WO2023088131A1

WO2023088131A1 - Traffic state prediction method and apparatus, and device, medium and program

Info

Publication number: WO2023088131A1
Application number: PCT/CN2022/130549
Authority: WO
Inventors: 鱼一帆
Original assignee: 中移(上海)信息通信科技有限公司; 中移智行网络科技有限公司; 中国移动通信集团有限公司
Priority date: 2021-11-22
Filing date: 2022-11-08
Publication date: 2023-05-25
Also published as: CN113821985B; CN113821985A

Abstract

A traffic state prediction method and apparatus, and a device, a medium and a program, which relate to the technical field of intelligent traffic. The method comprises: generating a plurality of chromosome units (101), wherein each chromosome unit is used for representing a type of spatial-temporal convolutional network model; respectively calculating, on the basis of a sample set, a loss value of the spatial-temporal convolutional network model corresponding to each of the plurality of chromosome units (102); according to the loss values of spatial-temporal convolutional network models corresponding to the plurality of chromosome units, updating the plurality of chromosome units, and returning to execute the step of respectively calculating, on the basis of a sample set, a loss value of the spatial-temporal convolutional network model corresponding to each of the plurality of chromosome units until a target chromosome unit that meets a preset condition is determined (103); on the basis of a spatial-temporal convolutional network model corresponding to the target chromosome unit, determining a pre-trained spatial-temporal convolutional network model (104); and on the basis of the pre-trained spatial-temporal convolutional network model, predicting a traffic state (105). By means of the method, the accuracy of predicting a traffic state can be improved.

Description

A traffic state prediction method, device, equipment, medium and program

Cross References to Related Applications

This patent application requires that the Chinese patent application number submitted on November 22, 2021 is 202111382079.5, and the applicants are China Mobile (Shanghai) Information and Communication Technology Co., Ltd., China Mobile Zhixing Network Technology Co., Ltd., and China Mobile Communications Group Co., Ltd., and the application name is The priority of "a traffic state prediction method, device and electronic equipment", the entirety of the application is incorporated in this application by reference.

technical field

The present disclosure relates to the technical field of intelligent transportation, and in particular to a traffic state prediction method, device, equipment, medium and program.

Background technique

With the continuous advancement of the urbanization process, the importance of intelligent transportation is getting higher and higher. Traffic status prediction is an important part of intelligent transportation. The predicted traffic status information can help people make travel route decisions, thereby alleviating traffic congestion and improving the happiness of urban living. In the prior art, the traffic state is usually predicted by statistical methods, and the traffic state at the next moment is predicted by counting the number of vehicles and the speed of vehicles within a period of time. However, the statistical method relies on experience to predict the traffic state, and the accuracy of predicting the traffic state is relatively low. Difference.

Contents of the invention

Embodiments of the present disclosure provide a traffic state prediction method, device, equipment, medium, and program to solve the problem in the prior art that traffic state is predicted by statistical methods and the accuracy of traffic state prediction is poor.

In order to solve the above-mentioned technical problems, the present disclosure is achieved as follows:

An embodiment of the present disclosure provides a traffic state prediction method, the method comprising:

Generate a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

Calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

Updating the plurality of chromosome units according to the loss values of the spatio-temporal convolutional network models corresponding to the plurality of chromosome units, and returning to performing the calculation based on the sample set respectively corresponding to each of the chromosome units in the plurality of chromosome units The step of the loss value of the spatio-temporal convolutional network model until the target chromosome unit satisfying the preset condition is determined;

Determining a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

Traffic status is predicted based on the pre-trained spatio-temporal convolutional network model.

In some embodiments, the chromosome unit includes at least one of the following: layer number bits, observation domain bits, and expansion factor bits; wherein, the layer number bits are used to characterize the spatio-temporal convolutional network model The number of layers of the hidden layer, the observation domain bit is used to represent the observation domain of each hidden layer, and the expansion factor bit is used to represent the convolution expansion factor of each hidden layer.

In some embodiments, updating the plurality of chromosome units according to the loss values of the time-space convolutional network models corresponding to the plurality of chromosome units includes: according to the loss values of the time-space convolutional network models corresponding to the plurality of chromosome units Sorting the plurality of chromosome units from high to low in loss value; generating M2 first chromosome units based on the first M1 chromosome units, both M1 and M2 are positive integers, and M1 is greater than or equal to M2; The last M2 chromosome units in the unit are replaced by the M2 first chromosome units.

In some embodiments, the generating M2 first chromosome units based on the first M1 chromosome units includes: performing hybridization processing on the first M1 chromosome units to obtain at least one hybrid chromosome unit; performing hybridization processing on the first M1 chromosome units The mutation process is to obtain at least one mutant chromosome unit; wherein, the M2 first chromosome units include the at least one hybrid chromosome unit and the at least one mutant chromosome unit.

In some embodiments, the target chromosomal unit is the chromosomal unit that is sorted at the top after the number of updates reaches the first preset number; or, the target chromosomal unit is the chromosome unit that is sorted at the top for M3 consecutive times during the update process, M3 greater than or equal to the second preset number of times.

In some embodiments, the prediction of the traffic state based on the pre-trained spatio-temporal convolutional network model includes: inputting the real traffic state of the target road section N prediction moments before the current moment into the pre-trained spatio-temporal convolution Network model; wherein, the spatio-temporal convolutional network model includes an input layer, an output layer and a plurality of hidden layers connected between the input layer and the output layer, and the input layer is used to input the data before the current moment The real traffic state at N prediction moments, the output of each hidden layer in the plurality of hidden layers is obtained by performing convolution calculation on the input of each hidden layer based on the spatiotemporal attention mechanism, and N is a positive integer; based on The output of the output layer determines the predicted traffic state of the target road segment at the predicted time after the current time.

In some embodiments, the input layer is also used to input additional state information, the additional state information is used to characterize the environmental characteristics of the traffic state; the plurality of hidden layers include a first hidden layer, the second hidden layer A hidden layer is connected to the input layer, and the first hidden layer is used to fuse the real traffic state of the N predicted moments before the current moment with the additional state information.

An embodiment of the present disclosure provides a traffic state prediction device, the device comprising:

A generating part configured to generate a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

The calculation part is configured to separately calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

The update part is configured to update the plurality of chromosome units according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units, and return to perform the calculation of each of the plurality of chromosome units based on the sample set. The step of the loss value of the spatio-temporal convolutional network model corresponding to the chromosomal unit, until the target chromosomal unit satisfying the preset condition is determined;

The determining part is configured to determine a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

The prediction part is configured to predict the traffic state based on the pre-trained spatio-temporal convolutional network model.

