Traffic flow control method, system, terminal and medium based on in-transit information

Abstract

The present invention discloses a traffic flow control method, system, terminal and medium based on in-transit information, which relates to the field of intelligent transportation. The key points of its technical solution are as follows: The present invention collects the travel time information of traveling vehicles at adjacent network nodes, and performs linear classification processing on the travel time information of multiple neighboring traveling vehicles. Without adding hardware devices, it can batch-screen out the traveling vehicles that did not stay in the service area at the previous network node as target vehicles, and generate a guiding instruction to timely guide the target to drive into the service area of the current network node for rest, enabling comprehensive and timely control and management of the traffic flow on the highway, thereby reducing the probability of traffic accidents.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and more specifically, to a traffic flow control method, system, terminal and medium based on in-transit information. Background Art

[0002] Fatigue driving refers to the phenomenon that after a driver drives continuously for a long time, the physiological and psychological functions are out of balance, resulting in a decline in driving skills. This phenomenon may cause the driver to have symptoms such as blurred vision, backache, and slow reaction. In severe cases, the driver may even lose control of the vehicle, thus leading to traffic accidents.

[0003] Currently, the monitoring of fatigue driving mainly uses various sensors and algorithms to monitor the physiological and behavioral characteristics of the driver, so as to judge whether the driver is in a fatigued state. For example, in the direct monitoring method, physiological signal sensors such as cameras, infrared sensors, electrooculograms, electromyograms, and electrocardiograms are generally used to directly obtain the physiological and behavioral information of the driver. The advantage of direct monitoring is high accuracy, but it requires additional hardware costs. Another example is the indirect monitoring method, which generally infers the fatigue state of the driver by analyzing the driver's operating behaviors, such as the use of the steering wheel, vehicle speed, driving time, and vehicle status information. This method does not require additional hardware, but independent analysis of a large amount of vehicle data requires a large amount of network resources and stable and reliable communication with each vehicle, making it difficult to implement. In addition, the above-mentioned fatigue driving monitoring methods are all for the situation where the fatigue driving state has already occurred. Limited by the regulation that high-speed traffic cannot stop at will, it is difficult for the above-mentioned fatigue driving monitoring methods to control the vehicles and personnel with fatigue driving from a global perspective, and the timeliness of control is poor.

[0004] Therefore, how to research and design a traffic flow control method, system, terminal and medium based on in-transit information that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a traffic flow control method, system, terminal and medium based on in-transit information. By collecting the driving time information of driving vehicles at adjacent network nodes and performing linear classification processing on the driving time information of multiple adjacent driving vehicles, it is possible to batch-screen the driving vehicles that did not stop in the service area at the previous network node as target vehicles without adding hardware devices, and generate guiding instructions to timely guide the targets to drive into the service area of the current network node for rest, so as to comprehensively and timely control and manage the traffic flow on the highway, thereby reducing the probability of traffic accidents.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions:

[0007] In a first aspect, a traffic flow control method based on in - transit information is provided, including the following steps:

[0008] Construct a control network according to the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the nearest ETC recording points on the incoming side of the corresponding service areas;

[0009] Collect the end time recorded by the ETC recording point of the driving vehicle in the current network node and the start time recorded by the ETC recording point in the previous network node to obtain the driving time information of the driving vehicle;

[0010] Perform linear classification processing on the driving time information of multiple driving vehicles, and screen out the driving vehicles that did not stop at the service area in the previous network node as target vehicles;

[0011] Obtain the start time recorded by the ETC recording point of the target vehicle in the starting network node. The starting network node is the network node that is the farthest from the current network node during the continuous driving stage of the target vehicle;

[0012] Determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and combine the driving mileage corresponding to the continuous driving stage to determine the fatigue parameter of the target vehicle;

[0013] Generate a guiding instruction for the target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured by the service area in the current network node;

[0014] Transmit the guiding instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest.

