CN111723997A - Automatic generation method of urban major traffic accident data sample based on GAN - Google Patents

Automatic generation method of urban major traffic accident data sample based on GAN Download PDF

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CN111723997A
CN111723997A CN202010595276.4A CN202010595276A CN111723997A CN 111723997 A CN111723997 A CN 111723997A CN 202010595276 A CN202010595276 A CN 202010595276A CN 111723997 A CN111723997 A CN 111723997A
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吴超腾
周昱
曾江南
李春玲
沈丹凤
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Abstract

The invention relates to a method for automatically generating urban major traffic accident data samples based on GAN, which aims to automatically generate the urban major traffic accident data samples, thereby providing a large amount of data for learning and training a deep learning model, improving the accuracy of the model for predicting traffic accidents and further recovering a large amount of loss caused by the traffic accidents. The genetic variation-antagonistic generation network in the present invention is mainly composed of two parts, one is a generation network and the other is an identification network. The invention uses the genetic variation algorithm for generating the false data sample in combination with the generation network for the first time, and simultaneously, the anti-generation network is also used for generating the serious traffic accident sample for the first time.

Description

Automatic generation method of urban major traffic accident data sample based on GAN
Technical Field
The invention relates to a method for generating urban major traffic accident data samples based on a genetic variation-confrontation generation network, and belongs to the technical field of intelligent traffic application.
Background
Major traffic accidents, particularly car accidents, are a huge problem all over the world, with about 130 million people dying from car traffic accidents and about 5 million people injured during car traffic accidents each year all over the world. There are large data based machine learning techniques to predict traffic accidents ahead of time to minimize losses. As is well known, the deep learning technology needs a large number of traffic accident samples, and the more the traffic accident data, the higher the accuracy of the model. However, the number of the major traffic accidents occurring and recorded is not large, certain privacy is involved, and meanwhile, compared with a negative sample (i.e. no major traffic accident occurs), the number of the positive samples (the occurring major traffic accident) is too small, the positive samples belong to unbalanced samples, so that the training precision of the model is greatly influenced, how to increase the number of the major traffic accident samples and provide the samples for the model to learn is how to increase, and therefore, the prediction accuracy of the major traffic accident is improved, which is a very important problem. Therefore, the method has important value and significance for automatically generating the urban major traffic accident data sample through the artificial intelligence algorithm.
Disclosure of Invention
The purpose of the invention is: and the urban major traffic accident data samples are automatically generated, so that a large amount of data is provided for learning and training the deep learning model, the accuracy of the model for predicting the traffic accidents is improved, and a large amount of loss caused by the traffic accidents is saved.
In order to achieve the above object, the technical solution of the present invention is to provide an automatic generation method of an urban major traffic accident data sample based on GAN, which is characterized by comprising the following steps:
step 1, defining urban traffic accident characteristics as four dimensions: weather characteristic, time characteristic, road characteristic and vehicle type characteristic, and carrying out unique hot coding on the characteristics of each accident sample in the real and important traffic accident sample set to obtain the characteristicsEigenvectors as a real data sample library X ═ { X1,x2,…,xi,…,xm}，xiRepresenting the ith feature vector;
step 2, generating an initial fake data set Z ═ { Z ═ Z1,z2,…,zi,…,zm}，ziA feature vector representing an ith false significant traffic accident sample;
step 3, establishing a generating network and an identifying network, wherein the generating network and the identifying network are neural networks, and the generating network and the identifying network are as follows: the number of output layer neurons and the number of input layer neurons of the generated network are the same; the input layer of the identification network has real data and data generated by the generation network, the output layer of the identification network has a neuron, and the output result is a value; then initializing parameters of the generation network and the authentication network, the parameters of the generation network being thetadAnd the parameter of the authentication network is thetag;
step 4, generating a false data set Z ═ { Z ═ from step 21,z2,…,zi,…,zmSampling n samples, inputting the samples into a generating network, and calculating a result generated by the generating network at the moment:
Figure BDA0002552296100000021
and step 5, expressing an objective function V of the authentication network as follows:
Figure BDA0002552296100000022
in the formula, D (x)i) Is the output value of the real data;
Figure BDA0002552296100000023
