CN110852505A - Smart city traffic flow prediction method based on quantum genetic optimization LVQ neural network - Google Patents

Smart city traffic flow prediction method based on quantum genetic optimization LVQ neural network Download PDF

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CN110852505A
CN110852505A CN201911085728.8A CN201911085728A CN110852505A CN 110852505 A CN110852505 A CN 110852505A CN 201911085728 A CN201911085728 A CN 201911085728A CN 110852505 A CN110852505 A CN 110852505A
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张福泉
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Abstract

The invention relates to a smart city traffic flow prediction method based on a quantum genetic optimization LVQ neural network. By applying the quantum genetic algorithm with better global solution, the problems that the LVQ neural network is sensitive to the initial weight and is easy to fall into a local minimum value are solved, and the convergence speed is improved. On 5 general traffic flow data sets, comparative simulation experiments were performed with traffic flow predictions based on GA-BP neural networks and wavelet neural networks. The result shows that the short-term traffic flow prediction based on the QGA-LVQ neural network has better accuracy and real-time performance, and the reliability of the short-term traffic flow prediction is verified by the test results on a plurality of data sets.

Description

Smart city traffic flow prediction method based on quantum genetic optimization LVQ neural network
Technical Field
The invention relates to a smart city traffic flow prediction method based on a quantum genetic optimization LVQ neural network.
Background
Along with the rapid development of economy in China, the travel modes of people are changed greatly, the holding amount of private automobiles in China is greatly increased and promoted year by year, the problem of traffic jam in cities is increasingly prominent, and the people and relevant government departments pay more attention to the traffic jam. Through long-term investigation and analysis of urban traffic conditions, the traffic congestion is explained to be closely related to the flow. The traditional solution mainly adopts means of increasing road infrastructure, strengthening traffic management and the like, but needs a large amount of manpower and capital investment, has an unobvious effect, and cannot fundamentally solve the problem. The rise and rapid development of artificial intelligence scientific technology provides a new way for smart cities to solve the increasingly serious traffic congestion problem in the future.
An intelligent traffic system based on an artificial neural network becomes a key point and a difficult point of the current research in the field of smart cities. The artificial intelligence management technology can reasonably regulate and control the traffic flow of the whole city, optimize the utilization rate of a road network, effectively relieve traffic jam, reduce energy consumption and reduce pollution. Fig. 1 shows dynamic prediction and selection of a route by using an artificial neural network technology, and table 1 dynamically shows congestion conditions among nodes of a travel route network based on fig. 1, and the congestion conditions of current road sections are dynamically analyzed according to time periods by using the maps and tables, so that sufficient preparation is made for people to predict and select a proper travel route, the travel efficiency of people is improved, and precious time of people is saved; in addition, as can be seen from the figures and tables, the artificial intelligence technology supports the whole road network integrally to perform reasonable autonomous flow regulation and control, and the use efficiency of the road network is improved.
Table 1 congestion situation table based on fig. 1 between nodes of travel network
Figure BDA0002265269470000011
However, traffic flow data has special attributes such as strong periodicity, nonlinearity and uncertainty, so that real-time and accurate traffic flow prediction is difficult. In recent years, various machine learning and artificial neural network models are applied to this field, such as a BP neural network, a wavelet neural network, and the like, but accuracy and real-time performance still cannot meet practical requirements. As an extension of the SOM self-organizing network, the LVQ neural network has the advantages of simple structure, good clustering effect, simple calculation, strong nonlinear fitting capability and great application potential in traffic flow prediction. Therefore, the invention provides a quantum genetic optimization LVQ neural network-based urban traffic network flow prediction method. The quantum genetic algorithm has a better global solution, so that the method can be used for effectively overcoming the defects that the LVQ neural network is sensitive to an initial weight and is easy to fall into a local minimum value, and the convergence speed is improved.
Disclosure of Invention
The invention aims to provide a smart urban traffic flow prediction method based on a quantum genetic optimization LVQ neural network, which effectively improves the real-time performance and precision of urban traffic flow prediction.
In order to achieve the purpose, the technical scheme of the invention is as follows: a smart city traffic flow prediction method based on a quantum genetic optimization LVQ neural network comprises the following steps:
s1, carrying out wavelet noise reduction on the traffic flow data acquired by the ground detector, and removing interference noise signals;
step S2, dividing the denoised data into a training sample part and a test sample part;
step S3, normalizing the two divided samples;
step S4, initializing LVQ neural network algorithm parameters and QGA algorithm parameters;
s5, inputting a training sample, and optimizing the weight of the LVQ neural network algorithm parameter through a QGA algorithm;
and step S6, inputting the test sample to the optimized LVQ neural network for traffic flow short-term prediction.
