Expressway congestion prediction method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of road condition prediction, and particularly discloses a highway congestion prediction method and system, electronic equipment and a storage medium, and the method comprises the steps: determining a plurality of reference dates corresponding to a current date based on the date attribute of the current date, calculating periodic road condition data corresponding to the current date according to reference monitoring video data of a preset monitoring point of a target expressway at each reference date; according to the periodic road condition data and the current monitoring video data of the preset monitoring point position at the current date, obtaining a congestion prediction result of a monitoring road section of the target expressway corresponding to the preset monitoring point position; wherein the congestion prediction result represents whether the monitored road section is congested or not within a preset time period of the current date. According to the method, the congestion conditions of the expressway at different dates are predicted in combination with the multi-dimensional data, and the accuracy and efficiency of congestion prediction can be improved.

Description

Background Art

[0002] At present, existing road congestion prediction methods cannot accurately predict congestion situations for special events (such as holidays), lack the continuity of predicting road conditions in the relatively near future based on real-time road conditions, and are unable to make more accurate predictions of future road conditions.

[0003] Therefore, there is an urgent need to provide a technical solution to solve the above problems. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a highway congestion prediction method, system, electronic device, and storage medium.

[0005] In a first aspect, the present invention provides a highway congestion prediction method, and the technical solution of this method is as follows: Based on the date attribute of the current date, determine a plurality of reference dates corresponding to the current date; According to the reference road condition indexes of the preset monitoring points of the target highway in the reference time periods of each reference date, calculate the current road condition index corresponding to the time period to which the current moment of the current date belongs, and obtain a congestion prediction result according to the current road condition index and the current vehicle monitoring data of the preset monitoring points at the current moment; wherein, the reference time period is: the time period that is the same as the time period to which the current moment belongs.

[0006] The beneficial effects of a highway congestion prediction method of the present invention are as follows: The method of the present invention predicts the congestion situation of the highway on different dates by combining multi-dimensional data, and can improve the accuracy and efficiency of congestion prediction.

[0007] On the basis of the above solution, a highway congestion prediction method of the present invention can also be improved as follows.

[0008] In an optional manner, the date attribute is: holiday or non-holiday; the step of determining a plurality of reference dates corresponding to the current date based on the date attribute of the current date includes: When the date attribute of the current date is a holiday, determine each holiday within the preset number of days before the current date and the day before and after each holiday as reference dates; When the date attribute of the current date is a non-holiday, determine the target date within the preset number of days before the current date as the reference date; wherein, the target date is: the date having the same week as the current date.

[0009] In an alternative manner, the step of calculating the current traffic condition index corresponding to the current time period of the current date based on the reference traffic condition indices of the preset monitoring points of the target highway in the reference time periods of each reference date includes: Obtaining the reference traffic condition index of each reference time period until the reference traffic condition indices of each reference time period are obtained according to the average value of the vehicle monitoring data in each sub-time period within any reference time period; When the date attribute of the current date is a holiday, determining the average value of all reference traffic condition indices as the current traffic condition index; When the date attribute of the current date is not a holiday, determining the current traffic condition index based on the reference traffic condition indices of the reference time periods of the preset number of reference dates adjacent to the current date and in combination with the average value of all reference traffic condition indices.

[0010] In an alternative manner, the step of obtaining the congestion prediction result based on the current traffic condition index and the current vehicle monitoring data at the preset monitoring point at the current time includes: Determining the predicted traffic condition index within a preset time period after the current time according to the current traffic condition index and the current vehicle monitoring data, and obtaining the congestion prediction result according to the predicted traffic condition index.