In some embodiments, the updating part includes: a sorting subsection configured to sort the multiple chromosome units from high to low according to the loss values of the spatio-temporal convolutional network models corresponding to the multiple chromosome units; generating sub-parts, configured to generate M2 first chromosome units based on the first M1 chromosome units, where both M1 and M2 are positive integers, and M1 is greater than or equal to M2; replacing sub-parts, configured to convert the plurality of chromosome units The last M2 chromosome units are replaced by the M2 first chromosome units.

In some embodiments, the generation sub-part is further configured to perform hybridization processing on the first M1 chromosome units to obtain at least one hybrid chromosome unit; perform mutation processing on the first M1 chromosome units to obtain at least one mutant chromosome unit; wherein, the M2 first chromosome units include the at least one hybrid chromosome unit and the at least one variant chromosome unit.

In some embodiments, the prediction part is further configured to input the real traffic state of the target road section N prediction moments before the current moment into the pre-trained spatiotemporal convolutional network model; wherein, the spatiotemporal convolutional network The model includes an input layer, an output layer and a plurality of hidden layers connected between the input layer and the output layer, the input layer is used to input the real traffic status of N prediction moments before the current moment, the The output of each hidden layer in the plurality of hidden layers is obtained by performing convolution calculation on the input of each hidden layer based on the spatiotemporal attention mechanism, and N is a positive integer; the target road section is determined based on the output of the output layer The predicted traffic state at a predicted time after the current time.

An embodiment of the present disclosure provides an electronic device, including: a processor, a memory, and a program stored in the memory and operable on the processor. When the program is executed by the processor, any of the above-mentioned The traffic state prediction method described above.

An embodiment of the present disclosure provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, any one of the traffic state prediction methods described above is implemented.

An embodiment of the present disclosure provides a computer program, the computer program includes computer-readable codes, and when the computer-readable codes run in an electronic device, the processor of the electronic device executes to implement any of the above-mentioned The traffic state prediction method described above.

In the embodiment of the present disclosure, a plurality of chromosome units are generated, and each chromosome unit is used to characterize a class of spatio-temporal convolutional network model; based on the sample set, the time-space corresponding to each of the chromosome units in the plurality of chromosome units is calculated respectively. The loss value of the convolutional network model; update the plurality of chromosome units according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units, and return to perform the calculation of the plurality of chromosome units based on the sample set The step of the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosomal units until the target chromosomal unit satisfying the preset condition is determined; the pre-trained spatio-temporal convolution is determined based on the spatio-temporal convolutional network model corresponding to the target chromosomal unit A network model; predicting a traffic state based on the pre-trained spatio-temporal convolutional network model. In this way, predicting the traffic state through the pre-trained spatio-temporal convolutional network model can improve the accuracy of predicting the traffic state; and using the evolutionary algorithm to optimize the model structure of the spatio-temporal convolutional network model can reduce the parameters of debugging the spatio-temporal convolutional network model the cost of.

In order to make the above-mentioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

FIG. 1 is a flow chart of a traffic state prediction method provided by an embodiment of the present disclosure;

FIG. 2 is one of the schematic structural diagrams of a space-time convolutional network model provided by an embodiment of the present disclosure;

Fig. 3 is one of the schematic diagrams of information transmission in a space-time convolutional network model provided by an embodiment of the present disclosure;

Fig. 4 is the second schematic diagram of information transmission in a space-time convolutional network model provided by an embodiment of the present disclosure;

Fig. 5 is the third schematic diagram of information transmission in a space-time convolutional network model provided by an embodiment of the present disclosure;

FIG. 6 is the second structural schematic diagram of a space-time convolutional network model provided by an embodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of a traffic state prediction device provided by an embodiment of the present disclosure;

Fig. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are part of the embodiments of the present disclosure, not all of them. Based on the embodiments in the present disclosure, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present disclosure.

Referring to FIG. 1, FIG. 1 is a flow chart of a traffic state prediction method provided by an embodiment of the present disclosure. As shown in FIG. 1, the method includes the following steps:

Step 101, generating a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

Step 102, respectively calculating the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

Step 103: Update the plurality of chromosome units according to the loss values of the spatio-temporal convolutional network models corresponding to the plurality of chromosome units, and return to perform the calculation based on the sample set for each of the chromosomes in the plurality of chromosome units The step of the loss value of the spatio-temporal convolutional network model corresponding to the unit, until the target chromosome unit satisfying the preset condition is determined;

Step 104, determining a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

Step 105. Predict traffic status based on the pre-trained spatio-temporal convolutional network model.

Wherein, multiple chromosome units can be randomly combined to generate, and the generated chromosome units can also be deduplicated to avoid repeated chromosome units. For example, the total number of chromosome units can be initialized as 100, and 100 non-repetitive chromosome units can be randomly combined. The sample set can be a training sample set and/or a testing sample set. The chromosome unit may include at least one of the following: layer number bits, observation domain bits, expansion factor bits, etc., wherein the layer number bits are used to represent the hidden layer of the spatio-temporal convolutional network model The number of layers, the observation domain bits are used to characterize the observation domain k of each hidden layer, that is, the number of kernals, and the expansion factor bits are used to characterize the convolution expansion factor d of each hidden layer, namely Dilation factor.

In addition, when calculating the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set, taking a single chromosome unit as an example, it can be randomly selected from the training sample set or the test sample set Select preset time-continuous samples to calculate the loss value of the temporal-spatial convolutional network model corresponding to the chromosome unit; or, randomly select some time-continuous samples from the training sample set to the temporal-spatial convolutional network corresponding to the chromosome unit The model is trained, and when the number of training rounds reaches the preset number of rounds, samples with a preset duration and continuous time are randomly selected from the test sample set to calculate the loss value of the spatio-temporal convolutional network model corresponding to the chromosome unit. The samples used to calculate the loss value for the spatio-temporal convolutional network models corresponding to multiple chromosome units can be the same or different. The preset duration can be 2 hours, 1 hour or 0.5 hours and so on. For example, samples from 4:00 to 5:00 may be used to calculate the loss value. The loss value of the spatio-temporal convolutional network model can be the average of the loss values of the preset time-continuous samples and the real value. The loss value of the preset duration and time-continuous samples and the real value can be calculated, and then averaged to obtain the loss value of the spatio-temporal convolutional network model. The loss value of the spatio-temporal convolutional network model can be used as an evaluation value, which can be used as an indicator of the quality of the chromosome. The smaller the evaluation value, the better the chromosome.

In some embodiments, after the multiple chromosome units are generated, update iterations may be performed on the multiple chromosome units until a target chromosome unit that satisfies the preset condition is determined. The process of updating and iterating multiple chromosomal units may be as follows: respectively calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosomal units in the multiple chromosomal units based on the sample set; The loss value of the space-time convolutional network model of the plurality of chromosome units is updated, and the calculation of the loss value of the space-time convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units is returned based on the sample set. Steps until the target chromosome unit satisfying the preset condition is determined. In some embodiments, the process of updating and iterating multiple chromosome units may be as follows:

(1) Calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set, and perform (2);

(2) Update the plurality of chromosome units according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units, whether there is a target chromosome unit satisfying the preset condition in the updated plurality of chromosome units, and if so, then End the update iteration; if not, return to execute (1).