[0015] Furthermore, the step of performing linear classification processing on the driving time information of multiple driving vehicles and screening out the driving vehicles that did not stop at the service area in the previous network node as target vehicles specifically includes the following steps:

[0016] Calculate the actual driving time of the corresponding driving vehicle by the difference between the end time and the start time of the driving vehicle;

[0017] Establish a time scatter plot with the start time as the abscissa and the actual driving time as the ordinate;

[0018] Set a linear function T = a, where T is the actual driving time and a is a constant value;

[0019] Take the minimum average scatter degree of all discrete points in the time scatter plot to the linear function as the optimization goal, and solve to obtain the optimal linear function;

[0020] The driving vehicle corresponding to the discrete points below the optimal linear function is the target vehicle.

[0021] Further, the method further includes:

[0022] Dynamically updating the time discretization graph according to the driving time information of the driving vehicle collected in real time;

[0023] When the new time width corresponding to the dynamic update of the time discretization graph reaches the set time sliding window width, perform linear classification processing on the area corresponding to the new time width in the time discretization graph.

[0024] Further, the fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the driving mileage corresponding to the corresponding continuous driving stage;

[0025] When the continuous driving time of the target vehicle remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the driving mileage corresponding to the corresponding continuous driving stage;

[0026] When the driving mileage corresponding to the continuous driving stage remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the continuous driving time of the target vehicle.

[0027] Further, the specific calculation expression of the fatigue parameter of the target vehicle is:

[0028] ;

[0029] Wherein, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter.

[0030] Further, the specific calculation expression of the fatigue parameter of the target vehicle is:

[0031] ;

[0032] Wherein, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the average driving speed of the target vehicle; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter; Represents the correlation function between the average driving speed and the fatigue parameter.

[0033] Furthermore, the calculation expression of the parameter threshold is specifically:

[0034] ;

[0035] Wherein, represents the parameter threshold configured for the service area in the network node ; represents the vehicle reception capacity designed for the service area in the network node ; represents the standard vehicle reception capacity of the service area in the network node; represents the standard fatigue parameter value; represents a natural number; represents the proportion of the remaining vehicle reception capacity of the service area in the network node .

[0036] In a second aspect, a traffic flow control system based on in - transit information is provided. This system is used to implement the traffic flow control method based on in - transit information as described in any one of the first aspects, and includes:

[0037] A network construction module, which is used to construct a control network according to the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the nearest ETC recording points on the incoming side of the corresponding service areas;

[0038] An information collection module, which is used to collect the end time recorded by the ETC recording point of the driving vehicle in the current network node and the start time recorded by the ETC recording point in the previous network node, so as to obtain the driving time information of the driving vehicle;

[0039] A classification processing module, which is used to perform linear classification processing on the driving time information of multiple driving vehicles, and screen out the driving vehicles that did not stay in the service area of the previous network node as target vehicles;

[0040] A time calling module, which is used to obtain the start time recorded by the ETC recording point of the target vehicle in the starting network node. The starting network node is the network node that is the farthest from the current network node during the continuous driving stage of the target vehicle;

[0041] A fatigue analysis module, which is used to determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and combine the driving mileage corresponding to the continuous driving stage to determine the fatigue parameter of the target vehicle;

[0042] An instruction generation module, configured to generate a guidance instruction for a target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured for the service area in the current network node;

[0043] A vehicle control module, configured to transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest.

[0044] In a third aspect, a computer terminal is provided, 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 traffic flow control method based on in-transit information described in any one of the first aspects is implemented.

[0045] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the traffic flow control method based on in-transit information described in any one of the first aspects can be implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. For the traffic flow control method based on in-transit information provided by the present invention, the driving time information of the driving vehicles is collected between adjacent network nodes, and the driving time information of multiple adjacent driving vehicles is linearly classified. Without adding hardware devices, the driving vehicles that did not stay in the service area in the previous network node can be screened out in batches as target vehicles, and a guidance instruction is generated to timely guide the target to drive into the service area in the current network node for rest, which can comprehensively and timely control and manage the traffic flow on the highway, thereby reducing the probability of traffic accidents;

[0048] 2. The present invention establishes a time discrete graph with the starting time as the abscissa and the actual driving time as the ordinate, and takes the minimum average discrete degree of all discrete points in the time discrete graph to the linear function as the optimization goal, which can more accurately classify the driving vehicles into two categories: staying in the service area and not staying in the service area, without considering the interference of environmental conditions and specific road conditions on the linear classification;