generating an output value of false data generated by the network in the authentication network;
the weight parameters are updated by a gradient ascent method with the goal of maximizing the objective function V:
Figure BDA0002552296100000024
Figure BDA0002552296100000025
where η denotes a Learning Rate (Learning Rate), which represents the length of each parameter update step,
Figure BDA0002552296100000026
representing the parameter thetagA derivative of (a);
step 6, generating a false data set Z ═ { Z ═ from step 21,z2,…,zi,…,zmN samples are sampled, and the result generated by the network at that time is calculated
Figure BDA0002552296100000027
According to the objective function of the generated network
Figure BDA0002552296100000028
The weight parameters are updated by a gradient-ascending method:
Figure BDA0002552296100000029
in the formula (I), the compound is shown in the specification,
Figure BDA00025522961000000210
representing the parameter thetagA derivative of (a);
and 7, repeating the steps 4 to 6 until the generation network and the identification network are converged, obtaining the trained confrontation generation network, and generating the major traffic accident data after the confrontation generation network is trained.
Preferably, in step 1: the time characteristics comprise whether the time is morning and evening peak time, whether the time is a commute day, whether the time is a holiday, and which month in a year; the road characteristics include a distance to a nearest intersection, a road width, and a road tortuosity.
Preferably, in step 2, when the false data set Z is generated, the exploration rate a% is set, and then, a% of data in the initial false data set Z is generated according to a mutation algorithm in a genetic algorithm, and (100-a)% of data is completely randomly generated.
Preferably, the mutation algorithm in the genetic algorithm comprises the steps of:
step 2.1, respectively counting the mean value, the minimum value and the maximum value of the four dimensional characteristics in the real data sample library X to obtain the mean value mu of the weather characteristic characteristics1Minimum value min1Max, max1(ii) a Mean value mu of the temporal characteristic2Minimum value min2Max, max2(ii) a Mean value mu of road characteristic features3Minimum value min3Max, max3(ii) a Mean value mu of characteristic features of type of vehicle4Minimum value min4Max, max4；
Step 2.2, a mutation algorithm in the genetic algorithm: and (3) according to a Gaussian distribution rule and a 3 sigma theorem, obtaining the mean value mu and the variance (max-min)/3 of 60% of data in the generated false data set Z according to the mean value, the minimum value and the maximum value of the four dimensional characteristics obtained in the step 2.1.
Preferably, in step 3, the generating network and the identifying network are neural networks with hidden layers of 5 layers and each hidden layer contains 4 neurons.
The invention discloses a method for generating urban major traffic accident data samples based on a genetic variation-confrontation generation network (GAN), and aims to automatically generate the urban major traffic accident data samples, so that a large amount of data is provided for learning and training a deep learning model, the accuracy of the model for predicting traffic accidents is improved, and a large amount of loss caused by the traffic accidents is saved. The method defines the urban traffic accident characteristics as four dimensions: the method comprises the steps of obtaining a characteristic vector (vector) as a real data sample library after one-hot coding (one-hot) is carried out on the characteristics of each accident sample according to weather characteristics, time characteristics, road characteristics and vehicle type characteristics. The genetic variation-confrontation generation network mainly comprises two parts, namely a generation network and an identification network, wherein the genetic variation-generation network (Generator) is adopted to generate a false serious traffic accident sample, the identification network (Discrimentor) is used to score the real data and the generated data, the result is fed back to the generation network (Generator), the generated data is improved by the generation network (Generator), and then the identification network is used to score, so that a good generation sample can be obtained after continuous game updating. The method is characterized in that a genetic variation algorithm is firstly used for generating false data samples by combining with a generation network, and meanwhile, the countergeneration network is also firstly used for generating serious traffic accident samples.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of an authentication network.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention provides a method for intelligently and automatically generating an urban accident data sample. The method defines the urban traffic accident characteristics as four dimensions: weather characteristics, time characteristics (whether the time is morning and evening rush hour, whether the time is commuting day, whether the time is holiday, which month in a year), road characteristics (distance from the nearest intersection, road width and road tortuosity) and vehicle type characteristics, and a characteristic vector (vector) is obtained after the characteristic of each accident sample is subjected to one-hot coding (one-hot) and is used as a real data sample library.
