Toll station lane management and control method and device, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of intelligent traffic, and provides a toll station lane management and control method and device, terminal equipment and a storage medium, and the method comprises the steps: firstly obtaining vehicle track data in at least one upstream sensing area corresponding to a downstream toll station; then, based on the vehicle track data in all the upstream sensing areas, the queuing condition of the downstream toll station is predicted, and a real-time congestion prediction result of the downstream toll station is generated; and finally, according to a real-time congestion prediction result, dynamically managing and controlling lanes of a downstream toll station. Therefore, real-time congestion prediction is performed by using the upstream vehicle trajectory data, and traffic flow changes are coped with by dynamically scheduling the lanes of the downstream toll station, so that flexible management and control of the lanes of the toll station are realized, and the overall traffic efficiency is improved.

Description

Technical Field

[0001] This application belongs to the field of intelligent transportation technology, and particularly relates to a toll station lane control method, device, terminal device, and storage medium. Background Art

[0002] With the continuous increase in the number of automobiles, the traffic flow on expressways is growing day by day, and the imbalance between travel demand and road supply gradually appears. Especially during holidays and morning and evening rush hours, traffic congestion problems at some sections and toll stations are becoming increasingly prominent, bringing inconvenience to travelers.

[0003] In related technologies, when the number of open toll lanes at a toll station is small, congestion is likely to occur; if the number of open lanes is too large, there may be a problem of resource waste. Therefore, how to achieve flexible control of toll station lanes has become an urgent problem to be solved. Summary of the Invention

[0004] The embodiments of this application provide a toll station lane control method, device, terminal device, and storage medium, which can achieve flexible control of toll station lanes.

[0005] In a first aspect of the embodiments of this application, a toll station lane control method is provided, including: obtaining vehicle trajectory data in at least one upstream sensing area corresponding to a downstream toll station; predicting the queuing situation of the downstream toll station based on the vehicle trajectory data in all upstream sensing areas, and generating a real-time congestion prediction result of the downstream toll station; and dynamically controlling the lanes of the downstream toll station according to the real-time congestion prediction result.

[0006] Optionally, in another possible implementation manner of the first aspect, the above real-time congestion prediction result includes a congestion ratio. Dynamically controlling the lanes of the downstream toll station according to the real-time congestion prediction result includes:

[0007] When the congestion ratio is in an upward state, increasing the number of open lanes of the downstream toll station according to the rising amplitude of the congestion ratio;

[0008] When the congestion ratio is in a downward state, reducing the number of open lanes of the downstream toll station according to the decreasing amplitude of the congestion ratio.

[0009] Optionally, in yet another possible implementation manner of the first aspect, the lanes of the downstream toll station include tidal lanes, and the real-time congestion prediction result includes the congestion ratios corresponding to the two directions of the tidal lanes respectively. Dynamically controlling the lanes of the downstream toll station according to the real-time congestion prediction result includes:

[0010] When the congestion ratio in any direction of the tidal lane exceeds a preset threshold, changing the tidal lane to a one-way traffic lane in any direction.

[0011] Optionally, in another possible implementation manner of the first aspect, the lanes of the downstream toll station include target lanes, and each target lane corresponds to at least one target vehicle type. The toll station lane control method further includes:

[0012] Determine the vehicle trajectory data of vehicles of the target vehicle type in the upstream sensing area;

[0013] Dynamically control the target lanes of the downstream toll station according to the vehicle trajectory data of the target vehicle type.

[0014] Optionally, in another possible implementation manner of the first aspect, predicting the queuing situation of the downstream toll station based on the vehicle trajectory data of all upstream sensing areas, and generating a real-time congestion prediction result of the downstream toll station, includes:

[0015] Input the vehicle trajectory data of all upstream sensing areas into a preset congestion prediction model, and the preset congestion prediction model outputs a real-time congestion prediction result.

[0016] Optionally, in another possible implementation manner of the first aspect, the preset congestion prediction model is trained and generated through the following steps:

[0017] Obtain multiple groups of reference vehicle trajectory data;

[0018] Use multiple groups of reference vehicle trajectory data to perform spatio-temporal feature training on an initial long short-term memory neural network model to generate a preset congestion prediction model.