[0049] 3. The present invention dynamically updates the time discrete graph according to the real-time collected driving time information of the driving vehicles; when the new time width corresponding to the dynamic update of the time discrete graph reaches the set time sliding window width, the linear classification process is performed on the area corresponding to the new time width in the time discrete graph, which can perform accurate classification according to the change of the congestion degree;

[0050] 4. When the present invention dynamically configures parameter thresholds for network nodes considering the actual reception capacity of each service area, it balances the actual operating conditions of each service area, reduces extreme situations such as congestion and idleness in different service areas respectively, and can reasonably regulate the traffic flow on the highway to a certain extent, making the transportation efficiency of the highway higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0052] Figure 1 is the flowchart in Embodiment 1 of the present invention;

[0053] Figure 2 is the system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and do not limit the present invention.

[0055] Embodiment 1: A traffic flow control method based on in-transit information, as Figure 1 shown, includes the following steps:

[0056] S1: Construct a control network according to the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the nearest ETC recording point on the incoming side of the corresponding service area;

[0057] S2: Collect the end time recorded by the ETC recording point of the driving vehicle in the current network node and the start time recorded by the ETC recording point in the previous network node to obtain the driving time information of the driving vehicle;

[0058] S3: Perform linear classification processing on the driving time information of multiple driving vehicles, and screen out the driving vehicles that did not stop in the service area in the previous network node as target vehicles;

[0059] S4: Obtain the start time recorded by the ETC recording point of the target vehicle in the starting network node, where the starting network node is the network node farthest from the current network node during the continuous driving stage of the target vehicle;

[0060] S5: Determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and determine the fatigue parameter of the target vehicle in combination with the driving mileage corresponding to the continuous driving stage;

[0061] S6: Generate a guidance instruction for a target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured for the service area in the current network node;

[0062] S7: Transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest.

[0063] In step S1, highways are generally two-way roads, while the control network constructed in the present invention is only for one-way roads. The ETC (Electronic Toll Collection) recording points on highways usually refer to the ETC gantry systems, which are distributed at various key positions on highways and are used to record the driving trajectories and transaction information of vehicles. The ETC gantry system is an important part of the electronic toll collection system, which can automatically identify and record the passing information of vehicles, including vehicle identity, passing time and location, etc.

[0064] The distribution of ETC gantries is usually designed according to the traffic flow, geographical environment and traffic management requirements of highways. For example, ETC gantries are set at highway entrances and exits with large traffic volumes, as well as at key nodes such as important transportation hubs, tunnels and bridges. This can ensure comprehensive monitoring of vehicle driving paths and also help improve the operation efficiency and safety of highways.

[0065] Highway service areas are important facilities set up to meet the rest and supply needs of drivers and passengers during long-distance driving. The setting of service areas is usually planned according to factors such as traffic flow, geographical environment, road network structure and traffic demand.

[0066] In practical applications, the number of service areas is less than that of ETC recording points. Considering factors such as driving distance and driving time, the accuracy and timeliness of fatigue detection and analysis at multiple ETC recording points closer to the service area are poor, and it will consume a large amount of network resources. Therefore, in the present invention, when constructing the control network, each service area is used as a network node, and each network node also consists of the nearest ETC recording point on the driving-in side of the corresponding service area, so as to guide the driver to enter the service area to stay and rest in time after fatigue detection and analysis.

[0067] In step S2, since there is a service area between the ETC recording points in two adjacent network nodes, if it can be determined that the vehicle stays in the service area, it is defaulted that the driver has rested and no fatigue analysis is required; on the contrary, for vehicles that do not stay in the service area, detection is needed.

[0068] Under approximate road conditions, whether a vehicle stops at a service area can be distinguished by the duration of the vehicle passing between two ETC recording points. Therefore, it is necessary to collect the end time recorded by the ETC recording point of the traveling vehicle at the current network node and the start time recorded by the ETC recording point at the previous network node to obtain the travel time information of the traveling vehicle.

[0069] Among them, the way to obtain the start time recorded by the ETC recording point at the previous network node can be to establish communication between two network nodes. The current network node can actively read the start time from the previous network node according to the vehicle information. In addition, each network node can also establish communication with the cloud platform center. All the information collected by the ETC recording points is stored in the database owned by the cloud platform center, and the time information required by each network node can be read from the database.

