Construction method of road transfer traffic volume prediction model

Abstract

The invention discloses a construction method of a road transfer traffic volume prediction model, which comprises the following steps: collecting flow monitoring data and influence data of preset road traffic, and preprocessing the flow monitoring data and the influence data; performing time-varying screening on the flow monitoring data to obtain time-varying data, and performing behavior preference analysis on the time-varying data to obtain a transition probability; performing influence analysis on the influence data to obtain an indefinite factor, and constructing a road transfer traffic volume prediction model according to the transfer probability, the indefinite factor and the flow monitoring data; and optimizing the road transfer traffic volume prediction model according to the prediction error, and outputting a target model. The method not only can improve the precision of road transfer traffic volume prediction, but also has good interpretability, and can be directly applied to a road transfer traffic volume prediction system.

Description

Technical Field

[0001] The invention relates to the field of prediction construction, and in particular to a method for constructing a highway transfer traffic volume prediction model. Background Art

[0002] With the acceleration of urbanization and the increasing complexity of transportation networks, accurate prediction of highway traffic volume is of vital importance for traffic management, planning and alleviating traffic congestion. Traditional traffic flow prediction methods often rely on statistical analysis of historical data. However, these methods have obvious limitations in prediction accuracy and adaptability when facing dynamically changing traffic environments. In particular, when faced with non-routine factors such as holidays, special weather, and emergencies, the prediction ability of traditional models is greatly reduced.

[0003] In order to more accurately predict highway transfer traffic volume, that is, the changes in the behavior of vehicles choosing different routes or modes of transportation under different time periods and road conditions, a prediction model that can comprehensively consider multiple influencing factors and has self-optimization capabilities is needed. Existing research and practice have shown that relying solely on historical traffic data is difficult to meet this demand, because changes in traffic flow are not only affected by cyclical factors such as time and season, but also by many uncertain factors such as weather conditions, road construction, traffic accidents, and social and economic activities.

[0004] Therefore, Kou Dai invented a new highway transfer traffic volume prediction model to improve the accuracy, real-time and precision of highway transfer traffic volume prediction. Summary of the invention

[0005] The purpose of the present invention is to provide a method for constructing a highway transfer traffic volume prediction model.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions: The present invention comprises the following steps: Collecting flow monitoring data and impact data of preset highway traffic, and preprocessing the flow monitoring data and impact data; Performing time-varying screening on the traffic monitoring data to obtain time-varying data, and performing behavior preference analysis on the time-varying data to obtain a transfer probability; Performing an impact analysis on the impact data to obtain uncertain factors, and constructing a highway transfer traffic volume prediction model based on the transfer probability, the uncertain factors and the flow monitoring data; The highway transfer traffic volume prediction model is optimized according to the prediction error, and a target model is output.

[0007] Furthermore, the method for obtaining time-varying data by performing time-varying screening on the flow monitoring data includes: Calculate the linear correlation between the flow monitoring data, and cluster the flow monitoring data according to the linear correlation to obtain classification; Calculate the information entropy of traffic monitoring data: ; The information entropy of the yth flow monitoring data is , the number of categories is , the proportion of the yth flow monitoring data of the i-th category is ; Calculate the value of traffic monitoring data: ; The value of the yth dimension reduction data is , flow monitoring data The average value is , the yth flow monitoring data of the i-th classification is , the amount of flow monitoring data is , the number of categories is ; Calculate the time variation of flow monitoring data: ; The yth flow monitoring data is , flow monitoring data The time variation from the sth moment to the s+1th moment is , the yth flow monitoring data at the sth time is , the yth flow monitoring data at the s+1th time is The regulatory factor is ; The flow monitoring data with a time variation greater than 0.3072 is output as time-varying data.

[0008] Furthermore, the method of performing behavior preference analysis on the time-varying data to obtain the transition probability includes: Calculate the transportation time, transportation cost and connection capacity of the highway corresponding to the highway transfer traffic volume, and take shorter transportation time, lower transportation cost and convenient connection as the preferred type; The time series time analysis algorithm is used to perform preference analysis on time-varying data according to preference types to obtain preference quantities and preference weights; Obtain vehicle traffic data. When a road has multiple preference types, the ratio of the number of preferences to the number of times the same vehicle is on the road in different time periods is used as the preference weight. Calculate the transition probability based on the preference weight and preference amount: ; The transition probability of the xth preference type is , the preference weight of the xth preference type is , the preference amount is , the highway transfer traffic volume at time s is , the upper limit of the test time .