[0019] Optionally, in another possible implementation manner of the first aspect, predicting the queuing situation of the downstream toll station based on the vehicle trajectory data of all upstream sensing areas, and generating a real-time congestion prediction result of the downstream toll station, includes:

[0020] Obtain the transaction information of each lane of the downstream toll station;

[0021] Determine the flow imbalance coefficient according to the transaction information of each lane of the downstream toll station;

[0022] Predict the queuing situation of the downstream toll station according to the vehicle trajectory data of all upstream sensing areas and the flow imbalance coefficient, and generate a real-time congestion prediction result of the downstream toll station.

[0023] Optionally, in another possible implementation manner of the first aspect, the vehicle trajectory data includes: vehicle position, vehicle speed, vehicle density, traffic flow, driving direction, vehicle type.

[0024] A second aspect of the embodiments of the present application provides a toll station lane control device, including:

[0025] A data acquisition module, configured to acquire vehicle trajectory data within at least one upstream sensing area corresponding to a downstream toll station;

[0026] A congestion prediction module, configured to predict the queuing situation of a downstream toll station based on the vehicle trajectory data within all upstream sensing areas, and generate a real-time congestion prediction result for the downstream toll station;

[0027] A lane control module, configured to dynamically control the lanes of the downstream toll station according to the real-time congestion prediction result.

[0028] A third aspect of the embodiments of the present application provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the toll station lane control method of the first aspect is implemented.

[0029] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the toll station lane control method of the first aspect is implemented.

[0030] A fifth aspect of the embodiments of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is enabled to execute the toll station lane control method of the first aspect.

[0031] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: The embodiments of the present application disclose a toll station lane control method, device, terminal device, and storage medium. Among them, the method first acquires vehicle trajectory data within at least one upstream sensing area corresponding to a downstream toll station; then predicts the queuing situation of the downstream toll station based on the vehicle trajectory data within all upstream sensing areas, and generates a real-time congestion prediction result for the downstream toll station; finally, dynamically controls the lanes of the downstream toll station according to the real-time congestion prediction result. Thus, real-time congestion prediction is performed using upstream vehicle trajectory data, and traffic flow changes are responded to by dynamically scheduling the lanes of the downstream toll station, realizing flexible control of the toll station lanes and improving the overall traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a flowchart showing a toll station lane control method provided by an embodiment of the present application;

[0034] Figure 2 It is a schematic structural diagram of a toll station lane control device provided by an embodiment of the present application;

[0035] Figure 3 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. Detailed implementation manners

[0036] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0037] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0038] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0039] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.

[0040] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0041] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0042] It should be understood that the magnitudes of the sequence numbers of the steps in this embodiment do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0043] In the related art, when the number of open toll lanes at a toll station is small, congestion is likely to occur; while if the number of open lanes is too large, there may be a problem of resource waste. Therefore, how to achieve flexible control of toll lanes at a toll station has become an urgent problem to be solved.

[0044] In view of this, the embodiments of this application provide a toll lane control method, device, terminal device, and storage medium. By using upstream vehicle trajectory data for real-time congestion prediction and dynamically scheduling the lanes of downstream toll stations to cope with traffic flow changes, flexible control of toll lanes at a toll station is achieved, and the overall traffic efficiency is improved.

[0045] In order to illustrate the technical solution of this application, specific embodiments will be used to illustrate below.

[0046] Referring to Figure 1 , a flowchart showing a toll lane control method provided by an embodiment of this application is shown. As Figure 1 shown, the toll lane control method may include the following steps:

[0047] Step 101, obtain vehicle trajectory data in at least one upstream sensing area corresponding to a downstream toll station.

[0048] It should be noted that upstream and downstream are usually used to describe the relative position or direction in a flow system. Upstream refers to a position farther from a certain point or location, usually the area on the road before a certain traffic event or location. Upstream is usually the starting point or source of the vehicle or information flow. In the embodiments of the present application, the upstream sensing area can be the sensing area corresponding to the sensing device at a certain distance (such as 5 km, 10 km, etc.) from the toll station. Downstream refers to a position closer to a certain point or location, usually the area on the road after a certain traffic event or location. Downstream is the end point or destination of the vehicle or information flow. In the embodiments of the present application, downstream is relative to the upstream sensing area, that is, the vehicle passing through the upstream sensing area will subsequently drive into the corresponding downstream toll station.

