Expressway congestion spreading and relieving prediction method, system, equipment and medium

Abstract

The invention relates to the technical field of road condition prediction, and particularly discloses a highway congestion spreading and relieving prediction method, system and device and a medium, and the method comprises the steps: judging whether a preset monitoring point position is congested or not based on the first vehicle monitoring data of the preset monitoring point position of a target highway, and obtaining a first judgment result, based on second vehicle monitoring data of an upstream monitoring point position of the preset monitoring point position, whether the upstream monitoring point position is congested or not is judged, and a second judgment result is obtained; when the first judgment result is yes and the second judgment result is no, the congestion spreading condition of the upstream monitoring point position is predicted; or, when the first judgment result is no and the second judgment result is yes, the congestion relieving condition of the upstream monitoring point position is predicted. According to the method, the highway congestion spreading and relieving condition is determined by combining the accurately obtained vehicle monitoring information between different monitoring points, and the accuracy and efficiency of congestion spreading and relieving prediction can be improved.

Description

Background Art

[0002] Existing methods for predicting road congestion spread and mitigation have the following problems: 1) Lack of real-time performance and accuracy. The feature model lags relatively behind the road changes, the data source is single (mostly from map navigation), it is impossible to obtain comprehensive vehicle information, and there is a lack of congestion analysis ability for long-distance road sections. 2) Lack of real-time data support, and no more scene-fitting prediction is carried out for the relatively closed characteristics of highways.

[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 method, a system, a device and a medium for predicting highway congestion spread and mitigation.

[0005] In the first aspect, the present invention provides a method for predicting highway congestion spread and mitigation. The technical solution of this method is as follows: Based on the first vehicle monitoring data of the preset monitoring points of the target highway, determine whether congestion occurs at the preset monitoring points to obtain a first judgment result, and based on the second vehicle monitoring data of the upstream monitoring points of the preset monitoring points, determine whether congestion occurs at the upstream monitoring points to obtain a second judgment result; When the first judgment result is yes and the second judgment result is no, predict the congestion spread situation of the upstream monitoring points; or, when the first judgment result is no and the second judgment result is yes, predict the congestion mitigation situation of the upstream monitoring points.

[0006] The beneficial effects of a method for predicting highway congestion spread and mitigation of the present invention are as follows: The method of the present invention determines the highway congestion spread and mitigation situation by combining the vehicle monitoring information between different accurately obtained monitoring points, and can improve the accuracy and efficiency of congestion spread and mitigation prediction.

[0007] On the basis of the above solution, a method for predicting highway congestion spread and mitigation of the present invention can also be improved as follows.

[0008] In an optional manner, the first vehicle monitoring data includes: the first vehicle flow data of the current time period and the first vehicle density data of the current moment, and the second vehicle monitoring data includes: the second vehicle flow data of the current time period and the second vehicle density data of the current moment; wherein, the current moment is the end moment of the current time period, and the duration of the current time period is a preset duration; the step of determining whether congestion occurs at the preset monitoring points based on the first vehicle monitoring data of the preset monitoring points of the target highway includes: When the first vehicle density data is greater than or equal to the first threshold, it is determined that congestion occurs at the preset monitoring point; When the first vehicle density data is less than the first threshold, it is determined that no congestion occurs at the preset monitoring point.

[0009] In an alternative manner, the congestion spread situation is: the first prediction moment when congestion spreads; the steps for predicting the congestion spread situation of the upstream monitoring point include: According to the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, calculate the congestion spread prediction duration of the upstream monitoring point; Taking the current moment as the starting moment, and combining with the congestion spread prediction duration, determine the first prediction moment when congestion spreads at the upstream monitoring point.

[0010] In an alternative manner, the congestion alleviation situation is: the second prediction moment when congestion is alleviated; the steps for predicting the congestion alleviation situation of the upstream monitoring point include: According to the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, calculate the congestion alleviation prediction duration of the upstream monitoring point; Taking the current moment as the starting moment, and combining with the congestion alleviation prediction duration, determine the second prediction moment when congestion is alleviated at the upstream monitoring point.