The genetic variation-countermeasure generation network mainly comprises two parts, one part is a generation network, the other part is a countermeasure network, a false major traffic accident sample is generated by adopting the genetic variation-generation network (Generator), the identification network (Discrimentor) is used for scoring the real data and the generated data, the result is fed back to the generation network (Generator), the generated data is improved by the generation network (Generator), and then the identification network is used for scoring, so that a good generation sample can be obtained after continuous game updating.
The invention firstly uses a genetic variation algorithm to be combined with a generation network to generate a fake data sample, and simultaneously uses an antagonistic generation network to be also used for automatically generating a serious traffic accident sample for the first time, and the method specifically comprises the following steps:
step 1, defining urban traffic accident characteristics as four dimensions: weather characteristics, time characteristics (whether it is morning and evening rush hour, whether it is commuting day, whether it is holiday, which month of the year), road characteristics (distance to nearest intersection, road width, road tortuosity), vehicle type characteristics. Performing one-hot coding (one-hot) on the characteristics of each accident sample in the real major traffic accident sample set to obtain a characteristic vector, and using the characteristic vector as a real data sample library X ═ X1,x2,…,xi,…,xm}，xiRepresenting the ith feature vector.
Step 2. generating an initial fake data set Z ═ { Z ═ Z1,z2,…,zi,…,zm}，ziFeature vectors representing the ith false significant traffic accident sample: if the exploration rate (exploration rate) is set, for example, equal to 60%, then the algorithm for generating 60% of the data in the initial fake data set Z generates according to the mutation algorithm in the genetic algorithm, and 40% of the data generates completely randomly.
The diversity algorithm in the genetic algorithm is implemented as follows:
step 2.1, respectively counting the mean value, the minimum value and the maximum value of the four dimensional characteristics in the real data sample library X to obtain the mean value mu of the weather characteristic characteristics1Minimum value min1Max, max1(ii) a Mean value mu of the temporal characteristic2Minimum value min2Max, max2(ii) a Mean value mu of road characteristic features3Minimum value min3Max, max3(ii) a Mean value mu of characteristic features of type of vehicle4Minimum value min4Max, max4；
Step 2.2 mutation algorithm in genetic algorithm: and (3) according to a Gaussian distribution rule and a 3 sigma theorem, obtaining the mean value mu and the variance (max-min)/3 of 60% of data in the generated false data set Z according to the mean value, the minimum value and the maximum value of the four dimensional characteristics obtained in the step 2.1.
Step 3. GenerationThe network and the discrimination network are respectively a neural network with hidden layers of 5 layers and each hidden layer containing 4 neurons. The number of output layer neurons and input layer neurons of the generated network is the same. The input layer of the discrimination network has real data and data generated by the generation network, the output layer of the discrimination network has a neuron, and the output result is a value, as shown in fig. 2. The training network updates the weight parameters of the generating network and the identifying network. Initializing parameters of a generating network and an identifying network, the parameters of the generating network being thetadAnd the parameter for authenticating the network is thetag.
Step 4. from the false data set Z ═ { Z ] generated in step 21,z2,…,zi,…,zmN samples are sampled and input into the generating network, and the generating network at that time is calculated (theta)g0) The generated result is as follows:
Figure BDA0002552296100000051
and step 5, expressing an objective function V of the authentication network as follows:
Figure BDA0002552296100000052
in the formula, D (x)i) Is the output value of the real data, and the larger the value is, the better the value is for the authentication network;
Figure BDA0002552296100000053
is the output value of the false data generated by the generation network in the authentication network, and the lower the value is, the better the value is for the authentication network;
so the goal is to maximize the objective function V, the weight parameters are updated by a gradient-ascending method:
Figure BDA0002552296100000054
Figure BDA0002552296100000055
where η denotes a Learning Rate (Learning Rate), which represents the length of each parameter update step,
Figure BDA0002552296100000056
representing the parameter thetagThe derivative of (c).
Step 6. from the false data set Z ═ { Z ] generated in step 21,z2,…,zi,…,zmN samples are sampled, and the net generated at this time is calculated (theta)g) The result of the generation
Figure BDA0002552296100000057
According to the objective function of the generated network
Figure BDA0002552296100000058
The weight parameters are updated by a gradient-ascending method:
Figure BDA0002552296100000059
Figure BDA00025522961000000510
in the formula (I), the compound is shown in the specification,
Figure BDA00025522961000000511
representing the parameter thetagThe derivative of (c).
And 7, repeating the steps 4 to 6 until the generation network and the identification network are converged, obtaining the trained confrontation generation network, and generating the data of the major traffic accident after the confrontation generation network is trained.