[0049] In a possible implementation manner, vehicle trajectory data can be collected by an ETC (Electronic Toll Collection) gantry. The ETC gantry can identify the passing vehicles through wireless communication technologies such as radio frequency identification or microwave identification. In this scenario, each vehicle is equipped with a corresponding ETC tag or device for identification when passing through the gantry.

[0050] In the embodiments of the present application, the vehicle trajectory data may include vehicle position, vehicle speed, vehicle density, traffic flow, driving direction, vehicle type, etc. Among them, the vehicle position is used to record the specific position of the vehicle on the road in real time to track its movement trajectory; the vehicle speed includes the speed information of the vehicle at different time points, which helps to understand the changes and trends of the traffic flow; the vehicle density is the density of vehicles in a certain area, that is, the number of vehicles per unit area, which is used to evaluate the traffic flow; the traffic flow can be obtained by counting the number of vehicles passing through a certain point or area, which is used to quantify the intensity of the traffic flow; the driving direction helps to analyze the flow direction of the traffic flow; the vehicle type is used to distinguish different types of vehicles, such as small vehicles, large vehicles, etc., to consider the impact of different vehicle types on the traffic flow.

[0051] Furthermore, the vehicle trajectory data may also include timestamp information, by recording the timestamp of data collection to support time-related analysis and prediction.

[0052] Step 102, based on the vehicle trajectory data in all upstream sensing areas, predict the queuing situation of the downstream toll station, and generate a real-time congestion prediction result of the downstream toll station.

[0053] Among them, the queuing situation of each lane of the downstream toll station can be predicted based on all the collected vehicle trajectory data in the upstream sensing areas.

[0054] In a possible implementation, vehicle trajectory data within all upstream perception areas can be input into a preset congestion prediction model, and the preset congestion prediction model is utilized to output real-time congestion prediction results. The preset congestion prediction model has learned the changing trend of traffic flow dynamics and can accurately obtain the real-time congestion prediction results of the downstream toll station based on the input upstream vehicle trajectory data.

[0055] Furthermore, the preset congestion prediction model can be a Long Short-Term Memory (LSTM) neural network model. Among them, the long short-term memory neural network is a variant of the recurrent neural network, specifically designed to process and learn time series data, and performs well in dealing with long-term dependence problems, making it suitable for time series applications such as traffic flow prediction. That is, as a possible implementation of the embodiments of the present application, the above-mentioned preset congestion prediction model can be generated through the following steps: obtaining multiple groups of reference vehicle trajectory data; using the multiple groups of reference vehicle trajectory data to perform spatio-temporal feature training on the initial long short-term memory neural network model to generate the preset congestion prediction model.

[0056] Specifically, by training and learning the spatio-temporal features in the input data, the congestion prediction model can understand and predict the traffic states at different locations and time points. The consideration of spatio-temporal features enables the model to more accurately reflect the changing trends of road section flow and speed.

[0057] In the actual application process, through the preset congestion prediction model, prediction results such as road section flow, speed, and toll station queuing conditions within a future period of time (such as the next 5 to 15 minutes) can be output.

[0058] In addition, in order to achieve dynamic update of the traffic state, the congestion prediction model can also adopt a rolling optimization method. That is to say, at each moment, the congestion prediction model not only uses the data at the current moment for prediction, but also continuously updates and optimizes the prediction results over time to adapt to the changes in the actual traffic conditions.

[0059] In a possible implementation, since there may also be uneven traffic flow distribution among different lanes of a toll station, during the process of dynamically controlling the lanes of the downstream toll station, it is also possible to obtain the transaction information of each lane of the toll station, such as toll collection records and lane numbers. By analyzing the transaction information of each lane of the toll station, the traffic flow imbalance coefficient between different lanes can be obtained. Among them, the traffic flow imbalance coefficient reflects the traffic flow difference between lanes, that is, which lanes are relatively congested and which are relatively idle. That is to say, in step 102, the transaction information of each lane of the downstream toll station can be obtained; then, based on the transaction information of each lane of the downstream toll station, the traffic flow imbalance coefficient can be determined; finally, based on the vehicle trajectory data and the traffic flow imbalance coefficient within all upstream sensing areas, the queuing situation of the downstream toll station can be predicted to generate a real-time congestion prediction result for the downstream toll station. Thus, by considering the traffic flow imbalance coefficient during the congestion analysis process, the accuracy of the real-time congestion prediction result can be further improved.