In addition, each of the chromosomal units can be used to characterize a class of structural spatio-temporal convolutional network models. The structure of a class of spatio-temporal convolutional network models corresponding to different chromosome units is different. The spatiotemporal convolutional network model corresponding to each chromosomal unit may be a type of spatiotemporal convolutional network model represented by each chromosomal unit. The spatiotemporal convolutional network model corresponding to the plurality of chromosome units may be a multi-type spatiotemporal convolutional network model represented by the plurality of chromosome units. The spatio-temporal convolutional network model corresponding to the chromosome unit may be a result of initialization of a type of spatio-temporal convolutional network model represented by the chromosome unit. After the chromosome unit is generated, a class of spatio-temporal convolutional network model represented by the chromosome unit can be initialized to obtain the spatio-temporal convolutional network model corresponding to the chromosome unit. A type of space-time convolutional network model for initializing the chromosomal unit representation may be a model parameter of the space-time convolutional network model corresponding to the chromosomal unit with preset parameters.

In some embodiments, the determining the pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit may be, performing Model training to obtain the pre-trained spatio-temporal convolutional network model. Exemplarily, the chromosome unit includes layer number bits, observation domain bits and expansion factor bits. After the target chromosome is determined, based on the layer number bits, observation domain bits and expansion factor bits of the target chromosome, it can be constructed A spatio-temporal convolutional network model, training the constructed spatio-temporal convolutional network model to obtain the pre-trained spatio-temporal convolutional network model.

In some embodiments, the chromosomal unit comprises at least one of the following:

Layer number bits, observation domain bits, expansion factor bits;

Wherein, the number of layers bit is used to characterize the number of layers of the hidden layer of the spatio-temporal convolutional network model, the observation domain bit is used to characterize the observation domain of each hidden layer, and the expansion factor bit The convolutional dilation factor used to characterize each of said hidden layers.

In addition, the layer number bits may be header coding bits of the chromosome unit, and the number of layer number bits may be 3 bits, 5 bits, or 8 bits, etc., which is not limited in this embodiment. Taking the layer number bits of the chromosome unit as 3 bits as an example, the chromosome unit includes at most 7 hidden layers. The observation field of each hidden layer may be represented by 3 bits, or 5 bits, or 8 bits, etc., which is not limited in this embodiment. The convolution expansion factor of each hidden layer may be represented by 3 bits, or 5 bits, or 8 bits, etc., which is not limited in this embodiment. In some embodiments, when setting the observation domain and the convolution expansion factor, the values of the observation domain and the convolution expansion factor may be set to be smaller than the maximum number of inputs of the input layer.

In one embodiment, the number of bits in the number of layers is v1, the observation field of each hidden layer is represented by v2 bits, and the convolution expansion factor of each hidden layer is represented by v3 bits , for each hidden layer, including the observation field of v2 bits and the convolution expansion factor of v3 bits, if the number of layers of chromosome units represents the number of hidden layers of the space-time convolutional network model is n, then The total number of bits of the chromosome unit is: v1+(v2+v3)*n. For example, the number of bits in the number of layers is 3, the observation field of each hidden layer is represented by 3 bits, and the convolution expansion factor of each hidden layer is represented by 3 bits, for each Hidden layer, including the observation field of 3 bits and the convolution expansion factor of 3 bits, if the number of layers of the chromosomal unit indicates that the number of layers of the hidden layer of the space-time convolutional network model is n, then the number of layers of the chromosomal unit The total number of bits is: 3+(3+3)*n. The 1st to 3rd bits of the chromosome unit represent the number of layers of the hidden layer, the 4th to 6th bits represent the observation domain of the first hidden layer, and the 7th to 9th bits represent the convolution of the first hidden layer The expansion factor, the 10th to 12th bits represent the observation domain of the second hidden layer, the 13th to 15th bits represent the convolution expansion factor of the second hidden layer, and so on, and so on.

For example, the chromosome unit is: 010010010001001, the first to third digits "010" indicate that the number of hidden layers is 2, and the fourth to sixth digits "010" indicate that the observation domain of the first hidden layer is 2, The 7th to 9th "010" indicates that the convolution expansion factor of the first hidden layer is 2, the 10th to 12th "001" indicates that the observation field of the second hidden layer is 1, and the 13th to The 15th bit "001" indicates that the convolution expansion factor of the second hidden layer is 1.

In some embodiments, the chromosome unit may include one or more of layer number bits, observation domain bits, and expansion factor bits. Taking the chromosome unit only including the number of layers as an example, the observation field of each hidden layer and the convolution expansion factor of each hidden layer can be preset; taking the chromosome unit only including the observation field bits as an example, the The number of layers of the hidden layer of the space-time convolutional network model and the convolution expansion factor of each hidden layer can be preset; taking the chromosome unit including the number of layers bit and the observation domain bit as an example, each hidden layer The convolution expansion factor of can be preset.

In this embodiment, the number of layers bit is used to represent the number of hidden layers of the spatio-temporal convolutional network model, the observation field bit is used to represent the observation field of each hidden layer, and the extended The factor bits are used to characterize the convolution expansion factor of each hidden layer; like this, the characteristics of less network parameters of the space-time convolutional network model can be utilized to generate a prediction model that is more suitable for the actual situation for each scene, and then can Reduce the cost of personnel debugging the parameters of the model in different intersection scenarios.

In some embodiments, updating the plurality of chromosome units according to the loss values of the spatio-temporal convolutional network models corresponding to the plurality of chromosome units includes:

sorting the plurality of chromosome units from high to low according to the loss values of the spatio-temporal convolutional network models corresponding to the multiple chromosome units;

Generate M2 first chromosome units based on the first M1 chromosome units, both M1 and M2 are positive integers, and M1 is greater than or equal to M2;

replacing the last M2 chromosome units among the plurality of chromosome units with the M2 first chromosome units.

Wherein, M1 may be a preset value, for example, M1 may be 10, 30 or 50, etc., which is not limited in this embodiment.

In addition, the generating M2 first chromosome units based on the first M1 chromosome units may include performing hybridization processing on the first M1 chromosome units to obtain at least one hybrid chromosome unit, and the M2 first chromosome units may include the at least A hybrid chromosome unit; for example, M2 first chromosome units may be the at least one hybrid chromosome unit; and/or, generating M2 first chromosome units based on the first M1 chromosome units may include, for the first M1 chromosomal units undergo mutation processing to obtain at least one mutated chromosomal unit, wherein the M2 first chromosomal units include the at least one mutated chromosomal unit, for example, the M2 first chromosomal units may be the at least one hybrid chromosome unit.