[0011] In an alternative manner, the preset time period includes: a first preset time period composed of the current time to a first preset time, a second preset time period composed of the first preset time to a second preset time, and a third preset time period composed of the second preset time to a third preset time; the predicted traffic condition indices include: a first predicted traffic condition index, a second predicted traffic condition index, and a third predicted traffic condition index; the step of determining the predicted traffic condition index within the preset time period after the current time according to the current traffic condition index and the current vehicle monitoring data includes: Calculating the first predicted traffic condition index in the first preset time period according to the current traffic condition index, the current vehicle monitoring data, and the first weight value set corresponding to the first preset time period; Calculating the second predicted traffic condition index in the second preset time period according to the current traffic condition index, the current vehicle monitoring data, and the second weight value set corresponding to the second preset time period; Calculating the third predicted traffic condition index in the third preset time period according to the current traffic condition index, the current vehicle monitoring data, and the third weight value set corresponding to the third preset time period.

[0012] In an alternative manner, the vehicle monitoring data is vehicle density data; the process of obtaining the vehicle monitoring data at the preset monitoring point at any time includes: Obtain the monitoring images collected at the preset monitoring point at any moment, and use the LargeKernel3D algorithm to obtain the three-dimensional detection frames of each vehicle in the monitoring images; Accumulate the size information of the bottom detection frames of each three-dimensional detection frame to obtain the vehicle occupancy information in the monitoring images, and obtain the vehicle density data at the preset monitoring point at any moment according to the ratio between the vehicle occupancy information and the road occupancy information in the monitoring images; wherein, the size information is: the long side or the area.

[0013] In a second aspect, the present invention provides a highway congestion prediction system, and the technical solution of the system is as follows: It includes: a processing module and a prediction module; The processing module is used to: determine a plurality of reference dates corresponding to the current date based on the date attribute of the current date; The prediction module is used to: calculate the current traffic condition index corresponding to the time period to which the current moment of the current date belongs according to the reference traffic condition indexes of the preset monitoring points of the target highway in the reference time periods of each reference date, and obtain the congestion prediction result according to the current traffic condition index and the current vehicle monitoring data at the preset monitoring point at the current moment; wherein, the reference time period is: the same time period as the time period to which the current moment belongs.

[0014] The beneficial effects of a highway congestion prediction system of the present invention are as follows: The system of the present invention can improve the accuracy and efficiency of congestion prediction by combining multi-dimensional data to predict the congestion conditions of highways on different dates.

[0015] On the basis of the above solution, a highway congestion prediction system of the present invention can also be improved as follows.

[0016] In an optional manner, the date attribute is: holiday or non-holiday; specifically, the processing module is used to: when the date attribute of the current date is a holiday, determine each holiday within the preset number of days before the current date and the day before and after each holiday as reference dates; When the date attribute of the current date is a non-holiday, determine the target dates within the preset number of days before the current date as reference dates; wherein, the target date is: the date with the same week as the current date.

[0017] In a third aspect, the technical solution of an electronic device of the present invention is as follows: It includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements the steps of the highway congestion prediction method of the present invention.

[0018] In a fourth aspect, a technical solution of a computer-readable storage medium provided by the present invention is as follows: Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, it causes the computer-readable storage medium to execute the steps of the highway congestion prediction method of the present invention.

[0019] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are hereinafter specifically exemplified. Description of the Drawings

[0020] The drawings are only used to illustrate the embodiments and are not considered to limit the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 It is a schematic flowchart of an embodiment of a highway congestion prediction method of the present invention; Figure 2 It is a schematic structural diagram of an embodiment of a highway congestion prediction system of the present invention; Figure 3 It is a schematic structural diagram of an embodiment of an electronic device of the present invention. Detailed Embodiments

[0021] Hereinafter, the exemplary embodiments of the present invention will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein.

[0022] Figure 1The figure shows a schematic flowchart of an embodiment of a highway congestion prediction method provided by the present invention. This highway congestion prediction method can be executed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the highway congestion prediction method by a processor calling computer-readable instructions stored in a memory. As Figure 1 shown, the method includes the following steps: S1. Based on the date attribute of the current date, determine multiple reference dates corresponding to the current date.