[0011] In an alternative manner, it further includes: Obtain the monitoring image of the road corresponding to the preset monitoring point at the current moment, and use the LargeKernel3D algorithm to obtain the three-dimensional detection box of each vehicle in the monitoring image; Accumulate the size information of the bottom detection box of each three-dimensional detection box to obtain the vehicle occupancy space; wherein, the size information is: the long side or the area.

[0012] In a second aspect, the present invention provides a highway congestion spread and alleviation prediction system, and the technical solution of the system is as follows: It includes: a judgment module and a prediction module; The judgment module is used to: based on the first vehicle monitoring data of the preset monitoring points on the target highway, judge whether congestion occurs at the preset monitoring points to obtain a first judgment result, and based on the second vehicle monitoring data of the upstream monitoring points of the preset monitoring points, judge whether congestion occurs at the upstream monitoring points to obtain a second judgment result; The prediction module is used to: when the first judgment result is yes and the second judgment result is no, predict the congestion spread situation of the upstream monitoring points; or, when the first judgment result is no and the second judgment result is yes, predict the congestion mitigation situation of the upstream monitoring points.

[0013] The beneficial effects of a highway congestion spread and mitigation prediction system of the present invention are as follows: The system of the present invention determines the highway congestion spread and mitigation situation by combining the accurately obtained vehicle monitoring information between different monitoring points, and can improve the accuracy and efficiency of congestion spread and mitigation prediction.

[0014] On the basis of the above solution, a highway congestion spread and mitigation prediction system of the present invention can also be improved as follows.

[0015] In an optional manner, the first vehicle monitoring data includes: the first vehicle flow data in the current period and the first vehicle density data at the current moment, and the second vehicle monitoring data includes: the second vehicle flow data in the current period and the second vehicle density data at the current moment; wherein, the current moment is the end moment of the current period, and the duration of the current period is a preset duration; the judgment module is specifically used to: When the first vehicle density data is greater than or equal to a first threshold, it is determined that congestion occurs at the preset monitoring point; When the first vehicle density data is less than the first threshold, it is determined that no congestion occurs at the preset monitoring point.

[0016] In an optional manner, the congestion spread situation is: the first prediction moment when congestion spread occurs; the prediction module is specifically used to: According to the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, calculate the congestion spread prediction duration of the upstream monitoring point; Take the current moment as the starting moment, and combine the congestion spread prediction duration to determine the first prediction moment when congestion spread occurs at the upstream monitoring point.

[0017] In a third aspect, a technical solution of an electronic device according to 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 method for predicting highway congestion spread and mitigation according to 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 method for predicting highway congestion spread and mitigation according to 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 specifically exemplified below. BRIEF 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 is a schematic flowchart of an embodiment of a method for predicting highway congestion spread and mitigation according to the present invention; Figure 2 is a schematic structural diagram of an embodiment of a system for predicting highway congestion spread and mitigation according to the present invention; Figure 3 is a schematic structural diagram of an embodiment of an electronic device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Hereinafter, 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 set forth herein.

[0022] Figure 1A flow chart of an embodiment of a method for predicting the spread and relief of highway congestion provided by the present invention is shown. The method for predicting the spread and relief of highway congestion can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the method for predicting the spread and relief of highway congestion by calling computer-readable instructions stored in a memory through a processor. Figure 1 As shown, the following steps are included: S1. Based on the first vehicle monitoring data of a preset monitoring point of the target highway, determine whether congestion occurs at the preset monitoring point to obtain a first judgment result, and based on the second vehicle monitoring data of an upstream monitoring point of the preset monitoring point, determine whether congestion occurs at the upstream monitoring point to obtain a second judgment result.

[0023] Among them, the target highway is an arbitrarily selected highway in this embodiment. A plurality of monitoring points are set on the target highway, and a camera is 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 may be overlapping areas, but there cannot be interruptions), that is, the target highway is monitored without blind spots. For example, assuming that the shooting radius of the camera at each monitoring point is 500m, then in theory, a camera is set every 1km. In the actual layout process, fine-tuning can be performed according to the actual conditions of the highway (slope, curve, tunnel, etc.) to ensure the target highway is monitored without blind spots.