In some embodiments, before replacing the last M2 chromosome units among the plurality of chromosome units with the M2 first chromosome units, the generated M2 first chromosome units may be checked against existing chromosome units. Repeated chromosomal units are removed.

In this embodiment, the plurality of chromosome units are sorted according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units from high to low; M2 first chromosome units are generated based on the first M1 chromosome units, Both M1 and M2 are positive integers, and M1 is greater than or equal to M2; the last M2 chromosome units in the plurality of chromosome units are replaced with the M2 first chromosome units. In this way, better target chromosome units can be selected by filtering iterative chromosome units through the loss value, so that a spatio-temporal convolutional network model with better performance can be determined.

In some embodiments, the generating M2 first chromosome units based on the first M1 chromosome units includes:

Perform hybridization processing on the first M1 chromosome units to obtain at least one hybrid chromosome unit;

performing mutation processing on the first M1 chromosomal units to obtain at least one mutated chromosomal unit;

Wherein, the M2 first chromosome units include the at least one hybrid chromosome unit and the at least one variant chromosome unit.

Wherein, the hybridization process of the first M1 chromosome units to obtain at least one hybrid chromosome unit may be, in the first M1 chromosome units, part of the bits at the same position of any two chromosome units are exchanged to obtain at least one hybrid chromosome units; or, two chromosome units can be selected from the first M1 chromosome units according to preset rules, and part of the bits at the same position of the selected chromosome units can be exchanged to obtain at least one hybrid chromosome unit. Two chromosome units can be randomly selected from the first M1 chromosome units at a time, and some bits at the same positions of the two selected chromosome units are exchanged for multiple times to obtain multiple hybrid chromosome units. The bits representing the observation field of a certain hidden layer can be swapped, or the bits representing the convolution expansion factor of a certain hidden layer can be swapped. Exemplarily, among the first M1 chromosome units, positions 4 to 6 of any two chromosome units can be exchanged; or, among the first M1 chromosome units, positions 7 to 6 of any two chromosome units can be exchanged. The ninth bit is exchanged; etc., which is not limited in this embodiment.

Exemplarily, the shortest chromosomal unit among the previous M1 chromosomal units can be used as the maximum mutation point, and the shortest chromosomal unit can be hybridized with other chromosomal units. Exemplarily, the chromosomal unit 001001001 with a layer number of 1 and the chromosome unit with a layer number of 2 The chromosomal unit 010010010001001 is hybridized, and the 4th to 6th positions of the two chromosomal units are exchanged to obtain two new chromosomal units.

In addition, the performing mutation processing on the first M1 chromosome units to obtain at least one mutated chromosome unit may be inverting some bits of at least one chromosome unit in the first M1 chromosome units; or, it may be Randomly replace some bits of at least one chromosome unit in the first M1 chromosome units, and so on. Random replacement can be to randomly select a value from 1 or 0 to replace the bit value in the chromosome unit. A chromosome unit may be randomly selected from the first M1 chromosome units, one or more bits may be randomly selected from the selected chromosome unit, and the selected bit may be reversed or randomly replaced. For example, the mutation rate μ can be set for the upper layer of the chromosome unit, the probability is μ=0.001*n1, where n1 is the n1th hidden layer, if the hit rate is hit, the hidden layer will appear in the observation domain, and the random expansion factor of the convolution Mutations to generate new chromosome units.

In this embodiment, hybridization processing is performed on the first M1 chromosome units to obtain at least one hybrid chromosome unit; mutation processing is performed on the first M1 chromosome units to obtain at least one mutant chromosome unit. In this way, the combination of chromosome units with better performance is realized through hybridization and mutation processing, so that a model design that is more suitable for the current scene can be obtained.

In some embodiments, the target chromosomal unit is the chromosomal unit that ranks first after the number of updates reaches the first preset number;

or,

The target chromosomal unit is the first chromosomal unit sorted for M3 consecutive times during the updating process, and M3 is greater than or equal to the second preset number of times.

Wherein, M3 is a positive integer. The first preset number of times may be 300, 400, or 500, etc., which is not limited in this embodiment. The second preset number of times may be 20, 40 or 50, etc., which is not limited in this embodiment. For example, the first preset number of times is 400, and the second preset number of times is 20. After 400 iterations, the chromosome unit sorted at the top can be determined as the target chromosome unit; The top chromosome unit is determined as the target chromosome unit.

In this embodiment, the target chromosomal unit is the chromosomal unit that is sorted at the top after the number of updates reaches the first preset number; or, the target chromosomal unit is the chromosome unit that is sorted at the top for N consecutive times during the update process, and N is greater than or equal to the second preset number of times. Therefore, a spatio-temporal convolutional network model with better performance can be determined through multiple update iterations.

In some embodiments, the prediction of the traffic state based on the pre-trained spatio-temporal convolutional network model includes:

First, input the real traffic state of the target road segment at N predicted moments before the current moment into the pre-trained spatio-temporal convolutional network model; A plurality of hidden layers between the output layer, the input layer is used to input the real traffic state of N prediction moments before the current moment, and the output of each hidden layer in the plurality of hidden layers is based on the space-time The attention mechanism performs convolution calculation on the input of each hidden layer, and N is a positive integer;

Then, based on the output of the output layer, the predicted traffic state of the target road segment at the predicted time after the current time is determined.

Wherein, the real traffic state can be characterized by the ratio of the average vehicle speed of the road section to the free flow speed. The average speed of the road section may be the average speed of the vehicles passing by the target road section within a preset time period as the average speed of the road section. In addition, the duration between every two predicted moments may be 5 minutes, 10 minutes, or 30 minutes, etc., which is not limited in this embodiment. As shown in Figure 2, taking the value of N as 5, the spatio-temporal convolutional network model includes four hidden layers, the observation field of the spatio-temporal convolutional network model is 2, and the convolution expansion factors d are 1, 2, and 4 as an example. The input layer inputs the real traffic state (X1, X2, X3, X4, X5) of the first 5 prediction moments, and the output layer can output the predicted traffic state Y5 of the future prediction moment. The output of each hidden layer in the plurality of hidden layers can be obtained by performing causal convolution calculation on the input of each hidden layer based on the spatio-temporal attention mechanism.

Wherein, the output of the output layer may include the ratio of the average vehicle speed of the road section at the predicted time after the current moment to the free flow speed, and the future traffic state corresponding to the ratio may be determined according to "GBT 33171-2016 Urban Traffic Operation Condition Evaluation Specification" and the like. . For example, if the ratio is greater than 0.7, it can be considered that the traffic is smooth; if the ratio is greater than 0.5 and less than or equal to 0.7, it can be considered that the traffic is basically smooth; if the ratio is greater than 0.4 and less than or equal to 0.5, it can be considered that the traffic is slightly congested; If the ratio is 0.3 and less than or equal to 0.4, it can be considered as moderate traffic congestion; if the ratio is less than or equal to 0.3, it can be considered as severe traffic congestion.