[0023] Among them, the date attribute is defaulted to: holiday or non-holiday, and can also be adjusted according to the actual situation, such as extreme weather (road icing, fog influence, etc.) or non-extreme weather, without limitation here. The reference date is a date before the current date.

[0024] S2. According to the reference road condition indexes of the preset monitoring points of the target highway in the reference time periods of each reference date, calculate the current road condition index corresponding to the time period to which the current moment of the current date belongs, and obtain a congestion prediction result according to the current road condition index and the current vehicle monitoring data of the preset monitoring points at the current moment.

[0025] Among them, the target highway is any selected highway in this embodiment. There are multiple monitoring points set on the target highway, and the preset monitoring point is one of them. A camera is respectively set at each monitoring point, and each camera covers a monitoring area for collecting video data of the corresponding monitoring area. The monitoring areas covered by two adjacent cameras are continuous (there can be overlapping areas, but there cannot be discontinuities), that is, the dead-angle-free monitoring of the target highway is realized. For example, assuming that the shooting radius of the camera at each monitoring point is 500m, then theoretically a camera is set every 1km. In the actual layout process, it can be fine-tuned according to the actual situation of the highway (gradient, bend, tunnel, etc.) to ensure the dead-angle-free monitoring of the target highway.

[0026] Among them, the reference period is: the same period as the period to which the current moment belongs. The period to which the current moment belongs is: the period including the current moment, and the duration and start and end times of this period can be set according to the actual situation. For example, the duration of each period is defaulted to 30 minutes, and the start time and end time are both at the whole hour or half hour of each hour. When the start time of a certain period is at the whole hour of a certain hour, the end time of this period is at the half hour of the corresponding hour; when the start time of a certain period is at the half hour of a certain hour, the end time of this period is at the whole hour of the next hour of the corresponding hour. Assuming the current moment is 16:05, then the period to which it belongs is 16:00 - 16:30, and the reference period of each reference date is 16:00 - 16:30.

[0027] Among them, the vehicle monitoring data is: the data obtained by monitoring the vehicle operation state at the current moment at the preset monitoring point, which can specifically be but is not limited to: vehicle density, vehicle quantity, vehicle type, etc., and there is no limit here. The road condition index refers to the data information reflecting the road traffic condition during a specific period. The reference road condition index refers to the data information reflecting the road traffic condition during the reference period, and the current road condition index refers to the data information reflecting the road traffic condition during the current period. The congestion prediction result represents whether congestion occurs on the monitoring section corresponding to the preset monitoring point of the target expressway within the preset period. The congestion prediction result is specifically: predicted congestion or predicted no congestion.

[0028] In an optional manner, S1 includes: When the date attribute of the current date is a holiday, each holiday within the preset number of days before the current date and the day before and after each holiday are determined as reference dates.

[0029] Among them, the preset number of days is defaulted to one year, and can also be adjusted according to the actual situation, and there is no limit here. For example, assuming the current date is February 1, 2025, the preset number of days before the current date refers to the total number of days from February 1, 2024 to January 31, 2025, and the reference dates include each holiday within the period from February 1, 2024 to January 31, 2025 and the day before and after each holiday. Taking one of the holidays, January 1, 2025, as an example, the reference dates corresponding to the holiday January 1, 2025 include: December 31, 2024, January 1, 2025, and January 2, 2025.

[0030] When the date attribute of the current date is a non - holiday, the target dates within the preset number of days before the current date are determined as reference dates.

[0031] Among them, the target date is: the date with the same day of the week as the current date. For example, if the current date is Monday, the target date is also Monday. Assume the current date is February 1, 2025 (Saturday), then the preset number of days before the current date refers to the total number of days from February 1, 2024 to January 31, 2025, and the reference dates include each Saturday from February 1, 2024 to January 31, 2025.

[0032] In an optional manner, the steps of calculating the current traffic condition index corresponding to the time period to which the current moment of the current date belongs according to the reference traffic condition indexes of the preset monitoring points of the target highway in the reference time periods of each reference date include: Obtain the reference traffic condition index of each reference time period according to the average value of the vehicle monitoring data of each sub-time period within any reference time period until the reference traffic condition indexes of each reference time period are obtained.