[0009] Furthermore, the method of performing an impact analysis on the impact data to obtain uncertain factors includes: Sort the impact data in chronological order, input the sorted impact data into the temporal impact analysis model, and calculate the weighted change of the impact data: ; The weighted change of the ath impact data in the cth monitoring is , the ath impact data at the s+1th moment is , the ath impact data at the sth moment is , the ath impact data is , affecting data The average value of , affecting data The standard deviation of ; Calculate the relevance of impact data to highway traffic transfer volume: ; The impact data is h, and the upper limit of the monitoring time is The inhibitory factor is , the highway traffic transfer volume at the cth monitoring time s is , the ath impact data at the cth monitoring time s is , the highway traffic transfer volume at the cth monitoring time s+1 is , the step function is , the ath impact data at the cth monitoring time s+1 is , the probability is , the highway traffic transfer volume monitored for the cth time is , the correlation degree of influencing data on highway traffic transfer volume is ; The influencing data with a correlation greater than 0.2475 is taken as the key data, and the uncertainty factor is calculated based on the key data: ; The number of impact data for the cth monitoring is , the square of the Euclidean norm is , the genetic factor is , the error coefficient is , the uncertainty factor of the cth monitoring is .

[0010] Furthermore, the method for constructing a highway transfer traffic volume prediction model based on the transfer probability, the uncertain factor and the flow monitoring data includes: The objective function is constructed based on the transfer probability, uncertain factors and flow monitoring data, and the expression is: ; The loss function is , the objective function is , the uncertainty factor of the cth monitoring is , the transition probability of the xth preference type is , the traffic volume monitored for the cth time is ; The highway transfer traffic volume prediction model includes autoencoder, similarity-based matching algorithm, and long short-term memory network algorithm; The autoencoder compresses the input data into a low-dimensional representation through unsupervised learning, and then reconstructs it back to the original data to learn useful features of the input data; The similarity-based matching algorithm quantifies the similarity between useful features, matches according to the similarity, and obtains matching data and unmatched data; The LSTM network algorithm predicts future highway transfer traffic volume by capturing long-term dependencies in time series data and utilizing historical information of matching and unmatched data.

[0011] Furthermore, the method for optimizing the highway transfer traffic volume prediction model according to the prediction error includes: Introduce particle swarm, take prediction error as fitness function, calculate particle fitness, and take the particle position with the smallest fitness value as the optimal position; Calculate the position of the particle: ; The convergence coefficient is , the random numbers from 0 to 1 are , , the best position for the t+1th iteration is , the position of the wth particle in the t+1th iteration is , the position of the wth particle in the tth iteration is ; Update the particle position to obtain the search position. The expression is: ; The search position of the wth particle in the t+1th iteration is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; The step control position is obtained by updating the search position using the step control parameters. The expression is: ; The step control parameters are , the step control position of the wth particle in the t+1th iteration is , the search position of the wth particle in the tth iteration is ; Adaptive weights are introduced to update the search position to obtain the adapted position. The expression is: ; ; The adaptive weight of the tth iteration is , the adjustment coefficient is , the adaptation position of the wth particle in the t+1th iteration is , the step control position of the wth particle in the tth iteration is , the position of the random particle at the tth iteration is , a random number from 0 to 1 is ; Continue to iterate until the maximum number of iterations is reached, otherwise update the adaptive weights.