For example, N is 3, the interval between every two predicted moments is 5 minutes, the N predicted moments before the current moment are 10:05, 10:10 and 10:15, and the predicted moments after the current moment are 10:20 , the traffic state at 10:20 can be predicted by the real traffic state at 10:05, 10:10 and 10:15.

In some embodiments, when training the spatio-temporal convolutional network model, the average vehicle speed of the road section can be calculated for the target road section, and the ratio of the average vehicle speed of the road section to the free flow speed at the current moment can be calculated. The calculation method of the average vehicle speed of the road section can be: subtract the time stamp Tc of the same vehicle passing the corresponding electric police checkpoint at the current moment from the time stamp Tp of passing the upstream electric police checkpoint to obtain the passing time T=Tc-Tp , divide the road length D between two electric police checkpoints by the passing time T to get Sv, Sv is the average speed of the vehicle on the target road section, and the average speed of the vehicles passing on the target road section within the preset time can be calculated as the average speed of the road segment. The average speed of each vehicle that passed the target road section in the previous 15 minutes can be calculated every 5 minutes, and the average speed of each vehicle can be calculated according to the traffic flow to obtain the average speed of the road section. 12 pieces of data can be obtained in one hour, and 24 pieces can be obtained in one day. *12＝288 pieces of data. The average vehicle speed of the multi-day road section calculated can be combined and normalized, and the normalized data can be divided into a training set and a test set. The normalized data can be used as a test set with a ratio of 20%, 80 % is divided as the training set. The divided training set and test set can be used to train the spatio-temporal convolutional network model.

If there is no passing vehicle on the target road section within the preset time period, the ratio of the average vehicle speed of the road section to the free flow speed at the current moment may be set to 1. The free flow speed can be the speed at which the traffic flow is not affected by upstream and downstream conditions. In actual use, the free-flow speed can be set as the maximum speed limit value of the target road section.

In addition, when training the spatio-temporal convolutional network model, the samples in the training set can be used for model training of the spatio-temporal convolutional network model, and the early stop (early stop method) method is used to automatically stop the training of the model to prevent the model from overfitting. After pausing, the trained prediction model is obtained, and then the samples in the test set are used to judge whether the prediction model is reliable.

In this embodiment, the real traffic state of the target road section at N predicted moments before the current moment is input into the pre-trained spatio-temporal convolutional network model, wherein the spatio-temporal convolutional network model includes an input layer, an output layer, and a connection between the A plurality of hidden layers between the input layer and the output layer, the input layer is used to input the real traffic state of N predicted moments before the current moment, the output of each hidden layer in the plurality of hidden layers Both are obtained by performing convolution calculation on the input of each hidden layer based on the spatio-temporal attention mechanism, and N is a positive integer; based on the output of the output layer, the predicted traffic state of the target road section at the predicted moment after the current moment is determined . In this way, the predicted traffic state of the target road segment at the predicted time after the current time is determined through the pre-trained spatio-temporal convolutional network model, which can improve the accuracy of the predicted traffic state.

In some embodiments, the input layer is also used to input additional state information, the additional state information is used to characterize the environmental characteristics to which the traffic state belongs;

The multiple hidden layers include a first hidden layer, the first hidden layer is connected to the input layer, and the first hidden layer is used to compare the real traffic state and The additional state information is fused.

Wherein, the additional status information may be weather information, and/or holiday information and the like. The first hidden layer can fuse the time period sequence as the real traffic state with additional state information such as weather information and/or holiday information, so that a single input has unique information. The process of fusion can be to superimpose the real traffic state of the N predicted moments before the current moment with the additional state information; or, it can also be to combine the real traffic state of the N predicted moments before the current moment with the additional state information Dot product calculation; or, it may also be to process the real traffic state and additional state information at N predicted moments before the current moment according to a preset algorithm, etc., which is not limited in this embodiment of the present disclosure. As a simple example, N is 2, and the real traffic status of the two predicted moments before the current moment is: (0.2, 0.5), the weather at the two predicted moments is sunny and rainy respectively, and 0.02 is used to represent sunny, 0.04 represents rain, and the corresponding weather information is: (0.02, 0.04). You can add (0.2, 0.5) and (0.02, 0.04) to get the output of the first hidden layer (0.22, 0.54).

In addition, the plurality of hidden layers may further include a second hidden layer, the second hidden layer is connected to the output layer, and the output layer is used to decode the output of the second hidden layer to obtain The ratio of the average vehicle speed of the road segment to the free flow speed at the prediction time. The process of fusing the real traffic state of the N predicted moments before the current moment with the additional state information can be an encode (encoding) process; the output of the second hidden layer is decoded to obtain the traffic state after the current moment The ratio of the average vehicle speed of the road section at the prediction time to the free flow speed can be a decode (decoding) process. After decoding, a continuous sequence can be obtained. Since no additional return value information is required during prediction, only the ratio of the average vehicle speed to the free flow speed of the road section at the prediction time after the current time can be returned during decoding. As shown in Figure 2, by introducing the encode-decode mechanism, the spatiotemporal convolutional network model can learn the correlation between time spans, and adding additional state information during encoding can help the spatiotemporal convolutional network model distinguish data in different states difference.

In this embodiment, the first hidden layer is used to fuse the real traffic state of the N predicted moments before the current moment with the additional state information, so that the spatio-temporal convolutional network model can learn the traffic state to which Environmental characteristics can improve the prediction accuracy of spatio-temporal convolutional network model.

In some embodiments, the output of the target hidden layer is obtained according to the convolution result and the enhanced residual result, and the convolution result is that the target hidden layer performs convolution processing on the input of the target hidden layer based on a spatio-temporal attention mechanism The enhanced residual result is obtained by performing enhanced residual processing on the input of the target hidden layer by the target hidden layer, and the target hidden layer is any hidden layer in the plurality of hidden layers.

Wherein, the input of the target hidden layer can be convoluted based on the spatio-temporal attention mechanism to obtain the convolution result, and the input of the target hidden layer can be enhanced to obtain the enhanced residual result. According to the convolution result, the enhanced The residual result and the input of the target hidden layer obtain the output of the target hidden layer. For example, an activation function may be used to perform operations on the convolution result, the enhanced residual result, and the input of the target hidden layer to obtain the output of the target hidden layer.

In this embodiment, the output of the target hidden layer is obtained according to the convolution result and the enhanced residual result, and the convolution result is that the target hidden layer convolves the input of the target hidden layer based on the spatio-temporal attention mechanism The enhanced residual result is obtained by performing enhanced residual processing on the input of the target hidden layer by the target hidden layer, and the target hidden layer is any hidden layer in the plurality of hidden layers. In this way, the ability of the target hidden layer to extract important information can be enhanced by enhancing the residual results, and the learning efficiency of the spatio-temporal convolutional network model can be improved.