[0033] Among them, each sub-time period of the reference time period does not overlap with each other, the duration of the sub-time period is defaulted to 1 minute, and it can also be adjusted according to the actual situation, without limitation here. In this embodiment, the vehicle monitoring data is defaulted to vehicle density data.

[0034] When the date attribute of the current date is a holiday, determine the average value of all reference traffic condition indexes as the current traffic condition index.

[0035] When the date attribute of the current date is not a holiday, determine the current traffic condition index based on the reference traffic condition indexes of the reference time periods of the preset number of reference dates adjacent to the current date and in combination with the average value of all reference traffic condition indexes.

[0036] Among them, the preset number is defaulted to three, and it can also be adjusted according to the actual situation, without limitation here. The three reference dates adjacent to the current date are: the reference date of the previous week of the current date, the reference date of the previous two weeks, and the reference date of the previous three weeks. Assume the current date is February 1, 2025 (Saturday), then the three reference dates adjacent to the current date are January 25, 2025, January 18, 2025, and January 11, 2025.

[0037] The steps of determining the current traffic condition index based on the reference traffic condition indexes of the reference time periods of the preset number of reference dates adjacent to the current date and in combination with the average value of all reference traffic condition indexes include: Determine the current traffic condition index based on the first preset formula and in combination with the reference traffic condition indexes of the reference time periods of the preset number of reference dates adjacent to the current date and the average value of all reference traffic condition indexes.

[0038] Among them, the first preset formula is: XA is the current road condition index, is the average value of all reference road condition indices. X1 is the reference road condition index of the reference period of the reference date in the previous week of the current date, X2 is the reference road condition index of the reference period of the reference date in the two weeks before the current date, X3 is the reference road condition index of the reference period of the reference date in the three weeks before the current date, a1, a2, a3, and a4 are weight values (the default value of each weight value is equal, and can also be adjusted according to the actual situation), and a1 + a2 + a3 + a4 = 1.

[0039] In an optional manner, the step of obtaining the congestion prediction result according to the current road condition index and the current vehicle monitoring data at the current moment of the preset monitoring point includes: Determine the predicted road condition index within a preset period after the current moment according to the current road condition index and the current vehicle monitoring data, and obtain the congestion prediction result according to the predicted road condition index.

[0040] Among them, the preset period includes: a first preset period composed of the current moment to the first preset moment, a second preset period composed of the first preset moment to the second preset moment, and a third preset period composed of the second preset moment to the third preset moment. The durations of the first preset period, the second preset period, and the third preset period are the same. The predicted road condition indices include: a first predicted road condition index, a second predicted road condition index, and a third predicted road condition index. For example, assuming that the current moment is 16:00 and the default duration of each preset period is 30 minutes, then the first preset moment is 16:30, the second preset moment is 17:00, the third preset moment is 17:30, the first preset period is 16:00 - 16:30, the second preset period is 16:30 - 17:00, and the third preset period is 17:00 - 17:30.

[0041] Specifically, the step of determining the predicted road condition index within a preset period after the current moment according to the current road condition index and the current vehicle monitoring data includes: Calculate the first predicted road condition index in the first preset period according to the current road condition index, the current vehicle monitoring data, and the first weight value corresponding to the first preset period.

[0042] Among them, the current vehicle monitoring data is the current vehicle density data. The first weight value includes: a first sub - weight value corresponding to the current road condition index and a second sub - weight value corresponding to the current vehicle density data. The sum of the first sub - weight value and the second sub - weight value is 1.

[0043] Specifically, based on the second preset formula, combined with the current road condition index and the corresponding first sub-weight value, and the current vehicle monitoring data and the corresponding second sub-weight value, the first predicted road condition index in the first preset time period is calculated. The second preset formula is: A1 = X A ×b1 + X0×b2; A1 is the first predicted road condition index, b1 is the first sub-weight value, b2 is the second sub-weight value, and X0 is the current vehicle density data.