[0024] Among them, the first vehicle monitoring data includes but is not limited to: the first vehicle flow data of the current time period and the first vehicle density data at the current moment. The second vehicle monitoring data includes but is not limited to: the second vehicle flow data of the current time period and the second vehicle density data at the current moment. The current moment is the end moment of the current time period, and the duration of the current time period is the preset duration. The preset duration defaults to 1min, and can also be set according to actual conditions. There is no limit here. For example, if the current moment is 12:00:00, then the current time period is 11:59:00-12:00:00. Vehicle flow data refers to the number of vehicles passing through the corresponding position of the corresponding monitoring point within a certain period of time. Vehicle density data refers to the proportion of vehicles on the road covered by the corresponding monitoring point at a certain moment (the ratio of vehicle length to total road length, or the ratio of vehicle area to total road area).

[0025] Taking the preset monitoring point as an example, the steps to obtain the first vehicle monitoring data of the preset monitoring point of the target highway include: ① Obtain the first monitoring video data of the preset monitoring point of the target highway in the current period. Use the yolov8 object detection algorithm and the ByteTracker multi-object tracking algorithm to identify the driving trajectories of each vehicle from the first monitoring video data, determine whether the driving trajectory of each vehicle exceeds the corresponding position of the preset monitoring point, and count the vehicles whose driving trajectories exceed the corresponding position of the preset monitoring point to obtain the first vehicle flow data in the current period.

[0026] ② Obtain the first monitoring image data of the preset monitoring point of the target highway at the current moment. Use the LargeKernel3D algorithm to obtain the three-dimensional detection frames of each vehicle in the first monitoring image data; accumulate the size information of the bottom detection frames of each three-dimensional detection frame to obtain the vehicle occupancy space in the road corresponding to the first monitoring image data; where the size information is: the long side or the area.

[0027] ③ Obtain the total size information of the road corresponding to the first monitoring image data (the total size information is the product of the total number of lanes of the corresponding road and the single lane, or the total area of the corresponding road), and obtain the first vehicle density data at the current moment according to the ratio of the vehicle occupancy space in the road corresponding to the first monitoring image data to the total size information of the road corresponding to the first monitoring image data.

[0028] It should be noted that the process of obtaining the second vehicle monitoring data of the upstream monitoring point of the target highway is the same as the process of obtaining the first vehicle monitoring data, and will not be elaborated here.

[0029] S2. When the first judgment result is yes and the second judgment result is no, predict the congestion spread situation of the upstream monitoring point; or, when the first judgment result is no and the second judgment result is yes, predict the congestion alleviation situation of the upstream monitoring point.

[0030] Among them, the congestion spread situation is defaulted to: the first prediction moment when congestion spread occurs. The congestion alleviation situation is defaulted to: the second prediction moment when congestion alleviation occurs.

[0031] In an optional manner, the steps to judge whether the preset monitoring point is congested based on the first vehicle monitoring data of the preset monitoring point of the target highway include: When the first vehicle density data is greater than or equal to the first threshold, it is determined that the preset monitoring point is congested; when the first vehicle density data is less than the first threshold, it is determined that the preset monitoring point is not congested.

[0032] Among them, the first threshold is defaulted to 0.6, and it can also be adjusted according to the actual situation without any restrictions here. When the vehicle density data is greater than or equal to 0.6, it is determined that congestion occurs; when the vehicle density data is less than 0.6, it is determined that congestion does not occur. In this embodiment, the situation where congestion does not occur can also be further divided. For example, when the vehicle density data is greater than or equal to 0.3 and less than 0.6, it is determined as slow traffic; when the vehicle density data is less than 0.3, it is determined as smooth traffic.

[0033] In an alternative manner, the step of predicting the congestion spread situation of the upstream monitoring point includes: calculating the congestion spread prediction duration of the upstream monitoring point based on the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment.

[0034] Specifically, based on the first preset formula, and according to the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, the congestion spread prediction duration of the upstream monitoring point is calculated.