In some embodiments, the target hidden layer includes K valid nodes, K is a positive integer, and the output of the valid node is the output of the valid node to at least two nodes of the previous hidden layer of the target hidden layer The output is obtained by convolution calculation; the output of the Mth effective node among the K effective nodes is obtained according to the convolution result corresponding to the Mth effective node and the enhanced residual result corresponding to the Mth effective node, and M is A positive integer greater than 1, and M is less than or equal to K.

Wherein, the convolution result corresponding to the Mth effective node, the enhanced residual result corresponding to the Mth effective node, and the node corresponding to the Mth effective node in the previous hidden layer of the target hidden layer can be used by the activation function The output of the operation is performed to obtain the output of the target hidden layer. In this way, the output of each node of the hidden layer includes enhanced residual information. The target hidden layer may be a hidden layer other than the first hidden layer.

Continuing to refer to Figure 2, the space-time convolutional network model includes four hidden layers, the observation field of the space-time convolutional network model is 2, and the convolution expansion factors d are 1, 2, and 4, respectively. In Figure 2, S _i ^j represents the i-th node in the j+1th hidden layer, i is a positive integer, and j is an integer greater than 0. For example, S ₅ ³ represents the fifth node in the fourth hidden layer node. In Figure 2, valid nodes are marked with a circle symbol above the node. Taking the second hidden layer as an example, S ₂ ¹ is the first effective node in the second hidden layer.

In some embodiments, the convolution result corresponding to the Mth effective node is obtained by performing causal convolution calculation on at least two of the P intermediate variables corresponding to the P nodes in the previous hidden layer, The prediction time corresponding to each node among the P nodes is not after the prediction time corresponding to the Mth valid node, and P is a positive integer;

The P intermediate variables corresponding to the P nodes are respectively determined according to the value vectors corresponding to the P nodes and the first weight matrix;

The first weight matrix is obtained by performing dot product calculation on key vectors and query vectors corresponding to the P nodes;

The key vector, value vector and query vector corresponding to the P nodes are determined based on a spatio-temporal attention mechanism.

Wherein, the convolution result corresponding to the Mth effective node may be obtained by performing one-dimensional causal convolution calculation on at least two of the P intermediate variables corresponding to the P nodes in the previous hidden layer. In order to keep the calculation length of each hidden layer the same, you can use zero-padding (zero padding) to add padding (filling) to the non-effective nodes on the left side of each layer, and slowly accumulate the information on the left side of each layer to the right side.

In addition, the first weight matrix can be:

Among them, l is used to represent the previous hidden layer of the target hidden layer, i∈{1,2,…,P}, j∈{1,2,…,P}, d _k is the dimension,

is the key vector corresponding to the i-th node in the key vectors corresponding to the P nodes,

is the query vector corresponding to the jth node among the query vectors corresponding to the P nodes.

In some embodiments, for the first weight matrix, the data in the lower left corner of the matrix can represent future information, the data in the upper right corner of the matrix can be used to represent past information, and the position of i<j in the first weight matrix can be The data of is set to 0, so that only past information can be used.

In addition, the first weight matrix and the value vectors corresponding to the M effective nodes can be processed through the softplus activation function to obtain the second weight matrix, and the corresponding values of the P nodes in the previous hidden layer can be calculated through the second weight matrix. P intermediate variables of

where, t∈{1,2,…,P},

is the second weight matrix,

is the output of the i-th node.

In addition, the key vector (key), value vector (values) and query vector (query) corresponding to the P nodes can be respectively: key: k _P ^l = f(s _P ^l ), query: q _P ^l = g (s _P ^l ), values: v _P ^l =h(s _P ^l ). The functions f, g, and h can be conversion functions of conventional spatiotemporal attention mechanisms. For example, the functions f(s _P ^l ), g(s _P ^l ), h(s _P ^l ) can be both s _P ^l and Different weight matrices are multiplied. As shown in Figure 3, the output Sl of the previous hidden layer of the target hidden layer can be processed by f(x), g(x) and h(x) respectively to obtain the corresponding key vector K, value vector Q and query vector V, so that the output data of the previous hidden layer of the target hidden layer can be mapped to three dimensions, the first weight matrix W l is obtained through the key vector K and the value vector Q, and the second weight matrix W ^l is obtained through the first weight matrix W ^l and the query vector V weight matrix Wa ^l , and get the intermediate variable through the second weight matrix and S ^l

For example, as shown in Figure 4, when calculating _{the effective node S 5 2, the intermediate variables Sa 1 2 to Sa 1} ₅ _can ^be ^determined ^through _the ^space ^- _time attention mechanism, and S For the convolution result Sc ^l ₅ corresponding to ₅ ² , ^the enhanced residual processing _of S ₅ ¹ is performed through the enhanced residual module, and the enhanced residual result Sr ^l ₅ corresponding to S ₅ ² ^is obtained. ₅ and Sr ^l ₅ to obtain S ₅ ² .

In this way, based on the key vector, value vector and query vector determined by the spatio-temporal attention mechanism, the convolution result corresponding to the Mth effective node can be obtained.

In some embodiments, the output of the Mth effective node among the K effective nodes is the output of the node corresponding to the Mth effective node in the previous hidden layer using an activation function, and the Mth effective node The convolution result corresponding to the effective node and the enhanced residual result corresponding to the Mth effective node are calculated and obtained;

The enhanced residual result corresponding to the Mth effective node is obtained by performing enhanced residual processing on the basis of the first weight matrix and the output of the node corresponding to the Mth effective node in the previous hidden layer.

Wherein, the output of the Mth effective node can be the output of the node corresponding to the Mth effective node in the previous hidden layer using the softplus activation function of the Mth effective node, and the Mth effective node The convolution result corresponding to the node and the enhanced residual result corresponding to the Mth effective node are obtained through calculation. The first weight matrix and the value vectors corresponding to the M valid nodes may be processed by a softplus activation function to obtain a second weight matrix. As shown in Figure 5, the data of each row in the second weight matrix W _a ^l is added to obtain the weight W _s ^l of each time step, and the weight W s l of each time step is combined with the weight W _s ^l in the previous hidden layer The Hadamard product is performed on the output S ^l of the node corresponding to the Mth effective node to obtain the enhanced residual result S _r ^l corresponding to the Mth effective node. Because the larger the observation domain of the spatio-temporal convolutional network model is, the easier it is for the network to degrade its learning ability. By calculating the weight of each time step, key information can be preserved.