[0044] It should be noted that in this embodiment, the first sub-weight value is set to 0.25, and the second sub-weight value is set to 0.75.

[0045] According to the current road condition index, the current vehicle monitoring data, and the second weight value set corresponding to the second preset time period, the second predicted road condition index in the second preset time period is calculated.

[0046] Among them, the second weight value includes: the third sub-weight value corresponding to the current road condition index and the fourth sub-weight value corresponding to the current vehicle density data. The sum of the third sub-weight value and the fourth sub-weight value is 1.

[0047] Specifically, based on the third preset formula, combined with the current road condition index and the corresponding third sub-weight value, and the current vehicle monitoring data and the corresponding fourth sub-weight value, the second predicted road condition index in the second preset time period is calculated. The third preset formula is: A2 = X A ×b3 + X0×b4; A2 is the second predicted road condition index, b3 is the third sub-weight value, b4 is the fourth sub-weight value, and X0 is the current vehicle density data.

[0048] It should be noted that in this embodiment, the third sub-weight value is set to 0.5, and the fourth sub-weight value is set to 0.5.

[0049] According to the current road condition index, the current vehicle monitoring data, and the third weight value set corresponding to the third preset time period, the third predicted road condition index in the third preset time period is calculated.

[0050] Among them, the third weight value includes: the fifth sub-weight value corresponding to the current road condition index and the sixth sub-weight value corresponding to the current vehicle density data. The sum of the fifth sub-weight value and the sixth sub-weight value is 1.

[0051] Specifically, based on the fourth preset formula, combined with the current road condition index and the corresponding fifth sub-weight value, and the current vehicle monitoring data and the corresponding sixth sub-weight value, the third predicted road condition index in the third preset time period is calculated. The fourth preset formula is: A3 = X A ×b5 + X0×b6; A3 is the third predicted road condition index, b5 is the fifth sub-weight value, b6 is the sixth sub-weight value, and X0 is the current vehicle density data.

[0052] It should be noted that in this embodiment, the fifth sub-weight value is set to 0.75, and the sixth sub-weight value is set to 0.25.

[0053] Specifically, the steps of obtaining the congestion prediction result according to the predicted road condition index include: Judging whether the predicted road condition index is greater than or equal to the target threshold to obtain a judgment result.

[0054] When the judgment result is yes, it is determined that the congestion prediction result is predicted congestion; when the judgment result is no, it is determined that the congestion prediction result is predicted non-congestion.

[0055] Among them, the target threshold is default set to 0.6 and can also be adjusted according to the actual situation without limitation here.

[0056] In this embodiment, the predicted non-congestion can also be further divided. For example, when the predicted road condition index is greater than or equal to 0.3 and less than 0.6, it is determined as slow traffic of the predicted road condition; when the predicted road condition index is less than 0.3, it is determined as smooth traffic of the predicted road condition.

[0057] In an alternative manner, the process of obtaining the vehicle monitoring data of a preset monitoring point at any moment includes: obtaining the monitoring image collected by the preset monitoring point at the any moment, and using the LargeKernel3D algorithm to obtain the three-dimensional detection frame of each vehicle in the monitoring image.

[0058] Among them, the monitoring image of the road corresponding to the preset monitoring point at any moment is obtained by using the camera arranged at the preset monitoring point.

[0059] The size information of the bottom detection frame of each three-dimensional detection frame is accumulated to obtain the vehicle occupancy information in the monitoring image, and according to the ratio between the vehicle occupancy information and the road occupancy information in the monitoring image, the vehicle density data of the preset monitoring point at the any moment is obtained.