In an embodiment of the present invention, the LVQ neural network includes an input layer, a competition layer and an output layer, wherein the competition layer is responsible for completing classification of input vectors in neurons of the input layer.
In an embodiment of the present invention, the LVQ neural network algorithm is a LVQ1 algorithm, and the implementation steps thereof are as follows:
step 1: initializing weight W between input layer and competition layeri,jAnd a learning rate η (η > 0);
step 2: converting the input vector X to (X)1,x2,...,xR)TImporting an input layer, R being the number of input elements, and calculating the distance d between neurons of the competition layer and an input vector:
Figure BDA0002265269470000021
wherein S islThe number of competitive neurons;
step 3: selecting the neuron with the shortest distance to the input vector if djAt a minimum, the class label that labels the output layer neuron to which it is connected is Cj；
Step 4: let the class label corresponding to the input vector be CxIf C is presentj＝CxThen, the weight is adjusted as follows:
Wij-new＝Wij-old+η(x-Wij-old)
otherwise, the weight is adjusted as follows:
Wij-new＝Wij-old-η(x-Wij-old)
step 5: jump to Step2 and repeat the execution until the set iteration number or the error precision requirement is met.
In an embodiment of the present invention, the step S5 is specifically implemented as follows:
step 1: let genetic algebra t equal to 0, initialize population Q (t)0) And each individual in the initial population is observed once to obtain a state p (t);
step 2: the fitness function of the LVQ neural network is defined according to the distance principle, the average distance from a random individual in a population to a sample point of an input layer is specifically adopted, and the calculation mode is as follows:
Figure BDA0002265269470000031
wherein, FtRepresenting a set of elements belonging to class k, NkNumber of elements, x, representing class kjA training sample input vector representing an LVQ neural network;
the fitness of the individual is defined as:
step 3: the termination condition of the iterative computation is:
Figure BDA0002265269470000033
wherein N is the number of sample input vectors;
if D < epsilon, then end;
step 4: and (3) performing observation once on each individual in the population Q (t), then calculating the fitness of each state, updating the population Q (t) by using a quantum rotating gate, recording the best individual and the fitness thereof, and returning to Step 2.
Compared with the prior art, the invention has the following beneficial effects: the method has better accuracy and real-time performance in short-time traffic flow prediction based on the QGA-LVQ neural network, and the reliability of the short-time traffic flow prediction is verified by test results on a plurality of data sets.
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FIG. 1 is a diagram of dynamic path selection based on an artificial neural network.
Fig. 2 is a coordinate diagram of a quantum revolving door.
Fig. 3 is a view of the structure of the LVQ neural network.
FIG. 4 is a short-term traffic flow prediction based on a QGA-LVQ neural network.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
The invention provides a smart city traffic flow prediction method based on a quantum genetic optimization LVQ neural network, which comprises the following steps:
s1, carrying out wavelet noise reduction on the traffic flow data acquired by the ground detector, and removing interference noise signals;
step S2, dividing the denoised data into a training sample part and a test sample part;
step S3, normalizing the two divided samples;
step S4, initializing LVQ neural network algorithm parameters and QGA algorithm parameters;
s5, inputting a training sample, and optimizing the weight of the LVQ neural network algorithm parameter through a QGA algorithm;
and step S6, inputting the test sample to the optimized LVQ neural network for traffic flow short-term prediction.
In this embodiment, the LVQ neural network includes an input layer, a competition layer and an output layer, wherein the competition layer is responsible for completing classification of input vectors in neurons of the input layer.
In this embodiment, the LVQ neural network algorithm is a LVQ1 algorithm, and the implementation steps thereof are as follows:
step 1: initializing weight W between input layer and competition layeri,jAnd a learning rate η (η > 0);
step 2: converting the input vector X to (X)1,x2,...,xR)TImporting an input layer, R being the number of input elements, and calculating the distance d between neurons of the competition layer and an input vector:
Figure BDA0002265269470000041
wherein S islThe number of competitive neurons;
step 3: selecting the neuron with the shortest distance to the input vector if djAt a minimum, the class label that labels the output layer neuron to which it is connected is Cj；
Step 4: let the class label corresponding to the input vector be CxIf C is presentj＝CxThen, the weight is adjusted as follows:
Wij-new＝Wij-old+η(x-Wij-old)
otherwise, the weight is adjusted as follows:
Wij-new＝Wij-old-η(x-Wij-old)
step 5: jump to Step2 and repeat the execution until the set iteration number or the error precision requirement is met.