[0035] Among them, the first preset formula is: T1 = (X AB ×N) / (Q B -Q A ) / S; T1 is the congestion spread prediction duration, X AB is the distance value between the preset monitoring point and the upstream monitoring point, N is the number of target lanes, Q A is the first vehicle flow data, Q B is the second vehicle flow data, and S is the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment.

[0036] Taking the current moment as the starting moment, and combining with the congestion spread prediction duration, the first prediction moment when congestion spreads at the upstream monitoring point is determined.

[0037] Among them, assuming that the current moment is 12:00:00 and the congestion spread prediction duration is 1 minute, then the first prediction moment when congestion spreads at the upstream monitoring point is 12:01:00.

[0038] In an alternative manner, the step of predicting the congestion alleviation situation at the upstream monitoring point includes: calculating the congestion alleviation prediction duration of the upstream monitoring point based on the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment.

[0039] Specifically, based on the second preset formula, and according to the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, calculate the congestion alleviation prediction duration of the upstream monitoring point.

[0040] Wherein, the second preset formula is: T2 = (X AB × N) / (Q A - Q B ) / S; T2 is the congestion alleviation prediction duration, X AB is the distance value between the preset monitoring point and the upstream monitoring point, N is the number of target lanes, Q A is the first vehicle flow data, Q B is the second vehicle flow data, and S is the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment.

[0041] Taking the current moment as the starting moment, and combining with the congestion alleviation prediction duration, determine the second prediction moment when congestion alleviation occurs at the upstream monitoring point.

[0042] Wherein, assuming the current moment is 12:00:00 and the congestion alleviation prediction duration is 2 min, the second prediction moment when congestion alleviation occurs at the upstream monitoring point is 12:02:00.

[0043] In an alternative manner, it further includes: Obtaining the monitoring image of the road corresponding to the preset monitoring point at the current moment, and using the LargeKernel3D algorithm to obtain the three-dimensional detection frame of each vehicle in the monitoring image.

[0044] Wherein, using the camera arranged at the preset monitoring point to obtain the monitoring image of the road corresponding to the preset monitoring point at the current moment.

[0045] Accumulating the size information of the bottom detection frame of each three-dimensional detection frame to obtain the vehicle occupancy space.

[0046] 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. The long sides of the bottom detection frames of each three-dimensional detection frame are obtained and accumulated to calculate the total length of the vehicle, which is the occupied space of the vehicle.

[0047] The technical solution of this embodiment can determine the congestion spread and alleviation of the highway by combining the vehicle monitoring information accurately obtained between different monitoring points, and can improve the accuracy and efficiency of congestion spread and alleviation prediction.

[0048] Figure 2 The structural schematic diagram of an embodiment of a highway congestion spread and alleviation prediction system 200 provided by the present invention is shown. As Figure 2 shown, the system 200 includes: a judgment module 210 and a prediction module 220; The judgment module 210 is used for: based on the first vehicle monitoring data of the preset monitoring points of the target highway, judging whether congestion occurs at the preset monitoring points to obtain a first judgment result, and based on the second vehicle monitoring data of the upstream monitoring points of the preset monitoring points, judging whether congestion occurs at the upstream monitoring points to obtain a second judgment result; The prediction module 220 is used for: when the first judgment result is yes and the second judgment result is no, predicting the congestion spread situation of the upstream monitoring points; or, when the first judgment result is no and the second judgment result is yes, predicting the congestion alleviation situation of the upstream monitoring points.

[0049] In an optional manner, the first vehicle monitoring data includes: the first vehicle flow data in the current period and the first vehicle density data at the current moment, and the second vehicle monitoring data includes: the second vehicle flow data in the current period and the second vehicle density data at the current moment; wherein, the current moment is the end moment of the current period, and the duration of the current period is a preset duration; the judgment module 210 is specifically used for: When the first vehicle density data is greater than or equal to the first threshold, it is determined that congestion occurs at the preset monitoring points; When the first vehicle density data is less than the first threshold, it is determined that no congestion occurs at the preset monitoring points.