[0060] Among them, the size information is: the long side or the area. Taking the long side as the size information as an example, the long side represents the length of the vehicle in the image. Obtain the long sides of the bottom detection frames of each three-dimensional detection frame and perform cumulative calculation to obtain the total length sum of the vehicles, which is the vehicle occupancy information. Obtain the road occupancy information of the road corresponding to the monitoring image (the road occupancy information is: the product of the total number of lanes of the corresponding road and the single lane), and obtain the first vehicle density data at the current moment according to the ratio between the vehicle occupancy information and the road occupancy information in the monitoring image. For example, assume that the monitoring image contains three lanes, each lane is 200m long, the length of each vehicle is 5m, and there are 30 vehicles in the monitoring image. Then the vehicle occupancy information is 5×30 = 150, the road occupancy information is 3×200 = 600, and the vehicle density data is 150 / 600 = 0.25.

[0061] The technical solution of this embodiment can predict the congestion situation of the highway on different dates by combining multi-dimensional data, and can improve the accuracy and efficiency of congestion prediction.

[0062] Figure 2 Fig. shows a schematic structural diagram of an embodiment of a highway congestion prediction system 200 provided by the present invention. As Figure 2 shown, the system 200 includes: a processing module 210 and a prediction module 220; The processing module 210 is configured to: determine a plurality of reference dates corresponding to the current date based on the date attribute of the current date; the prediction module 220 is configured to: calculate the current traffic condition index corresponding to the period to which the current moment of the current date belongs according to the reference traffic condition indexes of the preset monitoring points of the target highway in the reference periods of each reference date, and obtain a congestion prediction result according to the current traffic condition index and the current vehicle monitoring data of the preset monitoring points at the current moment; wherein, the reference period is: the same period as the period to which the current moment belongs.

[0063] In an optional manner, the date attribute is: a holiday or a non-holiday; the processing module 210 is specifically configured to: When the date attribute of the current date is a holiday, determine each holiday within the preset number of days before the current date and the day before and after each holiday as reference dates; When the date attribute of the current date is a non-holiday, determine the target dates within the preset number of days before the current date as reference dates; wherein, the target date is: the date having the same week as the current date.

[0064] In an optional manner, the prediction module 220 is specifically configured to: According to the average value of the vehicle monitoring data for each sub-period within any reference period, the reference road condition index for that reference period is obtained until the reference road condition index for each reference period is obtained; When the date attribute of the current date is a holiday, the average value of all reference road condition indexes is determined as the current road condition index; When the date attribute of the current date is not a holiday, based on the reference road condition indexes of the reference periods of a preset number of reference dates adjacent to the current date, and in combination with the average value of all reference road condition indexes, the current road condition index is determined.

[0065] In an optional manner, the prediction module 220 is specifically configured to: According to the current road condition index and the current vehicle monitoring data, determine the predicted road condition index within a preset period after the current moment, and obtain the congestion prediction result according to the predicted road condition index.

[0066] In an optional manner, the preset period includes: a first preset period composed of the current moment to the first preset moment, a second preset period composed of the first preset moment to the second preset moment, and a third preset period composed of the second preset moment to the third preset moment; the predicted road condition indexes include: a first predicted road condition index, a second predicted road condition index, and a third predicted road condition index; the prediction module 220 is specifically configured to: Calculate the first predicted road condition index within the first preset period according to the current road condition index, the current vehicle monitoring data, and the first weight value set corresponding to the first preset period; Calculate the second predicted road condition index within the second preset period according to the current road condition index, the current vehicle monitoring data, and the second weight value set corresponding to the second preset period; Calculate the third predicted road condition index within the third preset period according to the current road condition index, the current vehicle monitoring data, and the third weight value set corresponding to the third preset period.

[0067] In an optional manner, the vehicle monitoring data is vehicle density data; the system 200 further includes: an acquisition module; the acquisition module is used to: Acquire the monitoring image collected at the preset monitoring point at any moment, and use the LargeKernel3D algorithm to obtain the three-dimensional detection frame of each vehicle in the monitoring image; Accumulate the size information of the bottom detection frame of each three-dimensional detection frame to obtain the vehicle occupancy information in the monitoring image, and obtain the vehicle density data at any moment at the preset monitoring point according to the ratio between the vehicle occupancy information and the road occupancy information in the monitoring image; wherein, the size information is: the long side or the area.