[0012] In a second aspect, an embodiment of the present application further provides an electronic device, including: A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0013] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0014] The beneficial effects of the present invention are: The present invention is a method for constructing a highway transfer traffic volume prediction model. Compared with the prior art, the present invention has the following technical effects: The present invention can improve the accuracy of highway transfer traffic volume prediction through preprocessing, time-varying screening, behavior preference analysis, impact analysis, model construction and model optimization steps, thereby improving the accuracy of highway transfer traffic volume prediction, and optimizing highway transfer traffic volume prediction, which can greatly save resources and improve work efficiency, and can realize intelligent prediction of highway transfer traffic volume, and perform behavior preference analysis and impact analysis on highway transfer traffic volume prediction in real time, which is of great significance for user intention recognition in multi-dimensional human-computer interaction scenarios, and can adapt to the construction of highway transfer traffic volume prediction models of different standards and the construction requirements of different highway transfer traffic volume prediction models, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of the steps of the method for constructing a highway transfer traffic volume prediction model of the present invention; Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0016] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0017] The method for constructing a highway transfer traffic volume prediction model of the present invention comprises the following steps: like Figure 1 As shown, in this embodiment, the following steps are included: Collecting flow monitoring data and impact data of preset highway traffic, and preprocessing the flow monitoring data and impact data; In the actual evaluation, Highway A in XXX city is taken as the research object, and the influencing data include weather, road conditions, traffic accidents, and holidays; Performing time-varying screening on the traffic monitoring data to obtain time-varying data, and performing behavior preference analysis on the time-varying data to obtain a transfer probability; In the actual evaluation, the time-varying data are 7.50am to 9.05am and 16.55pm to 18.25pm; the transfer probabilities of shorter transportation time, lower transportation cost and convenient connection are 0.291, 0.227 and 0.132 respectively; Performing an impact analysis on the impact data to obtain uncertain factors, and constructing a highway transfer traffic volume prediction model based on the transfer probability, the uncertain factors and the flow monitoring data; In the actual evaluation, the uncertainty factors of section A from 7.50am to 9.05am and from 16.55pm to 18.25pm ​​were 0.4065 and 0.3715 respectively; The highway transfer traffic volume prediction model is optimized according to the prediction error, and a target model is output.

[0018] In this embodiment, the method for obtaining time-varying data by performing time-varying screening on the flow monitoring data includes: Calculate the linear correlation between the flow monitoring data, and cluster the flow monitoring data according to the linear correlation to obtain classification; Calculate the information entropy of traffic monitoring data: ; The information entropy of the yth flow monitoring data is , the number of categories is , the proportion of the yth flow monitoring data of the i-th category is ; Calculate the value of traffic monitoring data: ; The value of the yth dimension reduction data is , flow monitoring data The average value is , the yth flow monitoring data of the i-th classification is , the amount of flow monitoring data is , the number of categories is ; Calculate the time variation of flow monitoring data: ; The yth flow monitoring data is , flow monitoring data The time variation from the sth moment to the s+1th moment is , the yth flow monitoring data at the sth time is , the yth flow monitoring data at the s+1th time is The regulatory factor is ; The flow monitoring data with a time variation greater than 0.3072 is output as time-varying data.

[0019] In this embodiment, the method of performing behavior preference analysis on the time-varying data to obtain the transition probability includes: Calculate the transportation time, transportation cost and connection capacity of the highway corresponding to the highway transfer traffic volume, and take shorter transportation time, lower transportation cost and convenient connection as the preferred type; The time series time analysis algorithm is used to perform preference analysis on time-varying data according to preference types to obtain preference quantities and preference weights; Obtain vehicle traffic data. When a road has multiple preference types, the ratio of the number of preferences to the number of times the same vehicle is on the road in different time periods is used as the preference weight. Calculate the transition probability based on the preference weight and preference amount: ; The transition probability of the xth preference type is , the preference weight of the xth preference type is , the preference amount is , the highway transfer traffic volume at time s is , the upper limit of the test time ; In actual evaluation, the number of road trips is the number of different roads used during transportation, and the number of preferences is the number of times the vehicle chooses the same road.

[0020] In this embodiment, the method of performing influence analysis on the influence data to obtain uncertain factors includes: Sort the impact data in chronological order, input the sorted impact data into the temporal impact analysis model, and calculate the weighted change of the impact data: ; The weighted change of the ath impact data in the cth monitoring is , the ath impact data at the s+1th moment is , the ath impact data at the sth moment is , the ath impact data is , affecting data The average value of , affecting data The standard deviation of ; Calculate the relevance of impact data to highway traffic transfer volume: ; The impact data is h, and the upper limit of the monitoring time is The inhibitory factor is , the highway traffic transfer volume at the cth monitoring time s is , the ath impact data at the cth monitoring time s is , the highway traffic transfer volume at the cth monitoring time s+1 is , the step function is , the ath impact data at the cth monitoring time s+1 is , the probability is , the highway traffic transfer volume monitored for the cth time is , the correlation degree of influencing data on highway traffic transfer volume is ; The influencing data with a correlation greater than 0.2475 is taken as the key data, and the uncertainty factor is calculated based on the key data: ; The number of impact data for the cth monitoring is , the square of the Euclidean norm is , the genetic factor is , the error coefficient is , the uncertainty factor of the cth monitoring is .