Claims (5)

1. A method for automatically generating an urban major traffic accident data sample based on GAN is characterized by comprising the following steps:
step 1, defining urban traffic accident characteristics as four dimensions: the method comprises the steps of carrying out unique hot coding on the characteristics of each accident sample in a real major traffic accident sample set by using weather characteristics, time characteristics, road characteristics and vehicle type characteristics to obtain a characteristic vector, and using the characteristic vector as a real data sample library X ═ X1，x2，...，xi，...，xm}，xiRepresenting the ith feature vector;
step 2Generating an initial fake data set Z ═ { Z ═ Z1，z2，...，zi，...，zm}，ziA feature vector representing an ith false significant traffic accident sample;
step 3, establishing a generating network and an identifying network, wherein the generating network and the identifying network are neural networks, and the generating network and the identifying network are as follows: the number of output layer neurons and the number of input layer neurons of the generated network are the same; the input layer of the identification network has real data and data generated by the generation network, the output layer of the identification network has a neuron, and the output result is a value; then initializing parameters of the generation network and the authentication network, the parameters of the generation network being thetadAnd the parameter of the authentication network is thetag;
step 4, generating a false data set Z ═ { Z ═ from step 21，z2，...，zi，...，zmSampling n samples, inputting the samples into a generating network, and calculating a result generated by the generating network at the moment:
Figure FDA0002552296090000011
and step 5, expressing an objective function V of the authentication network as follows:
Figure FDA0002552296090000012
in the formula, D (x)i) Is the output value of the real data;
Figure FDA0002552296090000013
generating an output value of false data generated by the network in the authentication network;
the weight parameters are updated by a gradient ascent method with the goal of maximizing the objective function V:
Figure FDA0002552296090000014
Figure FDA0002552296090000015
in the formula, η represents a learning rate (bear)A ning Rate), representing the length of each parameter update step,
Figure FDA0002552296090000016
representing the parameter thetagA derivative of (a);
step 6, generating a false data set Z ═ { Z ═ from step 21，z2，...，zi，...，zmN samples are sampled, and the result generated by the network at that time is calculated
Figure FDA0002552296090000017
According to the objective function of the generated network
Figure FDA0002552296090000018
The weight parameters are updated by a gradient-ascending method:
Figure FDA0002552296090000019
in the formula (I), the compound is shown in the specification,
Figure FDA00025522960900000110
representing the parameter thetagA derivative of (a);
and 7, repeating the steps 4 to 6 until the generation network and the identification network are converged, obtaining the trained confrontation generation network, and generating the major traffic accident data after the confrontation generation network is trained.
2. The method for automatically generating the GAN-based urban mass transit accident data sample according to claim 1, wherein in the step 1: the time characteristics comprise whether the time is morning and evening peak time, whether the time is a commute day, whether the time is a holiday, and which month in a year; the road characteristics include a distance to a nearest intersection, a road width, and a road tortuosity.
3. The method as claimed in claim 1, wherein in the step 2, when the false data set Z is generated, the exploration rate a% is set, and then a% of data in the initial false data set Z is generated according to a variation algorithm in a genetic algorithm, and (100-a)% of data is completely randomly generated.
4. The method for automatically generating the GAN-based urban mass transit accident data sample as claimed in claim 1, wherein the mutation algorithm in the genetic algorithm comprises the following steps:
step 2.1, respectively counting the mean value, the minimum value and the maximum value of the four dimensional characteristics in the real data sample library X to obtain the mean value mu of the weather characteristic characteristics1Minimum value min1Max, max1(ii) a Mean value mu of the temporal characteristic2Minimum value min2Max, max2(ii) a Mean value mu of road characteristic features3Minimum value min3Max, max3(ii) a Mean value mu of characteristic features of type of vehicle4Minimum value min4Max, max4；
Step 2.2, a mutation algorithm in the genetic algorithm: and (3) according to a Gaussian distribution rule and a 3 sigma theorem, obtaining the mean value mu and the variance (max-min)/3 of 60% of data in the generated false data set Z according to the mean value, the minimum value and the maximum value of the four dimensional characteristics obtained in the step 2.1.
5. The method according to claim 1, wherein in step 3, the generating network and the discriminating network are neural networks with hidden layers of 5 layers and each hidden layer contains 4 neurons.
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CN113434459A (en) * 2021-06-30 2021-09-24 电子科技大学 Network-on-chip task mapping method based on generation of countermeasure network

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