[0068] It should be noted that the beneficial effects of the highway congestion prediction system provided in the above embodiments are the same as those of the highway congestion prediction method described above, and will not be elaborated here. In addition, when the system provided in the above embodiments realizes its functions, only the division of the above function modules is used for illustration. In practical applications, the above functions can be allocated to different function modules according to needs, that is, the system can be divided into different function modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0069] Among them, the highway congestion prediction system of the present invention can be a computer program (including program code) running in a computer device. For example, the highway congestion prediction system of the present invention is an application software and can be used to execute the corresponding steps in the highway congestion prediction method of the present invention.

[0070] In some embodiments, the highway congestion prediction system of the present invention can be implemented in a combination of software and hardware. As an example, the highway congestion prediction system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the highway congestion prediction method of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs, Application Specific IntegratedCircuit), DSPs, programmable logic devices (PLDs, Programmable Logic Device), complex programmable logic devices (CPLDs, Complex Programmable Logic Device), field programmable gate arrays (FPGAs, Field-Programmable Gate Array) or other electronic components.

[0071] Among them, the modules described in the embodiments of the present invention can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the module itself in some cases.

[0072] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned highway congestion prediction method is implemented. That is to say, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the highway congestion prediction method shown in any embodiment of the present invention by calling the computer program.

[0073] In an alternative embodiment, an electronic device is provided, as Figure 3 shown, Figure 3 the electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as connected through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiment of the present invention.

[0074] The processor 4001 may be a CPU (Central Processing Unit, central processing unit), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosure of the present invention. The processor 4001 may also be a combination that implements a computing function, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0075] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard structure) bus, etc. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3The bus 4002 is represented by only a thick line, but it does not mean that there is only one bus or one type of bus.

[0076] The memory 4003 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0077] The memory 4003 is used to store the application program code (computer program) for implementing the solution of the present invention and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0078] Among them, the electronic device can also be a terminal device. The terminal device can be any terminal device that can install an application and access a web page through the application, including at least one of a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle-mounted device.

[0079] It should be noted that Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0080] A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon. When the computer program is executed by a processor, it implements any one of the above highway congestion prediction methods.

[0081] Optionally, the computer-readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0082] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the above-mentioned highway congestion prediction method.

[0083] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, executed as a stand-alone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0084] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0085] The computer-readable storage medium provided by the embodiments of the present invention may be, but is not limited to, a system, device, or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0086] The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device is caused to execute the method shown in the above embodiments.

[0087] The above description is only for the preferred embodiments of the present invention and the description of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the present invention.

[0088] It should be noted that the terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and represent a limitation on a specific order or sequence. Under appropriate circumstances, the order of use of similar objects can be interchanged so that the embodiments of this application described here can be implemented in an order other than the illustrated or described order.

[0089] Those skilled in the art know that the present invention can be implemented as a system, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), or can also be a form of combination of hardware and software, which is generally referred to as "circuit", "module", or "system" in this article. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable media contains computer-readable program code.

[0090] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A highway congestion prediction method, characterized in that: include: Based on the date attribute of the current date, determining multiple reference dates corresponding to the current date; According to the reference road condition index of the preset monitoring points of the target expressway in the reference time period of each reference date, the current road condition index corresponding to the time period of the current moment of the current date is calculated, and the congestion prediction result is obtained according to the current road condition index and the current vehicle monitoring data of the preset monitoring points at the current moment; wherein the reference time period is: the same time period as the time period of the current moment.

2. The highway congestion prediction method according to claim 1, characterized in that: The date attribute is: holiday or non-holiday; based on the date attribute of the current date, the step of determining multiple reference dates corresponding to the current date includes: When the date attribute of the current date is a holiday, each holiday within a preset number of days before the current date and the day before and after each holiday are determined as reference dates; When the date attribute of the current date is non-holiday, a target date within a preset number of days before the current date is determined as a reference date; wherein the target date is: a date on the same week as the current date.

