1, obtaining a traffic flow data set to be predicted and preprocessing the traffic flow data set to obtain a preprocessed traffic flow data set to be predicted;
and 2, inputting the preprocessed traffic flow data set to be predicted into the short-time traffic flow prediction model obtained by the short-time traffic flow prediction model construction method according to claim 1 or 2, and obtaining the class of the short-time traffic flow to be predicted.
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