[0070] In step S3, since the traveling vehicles are only divided into two categories: staying at the service area and not staying at the service area, and there are obvious linear boundaries in the travel time information, a linear classification algorithm can be used for processing.

[0071] For the linear classification processing of the two categories, the linear classification processing in the present invention includes the following steps: calculating the actual travel time of the corresponding traveling vehicle by the difference between the end time and the start time of the traveling vehicle; establishing a time scatter plot with the start time as the abscissa and the actual travel time as the ordinate; setting a linear function T = a, where T is the actual travel time and a is a constant value; taking the minimum average scatter degree of all discrete points in the time scatter plot to the linear function as the optimization goal, and solving to obtain the optimal linear function; the traveling vehicles corresponding to the discrete points below the optimal linear function are the target vehicles.

[0072] In addition, considering that the highway congestion degree varies greatly at different times, in the scenario where the congestion degree changes rapidly, the value of the optimal linear function is determined by the overall situation and cannot accurately classify according to the congestion degree. For this reason, the present invention dynamically updates the time scatter plot based on the travel time information of the traveling vehicles collected in real time; when the new time width corresponding to the dynamic update of the time scatter plot reaches the set time sliding window width, linear classification processing is performed on the area corresponding to the new time width in the time scatter plot.

[0073] In step S4, attributes can be added to the classification results of each traveling vehicle at each network node. When a traveling vehicle is screened as a target vehicle at a network node, the attribute value of the traveling vehicle at the corresponding network node is 0; conversely, when a traveling vehicle is not screened as a target vehicle at a network node, the attribute value is 1.

[0074] If the attribute value sequence of a moving vehicle at five consecutive network nodes is {0, 0, 1, 0, 0}, it indicates that the moving vehicle has stopped and rested at the 3rd network node. When performing fatigue detection and analysis at the 5th network node, the 3rd network node is used as the starting network node.

[0075] In step S5, the continuous driving stage of the target vehicle should be the stage between the service area in the starting network node and the ETC recording point of the current network node. However, considering the convenience of obtaining time information and location information, the present invention replaces the service area in the starting network node with the ETC recording point in the starting network node. Therefore, the continuous driving time of the target vehicle is determined according to the difference between the start time and the end time of the target vehicle.

[0076] In the present invention, the fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the driving mileage corresponding to the corresponding continuous driving stage; when the continuous driving time of the target vehicle remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the driving mileage corresponding to the corresponding continuous driving stage; when the driving mileage corresponding to the continuous driving stage remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the continuous driving time of the target vehicle.

[0077] As an optional implementation manner, the calculation expression of the fatigue parameter of the target vehicle is specifically:

[0078] ;

[0079] Among them, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter.

[0080] As another optional implementation manner, the calculation expression of the fatigue parameter of the target vehicle is specifically:

[0081] ;

[0082] Among them, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the average driving speed of the target vehicle; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter; Represents the correlation function between the average driving speed and the fatigue parameter.

[0083] It should be noted that the above correlation functions can all be obtained through statistical analysis based on relevant statistical data, and can be represented by linear functions or non-linear functions, without limitation here.

[0084] In step S6, the parameter threshold configured for the service area can be set to a fixed empirical value, such as being differentiated and set on a daily or hourly basis.

[0085] In addition, the parameter threshold can also be dynamically designed according to the service capacity of the service area. The specific calculation expression of the parameter threshold is:

[0086] ;

[0087] Where, Represents the parameter threshold configured for the service area in the network node ; Represents the designed vehicle reception volume of the service area in the network node ; Represents the standard vehicle reception volume of the service area in the network node; Represents the standard fatigue parameter value; Represents a natural number; Represents the proportion of the remaining vehicle reception volume of the service area in the network node ;

[0088] It should be noted that the standard fatigue parameter value is a constant value, which is related to the recommended data of the high-speed section, and the recommended data of the high-speed section can also provide reference data for the calculation of the fatigue parameter of the target vehicle. For example, the speed limit of a certain highway is 100 km / h, and it is recommended to rest once after driving 200 kilometers at a speed of 100 km / h. The corresponding continuous driving time is 2 h, then the standard fatigue parameter value in this case can be defined as 0.8. Assuming a vehicle drives 300 kilometers at a speed of 110 km / h, the continuous driving time can be obtained as approximately 2.7 h. In 、 And Are all linear functions, then the fatigue parameter of the target vehicle is (0.8×2.7 / 2)×(0.8×110 / 100)×(0.8×300 / 200)=1.4256. The above is only an example for illustration.