[0021] In this embodiment, the method for constructing a highway transfer traffic volume prediction model based on the transfer probability, the uncertainty factor and the flow monitoring data includes: The objective function is constructed based on the transfer probability, uncertain factors and flow monitoring data, and the expression is: ; The loss function is , the objective function is , the uncertainty factor of the cth monitoring is , the transition probability of the xth preference type is , the traffic volume monitored for the cth time is ; The highway transfer traffic volume prediction model includes autoencoder, similarity-based matching algorithm, and long short-term memory network algorithm; The autoencoder compresses the input data into a low-dimensional representation through unsupervised learning, and then reconstructs it back to the original data to learn useful features of the input data; The similarity-based matching algorithm quantifies the similarity between useful features, matches according to the similarity, and obtains matching data and unmatched data; The LSTM network algorithm predicts future highway transfer traffic volume by capturing long-term dependencies in time series data and utilizing historical information of matching and unmatched data.

[0022] In this embodiment, the method for optimizing the highway transfer traffic volume prediction model according to the prediction error includes: Introduce particle swarm, take prediction error as fitness function, calculate particle fitness, and take the particle position with the smallest fitness value as the optimal position; Calculate the position of the particle: ; The convergence coefficient is , the random numbers from 0 to 1 are , , the best position for the t+1th iteration is , the position of the wth particle in the t+1th iteration is , the position of the wth particle in the tth iteration is ; Update the particle position to obtain the search position. The expression is: ; The search position of the wth particle in the t+1th iteration is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; The step control position is obtained by updating the search position using the step control parameters. The expression is: ; The step control parameters are , the step control position of the wth particle in the t+1th iteration is , the search position of the wth particle in the tth iteration is ; Adaptive weights are introduced to update the search position to obtain the adapted position. The expression is: ; ; The adaptive weight of the tth iteration is , the adjustment coefficient is , the adaptation position of the wth particle in the t+1th iteration is , the step control position of the wth particle in the tth iteration is , the position of the random particle at the tth iteration is , a random number from 0 to 1 is ; Continue to iterate until the maximum number of iterations is reached, otherwise update the adaptive weights.

[0023] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0024] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0025] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0026] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a construction device for the highway transfer traffic volume prediction model at the logical level. The processor executes the program stored in the memory and is specifically used to execute the construction method of any highway transfer traffic volume prediction model mentioned above.

[0027] The above application Figure 1The method for constructing a highway transfer traffic volume prediction model disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0028] The electronic device may also perform Figure 1 Construction method of highway transfer traffic volume prediction model and implementation Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0029] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute the aforementioned method for constructing any highway transfer traffic volume prediction model.

[0030] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0031] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0032] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0033] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0034] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0035] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0036] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0037] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0038] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a highway transfer traffic volume prediction model, characterized in that: The following steps are involved: Collecting flow monitoring data and impact data of preset highway traffic, and preprocessing the flow monitoring data and impact data; Performing time-varying screening on the traffic monitoring data to obtain time-varying data, and performing behavior preference analysis on the time-varying data to obtain a transfer probability; Performing an impact analysis on the impact data to obtain uncertain factors, and constructing a highway transfer traffic volume prediction model based on the transfer probability, the uncertain factors and the flow monitoring data; The highway transfer traffic volume prediction model is optimized according to the prediction error, and a target model is output.

2. The method for constructing a highway transfer traffic volume prediction model according to claim 1, characterized in that: The method for obtaining time-varying data by performing time-varying screening on the flow monitoring data comprises: Calculate the linear correlation between the flow monitoring data, and cluster the flow monitoring data according to the linear correlation to obtain classification; Calculate the information entropy of traffic monitoring data: ； The information entropy of the yth flow monitoring data is , the number of categories is , the proportion of the yth flow monitoring data of the i-th category is ; Calculate the value of traffic monitoring data: ； The value of the yth dimension reduction data is , flow monitoring data The average value is , the yth flow monitoring data of the i-th classification is , the amount of flow monitoring data is , the number of categories is ; Calculate the time variation of flow monitoring data: ； The yth flow monitoring data is , flow monitoring data The time variation from the sth moment to the s+1th moment is , the yth flow monitoring data at the sth time is , the yth flow monitoring data at the s+1th time is The regulatory factor is ; The flow monitoring data with a time variation greater than 0.3072 is output as time-varying data.