[0050] In an optional manner, the congestion spread situation is: the first prediction moment when congestion spread occurs; the prediction module 220 is specifically used for: Calculate the congestion spread prediction duration of the upstream monitoring point based on the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment; Use the current moment as the starting moment, and in combination with the congestion spread prediction duration, determine the first prediction moment when congestion spread occurs at the upstream monitoring point.

[0051] In an alternative manner, the congestion mitigation situation is: the second prediction moment when congestion mitigation occurs; the prediction module 220 is specifically configured to: Calculate the congestion mitigation prediction duration of the upstream monitoring point based on the distance value between the preset monitoring point and the upstream monitoring point, the number of target lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment; Use the current moment as the starting moment, and in combination with the congestion mitigation prediction duration, determine the second prediction moment when congestion mitigation occurs at the upstream monitoring point.

[0052] In an alternative manner, it further includes: an acquisition module; the acquisition module is used to: Acquire the monitoring image of the road corresponding to the preset monitoring point at the current moment, and use the LargeKernel3D algorithm to acquire 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 space; where the size information is: the long side or the area.

[0053] It should be noted that the beneficial effects of the highway congestion spread and mitigation prediction system provided in the above embodiments are the same as those of the highway congestion spread and mitigation 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 functional modules is used as an example. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the system can be divided into different functional 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, and will not be elaborated here.

[0054] Among them, the highway congestion spread and mitigation prediction system of the present invention can be a computer program (including program code) running in a computer device. For example, the highway congestion spread and mitigation prediction system of the present invention is an application software and can be used to execute the corresponding steps in the highway congestion spread and mitigation prediction method of the present invention.

[0055] In some embodiments, the highway congestion spread and mitigation prediction system of the present invention can be implemented in a combination of software and hardware. As an example, the highway congestion spread and mitigation 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 spread and mitigation prediction method of the present invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0056] Among them, the modules involved 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.

[0057] 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, it implements any one of the above-mentioned highway congestion spread and mitigation prediction methods. 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 spread and mitigation prediction method shown in any embodiment of the present invention by calling the computer program.

[0058] In an alternative embodiment, an electronic device is provided, as Figure 3 shown Figure 3The 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 being connected through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between this 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 embodiments of the present invention.

[0059] The processor 4001 can be a CPU (Central Processing Unit, central processor), 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 can also be a combination that implements computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0060] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard architecture) bus, etc. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent the bus 4002 in the figure, but it does not mean that there is only one bus or one type of bus.

[0061] 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.

[0062] The memory 4003 is used to store the application program code (computer program) for executing 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.

[0063] 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 device.

[0064] It should be noted that Figure 3 The illustrated electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0065] 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-mentioned highway congestion spread and mitigation prediction methods.

[0066] 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.

[0067] 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 spread and mitigation prediction method.

[0068] 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 the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the 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).

[0069] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the 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 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.

[0070] 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.

[0071] 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.

[0072] The above description is only a preferred embodiment of the present invention and an explanation 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.

[0073] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, and represent a limitation on a specific order or sequence. The order of use of similar objects may be interchanged appropriately so that the embodiments of this application described here can be implemented in an order other than the order shown or described.

[0074] 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 be completely software (including firmware, resident software, microcode, etc.), or can also be in the form of a 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.

[0075] Although the embodiments of the present invention have been shown and described above, it is to 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 method for predicting the spread and relief of highway congestion, characterized in that: include: Based on the first vehicle monitoring data of a preset monitoring point of the target highway, it is determined whether the preset monitoring point is congested to obtain a first determination result, and based on the second vehicle monitoring data of an upstream monitoring point of the preset monitoring point, it is determined whether the upstream monitoring point is congested to obtain a second determination result; When the first judgment result is yes and the second judgment result is no, predicting the congestion spread of the upstream monitoring point; Or, when the first judgment result is no and the second judgment result is yes, the congestion relief situation of the upstream monitoring point is predicted.