[0060] Step 103, perform dynamic control on the lanes of the downstream toll station according to the real-time congestion prediction result.

[0061] It should be noted that the real-time congestion prediction result can be quantified by the congestion ratio. Among them, the congestion ratio is an indicator used to measure the degree of traffic congestion, which can be obtained by calculating the ratio of the total vehicle length of the vehicles in the area to the total length of the area, or by calculating the ratio of the area of the vehicles in the area to the total area of the area.

[0062] In a possible implementation, when the congestion ratio is in an upward state, the number of open lanes of the downstream toll station can be increased according to the rising amplitude of the congestion ratio; when the congestion ratio is in a downward state, the number of open lanes of the downstream toll station can be reduced according to the falling amplitude of the congestion ratio. Thus, the number of open lanes for toll vehicles is flexibly adjusted according to the lane traffic conditions. When the prediction result indicates that the vehicle queuing situation is relatively serious, the number of toll lanes is increased, so that the traffic flow during peak hours is relatively stable. When the queuing situation slows down, the number of toll lanes is flexibly reduced to avoid waste of resources. In this way, flexible control of the toll lanes is achieved.

[0063] In the embodiments of the present application, the lanes of the downstream toll station include tidal lanes. A tidal lane is a lane whose vehicle driving direction can be changed according to the traffic flow demand. When the congestion ratio in a certain direction of the tidal lane exceeds the threshold, several oncoming lanes can be dynamically changed to the current direction lanes to increase the one-way traffic capacity. That is to say, when the congestion ratio in any direction of the tidal lane exceeds the preset limit threshold, the tidal lane can be changed to a one-way traffic lane in any direction. It should be understood that the specific value of the preset limit threshold can be determined in combination with the actual application scenario and requirements, and the embodiments of the present application do not limit this.

[0064] In a possible implementation manner of the embodiments of the present application, toll stations usually also set up special lanes such as green channel lanes. Compared with ordinary lanes, the number of vehicles in special lanes is relatively small because only specific vehicle types or personnel are eligible to use them. This characteristic means that special lanes such as green channels will not cause large-scale congestion problems to toll stations, and special vehicles need to be controlled separately.

[0065] As an example, the lanes of the downstream toll station include target lanes. Each target lane corresponds to at least one target vehicle type, and vehicle trajectory data of vehicles of the target vehicle type in the upstream sensing area can be determined; according to the vehicle trajectory data of vehicles of the target vehicle type, dynamic control is performed on the target lanes of the downstream toll station.

[0066] For example, if the target lane is a green channel lane, since the vehicles passing through the green channel lane are usually large trucks, the target vehicle type can be set as large trucks, and by analyzing the vehicle trajectory data of large trucks separately, dynamic control of the green channel lane of the downstream toll station is achieved.

[0067] The toll station lane control method disclosed in the above embodiments of the present application first obtains vehicle trajectory data in at least one upstream sensing area corresponding to the downstream toll station; then, based on the vehicle trajectory data in all upstream sensing areas, predicts the queuing situation of the downstream toll station to generate a real-time congestion prediction result of the downstream toll station; finally, according to the real-time congestion prediction result, dynamically controls the lanes of the downstream toll station. Thus, real-time congestion prediction is carried out using upstream vehicle trajectory data, and traffic flow changes are responded to by dynamically scheduling the lanes of the downstream toll station, realizing flexible control of toll station lanes and improving the overall traffic efficiency.

[0068] See Figure 2 , which shows a schematic structural diagram of a toll station lane control device provided by the embodiments of the present application. For the sake of convenience of description, only parts related to the embodiments of the present application are shown.

[0069] The toll station lane control device may specifically include the following modules:

[0070] A data acquisition module 201, configured to acquire vehicle trajectory data in at least one upstream sensing area corresponding to the downstream toll station.

[0071] A congestion prediction module 202, configured to predict the queuing situation of the downstream toll station based on the vehicle trajectory data in all upstream sensing areas, and generate a real-time congestion prediction result of the downstream toll station.

[0072] A lane control module 203, configured to dynamically control the lanes of the downstream toll station according to the real-time congestion prediction result.