3. The highway congestion prediction method according to claim 2, characterized in that: The step of calculating the current road condition index corresponding to the time period at the current time of the current date according to the reference road condition index of the preset monitoring points of the target highway in the reference time period of each reference date includes: According to the average value of the vehicle monitoring data of each sub-period within any reference period, a reference road condition index of the reference period is obtained, until a reference road condition index of each reference period is obtained; When the date attribute of the current date is a holiday, determining an average value of all reference traffic condition indexes as the current traffic condition index; When the date attribute of the current date is non-holiday, the current traffic condition index is determined based on the reference traffic condition indexes of reference time periods of a preset number of reference dates adjacent to the current date and in combination with an average value of all reference traffic condition indexes.

4. The highway congestion prediction method according to claim 1, characterized in that: The step of obtaining a congestion prediction result according to the current road condition index and the current vehicle monitoring data of the preset monitoring point at the current moment includes: determining the predicted road condition index within a preset time period after the current moment according to the current road condition index and the current vehicle monitoring data, and obtaining the congestion prediction result according to the predicted road condition index.

5. The highway congestion prediction method according to claim 4, characterized in that: The preset time period includes: a first preset time period formed from the current moment to the first preset moment, a second preset time period formed from the first preset moment to the second preset moment, and a third preset time period formed from the second preset moment to the third preset moment; the predicted road condition index includes: a first predicted road condition index, a second predicted road condition index and a third predicted road condition index; the step of determining the predicted road condition index within the preset time period after the current moment according to the current road condition index and the current vehicle monitoring data includes: Calculate a first predicted road condition index for the first preset time period according to the current road condition index, the current vehicle monitoring data, and a first weight value set corresponding to the first preset time period; Calculate a second predicted road condition index for the second preset time period according to the current road condition index, the current vehicle monitoring data, and a second weight value set corresponding to the second preset time period; A third predicted road condition index for the third preset time period is calculated based on the current road condition index, the current vehicle monitoring data and a third weight value set corresponding to the third preset time period.

6. The highway congestion prediction method according to any one of claims 1 to 5, characterized in that: The vehicle monitoring data is vehicle density data; The process of obtaining the vehicle monitoring data of the preset monitoring point at any time includes: Obtaining a monitoring image collected by the preset monitoring point at any time, and using the LargeKernel3D algorithm to obtain a three-dimensional detection frame of each vehicle in the monitoring image; The size information of the bottom surface detection frame of each three-dimensional detection frame is accumulated to obtain the vehicle occupancy information in the monitoring image, and the vehicle density data of the preset monitoring point at any time is obtained according to the ratio between the vehicle occupancy information and the road occupancy information in the monitoring image; wherein the size information is: long side or area.

7. A highway congestion prediction system, characterized in that: include: Processing module and prediction module; The processing module is used to: determine multiple reference dates corresponding to the current date based on the date attribute of the current date; The prediction module is used to calculate the current road condition index corresponding to the time period of the current date at the current time according to the reference road condition index of the preset monitoring points of the target highway in the reference time period of each reference date, and obtain the congestion prediction result according to the current road condition index and the current vehicle monitoring data of the preset monitoring points at the current time; wherein the reference time period is the same time period as the time period of the current time.

8. The highway congestion prediction system according to claim 7, characterized in that: The date attribute is: holiday or non-holiday; the processing module is specifically used for: When the date attribute of the current date is a holiday, each holiday within a preset number of days before the current date and the day before and after each holiday are determined as reference dates; When the date attribute of the current date is non-holiday, a target date within a preset number of days before the current date is determined as a reference date; wherein the target date is: a date on the same week as the current date.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the highway congestion prediction method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the highway congestion prediction method according to any one of claims 1 to 6.

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