In some embodiments, since multiple chromosomal units are used to represent spatio-temporal convolutional network models with different layers, the spatio-temporal convolutional network models represented by multiple chromosomal units can be modeled in the same network, as shown in Figure 6 , for the output of the spatio-temporal convolutional network model with a layer number of 1, it can be considered as the prediction result obtained by decoding the output of the first hidden layer of the network. For the output of the space-time convolutional network model with different layers, it can be considered as decoding the data of each layer, generating the prediction result of the current layer and saving it as Yn. Since evolutionary algorithms are used to generate multiple chromosome units, the weight matrix of each node will be put into a matrix to store the corresponding individual weight matrix. Delete the weight value of eliminated individuals to reduce space occupation and search time.

In some embodiments, the spatio-temporal convolutional network model is obtained through training based on root mean square error analysis algorithm.

Among them, the error analysis of the space-time convolutional network model can be performed through RMSE (root mean square error analysis), and the calculation method of RMSE can be:

Y _t represents the real traffic state,

is the predicted value, and m is the total number of predicted values. Error analysis is performed by comparing the predicted value with the real traffic state. When the error is less than the preset value, the model training can be considered complete.

As a feasible implementation, the traffic state prediction method may include a model training process, a model acquisition process, and a model prediction process. The model training process may be as follows: extract the average vehicle speed of the vehicle passing through the target road section from the roadside equipment of the target road section The average speed of the target road section can be calculated every 5 minutes between 0:00 and 24:00 every day, and the ratio of the average speed of the road section to the free flow speed can be calculated. If there is no passing vehicle within the statistical time during a certain statistics, the ratio of the average vehicle speed to the free flow speed of the road section in this statistics is set to 1. The ratio of the calculated average vehicle speed to the free flow speed of the road section is normalized, and the normalized data is divided into a training set and a test set. Generate a plurality of chromosome units, each of which is used to represent a class of spatiotemporal convolutional network model, use the training set to train the spatiotemporal convolutional network model corresponding to the chromosome unit, and iteratively update the multiple chromosome units to optimize the spatiotemporal volume The structure of the product network model. After determining the structure of the spatiotemporal convolutional network model, the spatiotemporal convolutional network model is trained by the training set, and the spatiotemporal convolutional network model is evaluated by the test set. It is judged whether the prediction result of the trained model is within the error range, if so, it is judged that the model training has been completed; if not, the model is retrained and optimized. The model acquisition process can be as follows: an encode-decode mechanism can be introduced in the spatiotemporal convolutional network model, and the output of each hidden layer in the spatiotemporal convolutional network model is based on the spatiotemporal attention mechanism to convolve the input of each hidden layer Calculated to obtain a spatio-temporal convolutional network model based on the spatio-temporal attention mechanism. The model prediction process is as follows: input the real traffic status of the N predicted moments before the current moment into the trained spatio-temporal convolutional network model, and the output of the spatio-temporal convolutional network model includes the average vehicle speed and free traffic at the predicted moment after the current moment. The ratio of flow velocity. If the ratio is greater than 0.7, it can be considered smooth traffic; if the ratio is greater than 0.5 and less than or equal to 0.7, it can be considered that the traffic is basically smooth; if the ratio is greater than 0.4 and less than or equal to 0.5, it can be considered mild traffic congestion; If it is equal to 0.4, it can be considered as moderate traffic congestion; if the ratio is less than or equal to 0.3, it can be considered as severe traffic congestion.

In addition, encode-decode can use any encoding method, which can be used to add additional status information such as location and/or weather. The attention mechanism itself cannot express the location information between each time node when calculating the weight, and the convolutional network itself has a vague memory of the long-distance location information. By using encode-decode with the attention mechanism and the space-time convolutional neural network make up for each other's shortcomings.

In some embodiments, a chromosome unit is equivalent to a model structure, and different combinations generate countless possible solutions of the network structure. A chromosomal unit is equivalent to an instance of this scheme. By screening and iterating individuals, the superiority of the scheme can be verified, and through subtle changes, such as mutation and generation of new individuals, the possibility of combining advantages can be expanded. The final iterative solution is the model design that is most suitable for the current scene.

In the embodiment of the present disclosure, the spatio-temporal convolutional network model is used in combination with the attention mechanism to allow the network to learn key points through dynamic attention weights, which can only be used in the past for short-term predictions, and can expand the length of the input time period, thereby improving the prediction time The accuracy of time data; and use the spatio-temporal convolutional network model with the attention mechanism to make the learning more efficient by learning the weight different from the previous standardized method, which can indirectly reduce the training time and calculation amount; and, using encode- Decode cooperates with the attention mechanism so that the network can learn the correlation between time spans, and add additional state information such as holidays or rest days or weather when encoding to help the model distinguish data differences in different states; in this way, an evolutionary algorithm is used. The optimization at the network structure level is universal, that is, this solution can be used for any intersection, which can reduce the cost of personnel adjustment and design.

Referring to FIG. 7, FIG. 7 is a schematic structural diagram of a traffic state prediction device provided by an embodiment of the present disclosure. As shown in FIG. 7, the traffic state prediction device 200 includes:

The generating part 201 is configured to generate a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

The calculation part 202 is configured to separately calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

The update part 203 is configured to update the plurality of chromosome units according to the loss values of the spatio-temporal convolutional network models corresponding to the plurality of chromosome units, and return to perform the calculation based on the sample set for each of the plurality of chromosome units. The step of the loss value of the spatiotemporal convolutional network model corresponding to each of the chromosomal units, until the target chromosomal unit satisfying the preset condition is determined;

The determining part 204 is configured to determine a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

The prediction part 205 is configured to predict the traffic state based on the pre-trained spatio-temporal convolutional network model.

Layer number bits, observation domain bits, expansion factor bits;

Wherein, the number of layers bit is configured to represent the number of hidden layers of the spatio-temporal convolutional network model, the observation field bit is configured to represent the observation field of each hidden layer, and the expansion factor Bits are configured to characterize the convolutional dilation factor of each of said hidden layers.

In some embodiments, the update section 203 includes:

The sorting subpart is configured to sort the multiple chromosome units from high to low according to the loss values of the spatio-temporal convolutional network models corresponding to the multiple chromosome units;

The generating subpart is configured to generate M2 first chromosome units based on the first M1 chromosome units, where both M1 and M2 are positive integers, and M1 is greater than or equal to M2;

The replacement subpart is configured to replace the last M2 chromosome units among the plurality of chromosome units with the M2 first chromosome units.

In some embodiments, the generation subsection is further configured to:

or,

In some embodiments, the prediction part 205 is further configured to:

Input the real traffic state of the target road section at N predicted moments before the current moment into the pre-trained spatio-temporal convolutional network model; A plurality of hidden layers between the output layer, the input layer is used to input the real traffic state of N prediction moments before the current moment, and the output of each hidden layer in the plurality of hidden layers is based on the space-time The attention mechanism performs convolution calculation on the input of each hidden layer, and N is a positive integer;

The predicted traffic state of the target road segment at a predicted time after the current time is determined based on the output of the output layer.