[0073] The toll station lane control device disclosed in the above embodiments of the present application first obtains vehicle trajectory data in at least one upstream sensing area corresponding to a downstream toll station. Then, based on the vehicle trajectory data in all upstream sensing areas, it predicts the queuing situation of the downstream toll station and generates a real-time congestion prediction result for the downstream toll station. Finally, according to the real-time congestion prediction result, it dynamically controls the lanes of the downstream toll station. Thus, by using upstream vehicle trajectory data for real-time congestion prediction and dynamically scheduling the lanes of the downstream toll station to cope with traffic flow changes, flexible control of the toll station lanes is achieved, and the overall traffic efficiency is improved.

[0074] Further, in a possible implementation manner of the embodiments of the present application, the above real-time congestion prediction result includes a congestion ratio, and the above lane control module 203 may specifically include the following sub-modules:

[0075] The first processing sub-module is used to increase the number of open lanes of the downstream toll station according to the rising amplitude of the congestion ratio when the congestion ratio is in an upward state.

[0076] The second processing sub-module is used to reduce the number of open lanes of the downstream toll station according to the decreasing amplitude of the congestion ratio when the congestion ratio is in a downward state.

[0077] Further, in another possible implementation manner of the embodiments of the present application, the lanes of the above downstream toll station include tidal lanes, and the real-time congestion prediction result includes the congestion ratios corresponding to the two directions of the tidal lanes respectively. The above lane control module 203 may specifically include the following sub-modules:

[0078] The third processing sub-module is used to change the tidal lane to a one-way traffic lane in any direction when the congestion ratio in any direction of the tidal lane exceeds a preset threshold.

[0079] Further, in yet another possible implementation manner of the embodiments of the present application, the lanes of the above downstream toll station include target lanes, and the target lanes correspond to at least one target vehicle type. The above toll station lane control device may specifically further include the following modules:

[0080] The first processing module is used to determine the vehicle trajectory data of the target vehicle type in the upstream sensing area.

[0081] The second processing module is used to dynamically control the target lanes of the downstream toll station according to the vehicle trajectory data of the target vehicle type.

[0082] Further, in still another possible implementation manner of the embodiments of the present application, the above congestion prediction module 202 may specifically include the following sub-modules:

[0083] The fourth processing sub-module is configured to input the vehicle trajectory data within all upstream sensing regions into a preset congestion prediction model, and the preset congestion prediction model outputs a real-time congestion prediction result.

[0084] Further, in another possible implementation manner of the embodiment of the present application, the preset congestion prediction model is generated through the following steps: obtaining multiple sets of reference vehicle trajectory data. Using the multiple sets of reference vehicle trajectory data, performing spatio-temporal feature training on the initial long short-term memory neural network model to generate the preset congestion prediction model.

[0085] Further, in another possible implementation manner of the embodiment of the present application, the congestion prediction module 202 may specifically include the following sub-modules:

[0086] The information acquisition sub-module is configured to acquire the transaction information of each lane of the downstream toll station.

[0087] The fifth processing sub-module is configured to determine the flow imbalance coefficient according to the transaction information of each lane of the downstream toll station.

[0088] The sixth processing sub-module is configured to predict the queuing situation of the downstream toll station according to the vehicle trajectory data within all upstream sensing regions and the flow imbalance coefficient, and generate a real-time congestion prediction result of the downstream toll station.

[0089] Further, in another possible implementation manner of the embodiment of the present application, the vehicle trajectory data includes: vehicle position, vehicle speed, vehicle density, traffic flow, driving direction, vehicle type.

[0090] The toll station lane control device provided by the embodiment of the present application can be applied in the foregoing method embodiment. For details, please refer to the description of the foregoing method embodiment, which will not be elaborated here.

[0091] Figure 3 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As Figure 3 shown, the terminal device 300 of this embodiment includes: at least one processor 310 ( Figure 3 only one processor is shown in the figure), a memory 320, and a computer program 321 stored in the memory 320 and executable on the at least one processor 310. When the processor 310 executes the computer program 321, the steps in the foregoing method embodiment of the toll station lane control method are implemented.