[0089] When the present invention dynamically configures parameter thresholds for network nodes considering the actual reception capabilities of each service area, it balances the actual operating conditions of each service area, reduces extreme situations such as congestion and idleness in different service areas respectively, and can reasonably regulate the traffic flow on the highway to a certain extent, making the transportation efficiency of the highway higher.

[0090] In step S7, after the guidance instruction is generated at the cloud platform center or each network node, short - term communication can be carried out between the network node and the target vehicle to achieve the transmission of the guidance instruction.

[0091] Embodiment 2: A traffic flow control system based on in - transit information, which is used to implement the traffic flow control method based on in - transit information described in Embodiment 1, as Figure 2 shown, including a network construction module, an information collection module, a classification processing module, a time call module, a fatigue analysis module, an instruction generation module, and a vehicle control module.

[0092] Among them, the network construction module is used to construct a control network according to the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the nearest ETC recording point on the incoming side of the corresponding service area; the information collection module is used to collect the end time recorded by the ETC recording point of the driving vehicle at the current network node and the start time recorded by the ETC recording point at the previous network node to obtain the driving time information of the driving vehicle; the classification processing module is used to perform linear classification processing on the driving time information of multiple driving vehicles and screen out the driving vehicles that did not stay in the service area at the previous network node as target vehicles; the time call module is used to obtain the start time recorded by the ETC recording point of the target vehicle at the starting network node, and the starting network node is the network node that is the farthest from the current network node during the continuous driving stage of the target vehicle; the fatigue analysis module is used to determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and combine the driving mileage corresponding to the continuous driving stage to determine the fatigue parameter of the target vehicle; the instruction generation module is used to generate a guidance instruction for the target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured by the service area in the current network node; the vehicle control module is used to transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node to rest.

[0093] The present invention also records a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the traffic flow control method based on in - transit information described in Embodiment 1.

[0094] The present invention also records a computer-readable medium, on which a computer program is stored. When the computer program is executed by a processor, it can implement the traffic flow control method based on in-transit information as described in Embodiment 1.

[0095] Working principle: The present invention collects the travel time information of traveling vehicles at adjacent network nodes, and performs linear classification processing on the travel time information of multiple neighboring traveling vehicles. Without adding hardware devices, it can batch-screen out the traveling vehicles that did not stay in the service area at the previous network node as target vehicles, and generate guiding instructions to timely guide the targets to drive into the service area of the current network node for rest, enabling comprehensive and timely control and management of the traffic flow on the highway, thereby reducing the probability of traffic accidents.

[0096] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0098] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0100] The specific embodiments described above have further elaborated on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A traffic flow control method based on in-transit information, characterized in that Including the following steps: Construct a control network based on the distribution information of ETC recording points and service areas. The network nodes in the control network consist of service areas and the nearest ETC recording points on the incoming side of the corresponding service areas; Collect the end time recorded by the ETC recording point of the traveling vehicle in the current network node and the start time recorded by the ETC recording point in the previous network node to obtain the travel time information of the traveling vehicle; Perform linear classification processing on the travel time information of multiple traveling vehicles, and screen out the traveling vehicles that did not stay in the service area in the previous network node as target vehicles; Obtain the start time recorded by the ETC recording point of the target vehicle in the starting network node. The starting network node is the network node that is the farthest from the current network node during the continuous driving stage of the target vehicle; Determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and combine the driving mileage corresponding to the continuous driving stage to determine the fatigue parameter of the target vehicle; Generate a guidance instruction for the target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured in the service area of the current network node; Transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest; The specific calculation expression of the parameter threshold is: ； Among them, represents the parameter threshold configured for the service area in the network node ; represents the designed vehicle reception capacity of the service area in the network node ; represents the standard vehicle reception capacity of the service area in the network node represents the standard fatigue parameter value represents a natural number represents the network node represents the proportion of the remaining vehicle reception capacity of the service area in the network node 2. The traffic flow control method based on in-transit information according to claim 1, characterized in that, The linear classification processing of the travel time information of multiple traveling vehicles to screen out the traveling vehicles that did not stay in the service area in the previous network node as target vehicles specifically includes the following steps: Calculate the actual travel time of the corresponding traveling vehicle by the difference between the end time and the start time of the traveling vehicle; Establish a time scatter plot with the start time as the abscissa and the actual travel time as the ordinate; Set a linear function T = a, where T is the actual travel time and a is a constant value; Taking the minimum average dispersion degree of all discrete points in the time scatter plot to the linear function as the optimization goal, solve for the optimal linear function; The traveling vehicles corresponding to the discrete points located below the optimal linear function are target vehicles.