Wherein, the traffic state predicting device can realize each process realized in the method embodiment of Fig. 1, and can achieve the same technical effect, no longer describe here.

As shown in FIG. 8 , an embodiment of the present disclosure also provides an electronic device 300, including: a processor 301, a memory 302, and a program stored in the memory 302 and executable on the processor 301, the When the program is executed by the processor 301, each process of the above-mentioned embodiment of the traffic state prediction method can be realized, and the same technical effect can be achieved, so no further description is given here.

Embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned embodiment of the traffic state prediction method can be achieved, and the same The technical effect will not be described here. Wherein, the computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and the like.

An embodiment of the present disclosure provides a computer program, the computer program includes computer-readable codes, and when the computer-readable codes run in an electronic device, the processor of the electronic device executes to implement the above-mentioned Each process of the embodiment of the traffic state prediction method can achieve the same technical effect, and will not be described here again.

As used herein, the terms "comprises," "comprises," or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article, or apparatus that includes a set of elements includes not only those elements, but also includes the elements not expressly included. other elements listed, or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present disclosure can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to enable a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present disclosure.

The embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, but the present disclosure is not limited to the above-mentioned specific embodiments. The above-mentioned disclosed embodiments are only illustrative, not restrictive. Under the inspiration of the present disclosure, without departing from the purpose of the present disclosure and the protection scope of the claims, many forms can be made, all of which are within the protection of the present disclosure.

Industrial Applicability

The present disclosure provides a traffic state prediction method, device, equipment, medium and program, which relate to the field of intelligent transportation technology. The method includes: generating a plurality of chromosome units, each chromosome unit is used to represent a class of spatio-temporal convolutional network model; Calculate the loss value of the time-space convolutional network model corresponding to each chromosome unit in multiple chromosome units based on the sample set; update multiple chromosome units according to the loss value of the time-space convolutional network model corresponding to multiple chromosome units, and return to execute based on The step of calculating the loss value of the time-space convolutional network model corresponding to each chromosome unit in the sample set, until the target chromosome unit that meets the preset conditions is determined; based on the time-space convolutional network model corresponding to the target chromosome unit Trained spatio-temporal convolutional network model; predict traffic status based on pre-trained spatio-temporal convolutional network model. The present disclosure can improve the accuracy of predicting the traffic state.

Claims

A traffic state prediction method, the method comprising:

Generate a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

Calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

Updating the plurality of chromosome units according to the loss values of the spatio-temporal convolutional network models corresponding to the plurality of chromosome units, and returning to performing the calculation based on the sample set respectively corresponding to each of the chromosome units in the plurality of chromosome units The step of the loss value of the spatio-temporal convolutional network model until the target chromosome unit satisfying the preset condition is determined;

Determining a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

Traffic status is predicted based on the pre-trained spatio-temporal convolutional network model.
The method according to claim 1, wherein the chromosomal unit comprises at least one of the following:

Layer number bits, observation domain bits, expansion factor bits;

Wherein, the number of layers bit is used to characterize the number of layers of the hidden layer of the spatio-temporal convolutional network model, the observation domain bit is used to characterize the observation domain of each hidden layer, and the expansion factor bit The convolutional dilation factor used to characterize each of said hidden layers.
The method according to claim 1, wherein updating the plurality of chromosome units according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units comprises:

sorting the plurality of chromosome units from high to low according to the loss values of the spatio-temporal convolutional network models corresponding to the multiple chromosome units;

Generate M2 first chromosome units based on the first M1 chromosome units, both M1 and M2 are positive integers, and M1 is greater than or equal to M2;

replacing the last M2 chromosome units among the plurality of chromosome units with the M2 first chromosome units.
The method according to claim 3, wherein said generating M2 first chromosome units based on the first M1 chromosome units comprises:

Perform hybridization processing on the first M1 chromosome units to obtain at least one hybrid chromosome unit;

performing mutation processing on the first M1 chromosomal units to obtain at least one mutated chromosomal unit;

Wherein, the M2 first chromosome units include the at least one hybrid chromosome unit and the at least one variant chromosome unit.
The method according to claim 3, wherein the target chromosome unit is the chromosome unit ranked first after the number of updates reaches the first preset number;

or,

The target chromosomal unit is the first chromosomal unit sorted for M3 consecutive times during the updating process, and M3 is greater than or equal to the second preset number of times.
The method according to claim 1, wherein the predicting traffic state based on the pre-trained spatio-temporal convolutional network model comprises:

Input the real traffic state of the target road section at N predicted moments before the current moment into the pre-trained spatio-temporal convolutional network model; A plurality of hidden layers between the output layer, the input layer is used to input the real traffic state of N prediction moments before the current moment, and the output of each hidden layer in the plurality of hidden layers is based on the space-time The attention mechanism performs convolution calculation on the input of each hidden layer, and N is a positive integer;

Based on the output of the output layer, the predicted traffic state of the target road section at the predicted moment after the current moment is determined.
The method according to claim 6, wherein the input layer is further used to input additional state information, and the additional state information is used to characterize the environmental characteristics to which the traffic state belongs;

The multiple hidden layers include a first hidden layer, the first hidden layer is connected to the input layer, and the first hidden layer is used to compare the real traffic state and The additional state information is fused.
A traffic state prediction device, said device comprising:

A generating part configured to generate a plurality of chromosome units, each of which is used to represent a class of spatio-temporal convolutional network models;

The calculation part is configured to separately calculate the loss value of the spatio-temporal convolutional network model corresponding to each of the chromosome units in the plurality of chromosome units based on the sample set;

The update part is configured to update the plurality of chromosome units according to the loss value of the spatio-temporal convolutional network model corresponding to the plurality of chromosome units, and return to perform the calculation of each of the plurality of chromosome units based on the sample set. The step of the loss value of the spatio-temporal convolutional network model corresponding to the chromosomal unit, until the target chromosomal unit satisfying the preset condition is determined;

The determining part is configured to determine a pre-trained spatiotemporal convolutional network model based on the spatiotemporal convolutional network model corresponding to the target chromosome unit;

The prediction part is configured to predict the traffic state based on the pre-trained spatio-temporal convolutional network model.
An electronic device, comprising: a processor, a memory, and a program stored on the memory and operable on the processor, when the program is executed by the processor, any one of claims 1 to 7 can be realized. The traffic state prediction method described in the item.
A computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the traffic state prediction method according to any one of claims 1 to 7 is implemented.
A computer program, said computer program comprising computer readable code, in the case of said computer readable code running in an electronic device, a processor of said electronic device executes to implement any one of claims 1 to 7 A described traffic state prediction method.