3. The method for constructing a highway transfer traffic volume prediction model according to claim 1, characterized in that: The method of performing behavior preference analysis on the time-varying data to obtain the transition probability comprises: Calculate the transportation time, transportation cost and connection capacity of the highway corresponding to the highway transfer traffic volume, and take shorter transportation time, lower transportation cost and convenient connection as the preferred type; The time series time analysis algorithm is used to perform preference analysis on time-varying data according to preference types to obtain preference quantities and preference weights; Obtain vehicle traffic data. When a road has multiple preference types, the ratio of the number of preferences to the number of times the same vehicle is on the road in different time periods is used as the preference weight. Calculate the transition probability based on the preference weight and preference amount: ； The transition probability of the xth preference type is , the preference weight of the xth preference type is , the preference amount is , the highway transfer traffic volume at time s is , the upper limit of the test time .

4. The method for constructing a highway transfer traffic volume prediction model according to claim 1, characterized in that: The method of performing an impact analysis on the impact data to obtain uncertain factors includes: Sort the impact data in chronological order, input the sorted impact data into the temporal impact analysis model, and calculate the weighted change of the impact data: ； The weighted change of the ath impact data in the cth monitoring is , the ath impact data at the s+1th moment is , the ath impact data at the sth moment is , the ath impact data is , affecting data The average value of , affecting data The standard deviation of ; Calculate the relevance of impact data to highway traffic transfer volume: ； The impact data is h, and the upper limit of the monitoring time is The inhibitory factor is , the highway traffic transfer volume at the cth monitoring time s is , the ath impact data at the cth monitoring time s is , the highway traffic transfer volume at the cth monitoring time s+1 is , the step function is , the ath impact data at the cth monitoring time s+1 is , the probability is , the highway traffic transfer volume monitored for the cth time is , the correlation degree of influencing data on highway traffic transfer volume is ; The influencing data with a correlation greater than 0.2475 is taken as the key data, and the uncertainty factor is calculated based on the key data: ； The number of impact data for the cth monitoring is , the square of the Euclidean norm is , the genetic factor is , the error coefficient is , the uncertainty factor of the cth monitoring is .

5. The method for constructing a highway transfer traffic volume prediction model according to claim 1, characterized in that: The method for constructing a highway transfer traffic volume prediction model according to the transfer probability, the uncertain factor and the flow monitoring data comprises: The objective function is constructed based on the transfer probability, uncertain factors and flow monitoring data, and the expression is: ； The loss function is , the objective function is , the uncertainty factor of the cth monitoring is , the transition probability of the xth preference type is , the traffic volume monitored for the cth time is ; The highway transfer traffic volume prediction model includes autoencoder, similarity-based matching algorithm, and long short-term memory network algorithm; The autoencoder compresses the input data into a low-dimensional representation through unsupervised learning, and then reconstructs it back to the original data to learn useful features of the input data; The similarity-based matching algorithm quantifies the similarity between useful features, matches according to the similarity, and obtains matching data and unmatched data; The LSTM network algorithm predicts future highway transfer traffic volume by capturing long-term dependencies in time series data and utilizing historical information of matching and unmatched data.

6. The method for constructing a highway transfer traffic volume prediction model according to claim 1, characterized in that: The method for optimizing the highway transfer traffic volume prediction model according to the prediction error comprises: Introduce particle swarm, take prediction error as fitness function, calculate particle fitness, and take the particle position with the smallest fitness value as the optimal position; Calculate the position of the particle: ； The convergence coefficient is , the random numbers from 0 to 1 are , , the best position for the t+1th iteration is , the position of the wth particle in the t+1th iteration is , the position of the wth particle in the tth iteration is ; Update the particle position to obtain the search position. The expression is: ； The search position of the wth particle in the t+1th iteration is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; The step control position is obtained by updating the search position using the step control parameters. The expression is: ； The step control parameters are , the step control position of the wth particle in the t+1th iteration is , the search position of the wth particle in the tth iteration is ; Adaptive weights are introduced to update the search position to obtain the adapted position. The expression is: ； ； The adaptive weight of the tth iteration is , the adjustment coefficient is , the adaptation position of the wth particle in the t+1th iteration is , the step control position of the wth particle in the tth iteration is , the position of the random particle at the tth iteration is , a random number from 0 to 1 is ; Continue to iterate until the maximum number of iterations is reached, otherwise update the adaptive weights.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 6.

Images