2. The method for predicting the spread and relief of highway congestion according to claim 1, characterized in that: The first vehicle monitoring data includes: first vehicle flow data of the current time period and first vehicle density data at the current moment, and the second vehicle monitoring data includes: second vehicle flow data of the current time period and second vehicle density data at the current moment; wherein the current moment is the end moment of the current time period, and the duration of the current time period is a preset duration; based on the first vehicle monitoring data of the preset monitoring point of the target highway, the step of determining whether congestion occurs at the preset monitoring point includes: When the first vehicle density data is greater than or equal to a first threshold, it is determined that congestion occurs at the preset monitoring point; When the first vehicle density data is less than the first threshold, it is determined that no congestion occurs at the preset monitoring point.

3. The method for predicting the spread and relief of highway congestion according to claim 2, characterized in that: The congestion spreading condition is: the first predicted moment of congestion spreading; the step of predicting the congestion spreading condition of the upstream monitoring point comprises: The predicted duration of congestion spread at the upstream monitoring point is calculated according to the distance between the preset monitoring point and the upstream monitoring point, the target number of lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupation space of the road corresponding to the preset monitoring point at the current moment; The current time is taken as the starting time, and combined with the predicted duration of congestion spread, the first predicted time when congestion spread occurs at the upstream monitoring point is determined.

4. The method for predicting the spread and relief of highway congestion according to claim 2, characterized in that: The congestion relief situation is: the second predicted time when congestion relief occurs; the step of predicting the congestion relief situation of the upstream monitoring point includes: According to the distance value between the preset monitoring point and the upstream monitoring point, the target number of lanes of the target expressway, the first vehicle flow data, the second vehicle flow data and the vehicle occupancy space of the road corresponding to the preset monitoring point at the current moment, the predicted congestion relief duration of the upstream monitoring point is calculated; taking the current moment as the starting moment, and combining with the predicted congestion relief duration, the second predicted moment when congestion relief occurs at the upstream monitoring point is determined.

5. The method for predicting the spread and relief of highway congestion according to claim 3 or 4, characterized in that: Also includes: Obtaining a monitoring image of the road corresponding to the preset monitoring point at the current moment, 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 space occupied by the vehicle; wherein the size information is: the long side or the area.

6. A highway congestion spread and relief prediction system, characterized in that: include: Judgment module and prediction module; The judgment module is used to: judge whether congestion occurs at a preset monitoring point on a target highway based on first vehicle monitoring data of the preset monitoring point, and obtain a first judgment result; and judge whether congestion occurs at an upstream monitoring point of the preset monitoring point based on second vehicle monitoring data of the upstream monitoring point, and obtain a second judgment result; The prediction module is used to: when the first judgment result is yes and the second judgment result is no, predict the congestion spread of the upstream monitoring point; Or, when the first judgment result is no and the second judgment result is yes, the congestion relief situation of the upstream monitoring point is predicted.

7. The highway congestion spread and relief prediction system according to claim 6, characterized in that: The first vehicle monitoring data includes: first vehicle flow data of the current time period and first vehicle density data of the current moment; the second vehicle monitoring data includes: second vehicle flow data of the current time period and second vehicle density data of the current moment; wherein the current moment is the end moment of the current time period, and the duration of the current time period is a preset duration; the judgment module is specifically used for: When the first vehicle density data is greater than or equal to a first threshold, it is determined that congestion occurs at the preset monitoring point; When the first vehicle density data is less than the first threshold, it is determined that no congestion occurs at the preset monitoring point.

8. The highway congestion spread and relief prediction system according to claim 7, characterized in that: The congestion spreading situation is: the first predicted moment of congestion spreading; the prediction module is specifically used for: The predicted duration of congestion spread at the upstream monitoring point is calculated according to the distance between the preset monitoring point and the upstream monitoring point, the target number of lanes of the target highway, the first vehicle flow data, the second vehicle flow data, and the vehicle occupation space of the road corresponding to the preset monitoring point at the current moment; The current time is taken as the starting time, and combined with the predicted duration of congestion spread, the first predicted time when congestion spread occurs at the upstream monitoring point is determined.

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 spread and relief prediction method as described in any one of claims 1 to 5.

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 spread and relief prediction method as described in any one of claims 1 to 5.

Images