In this embodiment, the step S5 is specifically implemented as follows:
step 1: let genetic algebra t equal to 0, initialize population Q (t)0) And each individual in the initial population is observed once to obtain a state p (t);
step 2: the fitness function of the LVQ neural network is defined according to the distance principle, the average distance from a random individual in a population to a sample point of an input layer is specifically adopted, and the calculation mode is as follows:
Figure BDA0002265269470000051
wherein, FtRepresenting a set of elements belonging to class k, NkNumber of elements, x, representing class kjA training sample input vector representing an LVQ neural network;
the fitness of the individual is defined as:
Figure BDA0002265269470000052
step 3: the termination condition of the iterative computation is:
wherein N is the number of sample input vectors;
if | D | < ε, then end;
step 4: and (3) performing observation once on each individual in the population Q (t), then calculating the fitness of each state, updating the population Q (t) by using a quantum rotating gate, recording the best individual and the fitness thereof, and returning to Step 2.
The following explains a specific implementation process of the present invention.
1. Quantum genetic algorithm (Quantum genetic algorithm, QGA)
Compared with the traditional genetic algorithm, the quantum genetic algorithm has the advantages of high convergence rate and strong global optimization capability. Some known quantum algorithms are faster than conventional general purpose computers in processing problems, and thus complete a very complicated solution in a reasonable time.
A) Quantum bit encoding
The definition of |0 > and |1 > in the two-dimensional complex vector space represents two different qubit states, and in addition to the two states, the state of a qubit can also be a superposition between the two states. As a minimum unit of information, the state of one qubit can be represented as:
Figure BDA0002265269470000054
wherein α and β both represent a complex number, called the probability magnitude of the correlation, and satisfy the condition | α2+|β|2＝1。
In the quantum genetic algorithm, chromosomes are encoded by using quantum bits and quantum superposition states, and the encoding mode of each quantum chromosome is as follows:
Figure BDA0002265269470000061
wherein t represents the population generation number, the quantum population of the t generation is represented as
Figure BDA0002265269470000066
m represents the number of quanta and n represents the population size. Further, the following normalization condition needs to be satisfied:
Figure BDA0002265269470000067
B) quantum revolving door
As the most basic operation step, qubits use quantum gates to perform state transitions by matrix transformation in order to complete population evolution. Qubit operation typically employs a quantum rotating gate, which is defined as follows:
the population evolution process is as follows:
Figure BDA0002265269470000063
wherein, thetaiIndicating the rotation angle, theta needs to be specified according to the adjustment ruleiAngle and direction of (d). The coordinate diagram of the quantum rotary gate is shown in fig. 2.
C) Quantum crossing and mutation
The quantum crossing operation is a full interference crossing operation based on the coherent properties of the quantum. For quantum crossing operations, each quantum chromosome in the population needs to be crossed. If the population number is 6 and the chromosome length is 7, a cross-over procedure for diagonal rearrangement and combination is given in Table 2.
TABLE 2 Total interference Cross
Figure BDA0002265269470000064
In quantum mutation operation, quantum genetic algorithm uses quantum mutation operator U (omega (delta theta)i) To implement update optimization.
Figure BDA0002265269470000065
ω(Δθi)＝f(αi,βi)*Δθi(7)
Wherein, f (α)i,βi) Indicating the direction of rotation, Δ θiThe rotation is shown, and delta represents an adjustment factor, which is generally small.
2. Short-term traffic flow prediction basic algorithm based on QGA-LVQ neural network
A) Structure of LVQ neural network
In consideration of the real-time performance of short-time traffic flow prediction, an LVQ neural network which is an extension of an SOM self-organizing network is selected to realize the prediction. The LVQ neural network can supervise and learn the clustering center so as to classify the input data, and can approach most of short-time traffic systems. The short-time traffic flow prediction is performed by using a three-layer LVQ neural network, which is divided into an input layer, a competition layer and an output layer as shown in FIG. 3. Wherein, the competition layer is mainly responsible for completing the classification of the input vectors in the neurons of the input layer.