[0092] The terminal device 300 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 310 and a memory 320. Those skilled in the art can understand, Figure 3This is only an example of the terminal device 300, and does not limit the terminal device 300. It may include more or fewer components than those shown, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0093] The so-called processor 310 may be a central processing unit (CPU), and the processor 310 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0094] In some embodiments, the memory 320 may be an internal storage unit of the terminal device 300, such as the hard disk or memory of the terminal device 300. In other embodiments, the memory 320 may also be an external storage device of the terminal device 300, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 300. Further, the memory 320 may also include both the internal storage unit and the external storage device of the terminal device 300. The memory 320 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program, etc. The memory 320 may also be used to temporarily store data that has been output or will be output.

[0095] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0096] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0098] In the embodiments provided in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the above-mentioned module or unit is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0099] The unit described as a separated component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0101] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0102] To implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by a computer program product. When the computer program product runs on a terminal device, the terminal device can execute the steps in the above-mentioned various method embodiments when executed.

[0103] The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A toll station lane control method, characterized in that, Including: Obtaining vehicle trajectory data within at least one upstream sensing area corresponding to a downstream toll station; Predicting the queuing situation of the downstream toll station based on the vehicle trajectory data within all the upstream sensing areas, and generating a real-time congestion prediction result for the downstream toll station; Performing dynamic control on the lanes of the downstream toll station according to the real-time congestion prediction result.

2. The toll station lane control method according to claim 1, wherein The real-time congestion prediction result includes a congestion ratio. Performing dynamic control on the lanes of the downstream toll station according to the real-time congestion prediction result includes: When the congestion ratio is in an increasing state, increasing the number of open lanes of the downstream toll station according to the increasing amplitude of the congestion ratio; When the congestion ratio is in a decreasing state, reducing the number of open lanes of the downstream toll station according to the decreasing amplitude of the congestion ratio.

3. The toll station lane control method according to claim 1, characterized in that The lanes of the downstream toll station include tidal lanes. The real-time congestion prediction result includes the congestion ratios corresponding to the two directions of the tidal lanes respectively. Performing dynamic control on the lanes of the downstream toll station according to the real-time congestion prediction result includes: When the congestion ratio in any direction of the tidal lane exceeds a preset limit threshold, changing the tidal lane to a one-way traffic lane in the any direction.

4. The toll station lane control method according to claim 1, wherein, The lanes of the downstream toll station include target lanes, and the target lanes correspond to at least one target vehicle type. The toll station lane control method further includes: Determining the vehicle trajectory data of the target vehicle type within the upstream sensing area; Performing dynamic control on the target lanes of the downstream toll station according to the vehicle trajectory data of the target vehicle type.

5. The toll station lane control method according to claim 1, characterized in that, The predicting the queuing situation of the downstream toll station based on the vehicle trajectory data within all the upstream sensing areas, and generating a real-time congestion prediction result for the downstream toll station includes: Inputting the vehicle trajectory data within all the upstream sensing areas into a preset congestion prediction model, and the preset congestion prediction model outputs the real-time congestion prediction result.

6. The toll station lane control method according to claim 5, characterized in that, The preset congestion prediction model is generated through the following steps: Obtaining multiple groups of reference vehicle trajectory data; Performing spatio-temporal feature training on an initial long short-term memory neural network model by using the multiple groups of reference vehicle trajectory data to generate the preset congestion prediction model.

7. The toll station lane control method according to claim 1, wherein, The predicting the queuing situation of the downstream toll station based on the vehicle trajectory data within all the upstream sensing areas, and generating a real-time congestion prediction result for the downstream toll station includes: Obtaining the transaction information of each lane of the downstream toll station; Determining a flow imbalance coefficient according to the transaction information of each lane of the downstream toll station; Predicting the queuing situation of the downstream toll station according to all the vehicle trajectory data within the upstream sensing areas and the flow imbalance coefficient, and generating a real-time congestion prediction result for the downstream toll station.

8. The toll station lane control method according to any one of claims 1-7, characterized in that, The vehicle trajectory data includes: vehicle position, vehicle speed, vehicle density, traffic flow, driving direction, vehicle type.

9. A toll station lane control device, characterized in that, Including: A data acquisition module for obtaining vehicle trajectory data within at least one upstream sensing area corresponding to a downstream toll station; A congestion prediction module, configured to predict the queuing situation of the downstream toll station based on the vehicle trajectory data in all the upstream sensing areas, and generate a real-time congestion prediction result of the downstream toll station; A lane control module, configured to dynamically control the lanes of the downstream toll station according to the real-time congestion prediction result.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

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