3. The traffic flow control method based on in-transit information according to claim 1, characterized in that, This method further includes: Dynamically update the time scatter plot based on the travel time information of the traveling vehicle collected in real time; When the new time width corresponding to the dynamic update of the time scatter plot reaches the set time sliding window width, perform linear classification processing on the area corresponding to the new time width in the time scatter plot.

4. The traffic flow control method based on in-transit information according to claim 1, characterized in that, The fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the driving mileage corresponding to the corresponding continuous driving stage; When the continuous driving time of the target vehicle remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the driving mileage corresponding to the corresponding continuous driving stage; When the driving mileage corresponding to the continuous driving stage remains unchanged, the fatigue parameter of the target vehicle is positively correlated with the continuous driving time of the target vehicle.

5. The traffic flow control method based on in-transit information according to claim 4, characterized in that, The specific calculation expression of the fatigue parameter of the target vehicle is: ； Among them, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter.

6. The traffic flow control method based on in-transit information according to claim 4, characterized in that The specific calculation expression of the fatigue parameter of the target vehicle is: ； Among them, represents the fatigue parameter of the target vehicle; represents the continuous driving time of the target vehicle; represents the driving mileage corresponding to the continuous driving stage; represents the average driving speed of the target vehicle; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between the driving mileage and the fatigue parameter; represents the correlation function between the average driving speed and the fatigue parameter.

7. A traffic flow control system based on in-transit information, characterized in that This system is used to implement the traffic flow control method based on in - transit information as described in any one of claims 1 - 6, including: A network construction module, configured to construct a control network according to the distribution information of ETC recording points and service areas, where the network nodes in the control network are composed of service areas and the nearest ETC recording point located on the incoming side of the corresponding service area; An information collection module, configured to collect the end time recorded by the ETC recording point of the current network node of the traveling vehicle and the start time recorded by the ETC recording point of the previous network node, so as to obtain the travel time information of the traveling vehicle; A classification processing module, configured to perform linear classification processing on the travel time information of multiple traveling vehicles, and screen out the traveling vehicles that did not stay in the service area of the previous network node as target vehicles; A time calling module, configured to obtain the start time recorded by the ETC recording point of the target vehicle in the starting network node, where the starting network node is the network node that is the farthest from the current network node during the continuous driving stage of the target vehicle; A fatigue analysis module, configured to determine the continuous driving time of the target vehicle according to the difference between the start time and the end time of the target vehicle, and combine the driving mileage corresponding to the continuous driving stage to determine the fatigue parameter of the target vehicle; An instruction generation module, configured to generate a guidance instruction for a target vehicle whose fatigue parameter is greater than or equal to the parameter threshold configured by the service area in the current network node; A vehicle control module, configured to transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest; The specific calculation expression of the parameter threshold is: ； Among them, represents the parameter threshold configured for the service area in the network node; represents the designed vehicle reception capacity of the service area in the network node; represents the standard vehicle reception capacity of the service area in the network node; represents the standard fatigue parameter value; represents a natural number; represents the proportion of the remaining vehicle reception capacity of the service area in the network node.

8. A computer terminal, 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, it implements the traffic flow control method based on in-transit information as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it can implement the traffic flow control method based on in-transit information as described in any one of claims 1-6.

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