The LVQ network algorithm is divided into 2 types: LVQ1 and LVQ 2. LVQ1 is more common, so the LVQ1 algorithm is used herein, with the following steps:
step 1: initializing weight W between input layer and competition layeri,jAnd a learning rate η (η > 0);
step 2: converting the input vector X to (X)1,x2,...,xR)TImporting an input layer, R being the number of input elements, and calculating the distance d between neurons of the competition layer and an input vector:
Figure BDA0002265269470000071
wherein S islThe number of competitive neurons;
step 3: selecting the neuron with the shortest distance to the input vector if djAt a minimum, the class label that labels the output layer neuron to which it is connected is Cj；
Step 4: let the class label corresponding to the input vector be CxIf C is presentj＝CxThen, the weight is adjusted as follows:
Wij-new＝Wij-old+η(x-Wij-old) (9)
otherwise, the weight is adjusted as follows:
Wij-new＝Wij-old-η(x-Wij-old) (10)
step 5: jump to Step2 and repeat the execution until the set iteration number or the error precision requirement is met.
B) Predictive algorithm flow
The invention mainly predicts the short-time traffic flow of a single time point, and the research idea is to select an initial value for the LVQ neural network through a quantum genetic algorithm, and finally the population gradually converges to an optimal solution so as to achieve the aim of further improving the classification accuracy. The QGA-LVQ neural network prediction steps are as follows:
step 1: let genetic algebra t equal to 0, initialize population Q (t)0) And each individual in the initial population is observed once to obtain a state p (t);
step 2: the fitness function of the LVQ neural network is defined according to the distance principle, the average distance from a random individual in a population to a sample point of an input layer is specifically adopted, and the calculation mode is as follows:
Figure BDA0002265269470000081
wherein, FtRepresenting a set of elements belonging to class k, NkNumber of elements, x, representing class kjA training sample input vector representing an LVQ neural network;
the fitness of the individual is defined as:
step 3: the termination condition of the iterative computation is:
Figure BDA0002265269470000083
wherein N is the number of sample input vectors;
if | D | < ε, then end;
step 4: and (3) performing observation once on each individual in the population Q (t), then calculating the fitness of each state, updating the population Q (t) by using a quantum rotating gate, recording the best individual and the fitness thereof, and returning to Step 2.
4. Results and analysis of the experiments
4.1 configuration of Experimental data sets and environmental parameters
To verify the feasibility of the proposed method, simulation experiments were performed using 5 urban network traffic data sets, wherein data sets dataset1, dataset2 and dataset3 were all from the data of the deluge traffic data research laboratory, university of minnesota, usa, 1440 groups of data and 864 groups of data, respectively. Two additional sets of dataset4 and dataset5 were from the PeMS system, with 864 sets of data. The time interval for all data was 5 minutes, and all traffic flow data was normalized.
The simulation experiment environment is configured by MATLAB 2012a simulation software, a Window7 operating system, an Intel core i58400CPU and an 8GB memory, experimental parameters are configured as follows, the population scale is 50, the maximum iteration number is 200, the chromosome length is 10, the epsilon is 0.01, α is 0.5, β is 0.5, the number of neurons of an input layer, a competition layer and an output layer in the LVQ neural network is 180, 180 multiplied by 5 and 5 respectively, and the learning rate η is 0.1.
4.2 Experimental evaluation index
To verify the convergence and optimization capabilities of the algorithms presented herein, 4 evaluation indices were used for performance analysis [7-9], including Mean Absolute Percentage Error (MAPE), Equalization Coefficient (EC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The specific calculation formula of each index is as follows:
the mean absolute percentage error is:
Figure BDA0002265269470000091
the equalization coefficients are:
Figure BDA0002265269470000092
the mean absolute error is:
Figure BDA0002265269470000093
the root mean square error is:
Figure BDA0002265269470000094
wherein N represents the number of test samples, Yp(t) represents the predicted output value of the QGA-LVQ neural network at time t, YrAnd (t) is expressed as an actual traffic flow numerical value.
4.3, analysis of results
FIG. 4 is a short-term traffic flow prediction from QGA-LVQ neural networks on dataset1 data sets. Table 3 shows the results of 10 runs of the short-term traffic flow prediction based on the QGA-LVQ neural network. As can be seen from fig. 4 and table 3, the predicted value of the proposed QGA-LVQ neural network is almost consistent with the actual traffic flow, verifying its feasibility.
TABLE 3 short term traffic flow prediction performance results based on QGA-LVQ neural network
Figure BDA0002265269470000095
Figure BDA0002265269470000101
The QGA-LVQ neural network, GA-BP neural network of the present invention were mapped on dataset1 dataset[9]Sum wavelet neural network[8]Comparative experimental analysis was performed as shown in table 4. Compared with other two neural network prediction models, the QGA-LVQ has certain advantages on 5 indexes, and particularly MAPE is reduced by about 40%. Although the QGA-LVQ neural network has longer running time than the wavelet neural network, because the convergence speed of the cells and the whole structure of the wavelet neural network is faster as determined by the wavelet analysis theory, the QGA-LVQ neural network is still shortened by about 7 seconds and has stronger real-time property than the GA-BP.
TABLE 4 comparative analysis of the predicted performance results of three neural networks
TABLE 5 prediction of QGA-LVQ neural networks on 5 datasets
Table 5 shows the predicted results of the QGA-LVQ neural network on 5 data sets, each of which was averaged 10 times. Through the analysis of the experimental results, the QGA-LVQ neural network provided by the method is stable on each data set, good in reliability and good in convergence rate and prediction accuracy.
The invention provides a QGA-LVQ neural network-based urban traffic network flow prediction method. By applying the quantum genetic algorithm with better global solution, the problems that the LVQ neural network is sensitive to the initial weight and is easy to fall into a local minimum value are solved, and the convergence speed is improved. On 5 general traffic flow data sets, comparative simulation experiments were performed with traffic flow predictions based on GA-BP neural networks and wavelet neural networks. The result shows that the short-term traffic flow prediction based on the QGA-LVQ neural network has better accuracy and real-time performance, and the reliability of the short-term traffic flow prediction is verified by the test results on a plurality of data sets.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (4)

1. A smart city traffic flow prediction method based on a quantum genetic optimization LVQ neural network is characterized by comprising the following steps:
s1, carrying out wavelet noise reduction on the traffic flow data acquired by the ground detector, and removing interference noise signals;
step S2, dividing the denoised data into a training sample part and a test sample part;
step S3, normalizing the two divided samples;
step S4, initializing LVQ neural network algorithm parameters and QGA algorithm parameters;
s5, inputting a training sample, and optimizing the weight of the LVQ neural network algorithm parameter through a QGA algorithm;
and step S6, inputting the test sample to the optimized LVQ neural network for traffic flow short-term prediction.
2. The quantum genetic optimization-based LVQ neural network intelligent urban traffic flow prediction method according to claim 1, wherein the LVQ neural network comprises an input layer, a competition layer and an output layer, wherein the competition layer is responsible for completing classification of input vectors in neurons of the input layer.
3. The intelligent city traffic flow prediction method based on the quantum genetic optimization LVQ neural network according to claim 1, wherein the LVQ neural network algorithm is an LVQ1 algorithm, and the implementation steps are as follows:
step 1: initializing weight W between input layer and competition layeri,jAnd a learning rate η (η > 0);
step 2: converting the input vector X to (X)1,x2,...,xR)TImporting an input layer, R being the number of input elements, and calculating the distance d between neurons of the competition layer and an input vector:
Figure FDA0002265269460000011
wherein S islThe number of competitive neurons;
step 3: selecting the neuron with the shortest distance to the input vector if djAt a minimum, the class label that labels the output layer neuron to which it is connected is Cj；
Step 4: let the class label corresponding to the input vector be CxIf C is presentj＝CxThen according to the followingThe way adjusts the weight:
Wij-new＝Wij-old+η(x-Wij-old)
otherwise, the weight is adjusted as follows:
Wij-new＝Wij-old-η(x-Wij-old)
step 5: jump to Step2 and repeat the execution until the set iteration number or the error precision requirement is met.
4. The smart city traffic flow prediction method based on the quantum genetic optimization LVQ neural network according to claim 1, wherein the step S5 is implemented as follows:
step 1: let genetic algebra t equal to 0, initialize population Q (t)0) And each individual in the initial population is observed once to obtain a state p (t);
step 2: the fitness function of the LVQ neural network is defined according to the distance principle, the average distance from a random individual in a population to a sample point of an input layer is specifically adopted, and the calculation mode is as follows:
Figure FDA0002265269460000021
wherein, FtRepresenting a set of elements belonging to class k, NkNumber of elements, x, representing class kjA training sample input vector representing an LVQ neural network;
the fitness of the individual is defined as:
Figure FDA0002265269460000022
step 3: the termination condition of the iterative computation is:
Figure FDA0002265269460000023
wherein N is the number of sample input vectors;
if | D | < ε, then end;
step 4: and (3) performing observation once on each individual in the population Q (t), then calculating the fitness of each state, updating the population Q (t) by using a quantum rotating gate, recording the best individual and the fitness thereof, and returning to Step 2.
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