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

Abstract

The invention discloses a traffic flow management and control method and system based on in-transit information, a terminal and a medium, and relates to the field of intelligent traffic, and the key points of the technical scheme are as follows: the driving time information of driving vehicles is collected at adjacent network nodes, and the driving time information of a plurality of adjacent driving vehicles is subjected to linear classification processing; according to the method, driving vehicles not staying in the service area of the previous network node can be screened out in batches to serve as target vehicles under the condition that hardware equipment is not added, the guide instruction is generated to guide the target vehicles to drive into the service area of the current network node for rest in time, the traffic flow on the expressway can be comprehensively and timely controlled and managed, and the traffic flow of the expressway can be timely controlled and managed. Therefore, the probability of traffic accidents is reduced.

Description

Technical Field

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

[0002] Fatigue driving refers to the phenomenon that after a long period of continuous driving, the driver's physiological and psychological functions become unbalanced, resulting in a decline in driving skills. This phenomenon may cause the driver to experience symptoms such as blurred vision, back pain, and slow reaction. In severe cases, the driver may even lose control of the vehicle, thus causing a traffic accident.

[0003] At present, the monitoring of fatigue driving mainly uses various sensors and algorithms to monitor the physiological and behavioral characteristics of the driver, so as to determine whether the driver is in a state of fatigue. For example, the direct monitoring method generally uses physiological signal sensors such as cameras, infrared sensors, electrooculograms, electromyograms, and electrocardiograms to directly obtain the driver's physiological and behavioral information. 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 driver's fatigue state by analyzing the driver's operating behavior, such as steering wheel use, vehicle speed, driving time, and vehicle status information. This method does not require additional hardware, but the data of a large number of vehicles are analyzed independently, and the required network resources are more, and it is necessary to establish stable and reliable communication with each vehicle, which is difficult to achieve. In addition, the above-mentioned fatigue driving monitoring methods are all aimed at the situation where the fatigue driving state has already appeared. Limited by the regulations that high-speed traffic cannot stop at will, the above-mentioned fatigue driving monitoring methods are difficult to control 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-mentioned defects is a problem that we urgently need to solve. Summary of the invention

[0005] In order 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, collect the travel time information of traveling vehicles at adjacent network nodes, and perform linear classification processing on the travel time information of multiple adjacent traveling vehicles. Without adding hardware equipment, traveling vehicles that have not stopped in the service area in the previous network node can be batch screened out as target vehicles, and guidance instructions can be generated to promptly guide the targets to enter the service area in the current network node for rest. The traffic flow on the highway can be comprehensively and timely controlled and managed, thereby reducing the probability of traffic accidents.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: In the first aspect, a traffic flow control method based on in-transit information is provided, comprising the following steps: A control network is constructed based on the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the ETC recording points located at the entrance side of the corresponding service areas and the closest distance. 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; Performing linear classification processing on the travel time information of multiple vehicles, and selecting the vehicles that have not stopped in the service area of ​​the previous network node as target vehicles; Obtain the start time recorded by the ETC record 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 phase 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 determine the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving stage; Generate guidance instructions for target vehicles whose fatigue parameters are greater than or equal to the parameter threshold configured in the service area in the current network node; The guidance instruction is transmitted to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest.

[0007] Furthermore, the linear classification process is performed on the travel time information of the plurality of traveling vehicles to select the traveling vehicles that have not stopped in the service area of ​​the previous network node as the target vehicles, which specifically includes the following steps: The actual travel time of the corresponding vehicle is calculated based on the difference between the end time and the starting time of the vehicle; A time-discrete graph is established with the starting time as the horizontal axis and the actual travel time as the vertical axis; Set the linear function T=a, where T is the actual driving time and a is a constant value; Taking the minimum average discrete degree of all discrete points in the time discrete graph to the linear function as the optimization goal, the optimal linear function is solved; The moving vehicle corresponding to the discrete point below the optimal linear function is the target vehicle.

[0008] Furthermore, the method also includes: Dynamically update the time discrete graph based on the travel time information of the traveling vehicles collected in real time; When the newly added time width corresponding to the dynamic update of the time discrete graph reaches the set time sliding window width, linear classification processing is performed on the area corresponding to the newly added time width in the time discrete graph.

[0009] Furthermore, the fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the 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 mileage corresponding to the corresponding continuous driving stage; When the 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.

[0010] Furthermore, the calculation expression of the fatigue parameter of the target vehicle is specifically: ; in, represents the fatigue parameter of the target vehicle; Indicates the continuous driving time of the target vehicle; Indicates the 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 mileage and fatigue parameters.

[0011] Furthermore, the calculation expression of the fatigue parameter of the target vehicle is specifically: ; in, represents the fatigue parameter of the target vehicle; Indicates the continuous driving time of the target vehicle; Indicates the mileage corresponding to the continuous driving stage; Indicates the average speed of the target vehicle; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between mileage and fatigue parameters; Represents the correlation function between the average driving speed and the fatigue parameter.

[0012] Furthermore, the calculation expression of the parameter threshold is specifically: ; in, Represents a network node The parameter thresholds configured in the service area; Represents a network node The vehicle reception capacity designed for the central service area; Indicates the standard vehicle reception capacity of the service area in the network node; represents the standard fatigue parameter value; represents a natural number; Represents a network node The proportion of remaining vehicles received by the central service area.

[0013] In a second aspect, a traffic flow control system based on in-transit information is provided, the system is used to implement the traffic flow control method based on in-transit information as described in any one of the first aspects, including: A network construction module is used to construct a control network based on the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the ETC recording points located at the entrance side of the corresponding service area and the closest distance; The information collection module is used to 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; A classification processing module is used to perform linear classification processing on the travel time information of multiple traveling vehicles, and select the traveling vehicles that have not stopped in the service area of ​​the previous network node as target vehicles; The time calling module 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 farthest from the current network node during the continuous driving phase of the target vehicle. A 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 determine the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving stage; An instruction generation module, used to generate a guidance instruction for a target vehicle whose fatigue parameter is greater than or equal to a parameter threshold configured in the service area in the current network node; The vehicle control module is used to transmit guidance instructions to the corresponding target vehicle to guide the target vehicle to enter the service area in the current network node for rest.

[0014] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a traffic flow control method based on in-transit information as described in any one of the first aspects is implemented.

[0015] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement a traffic flow control method based on in-transit information as described in any one of the first aspects.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The traffic flow control method based on in-transit information provided by the present invention collects the travel time information of the traveling vehicles at the adjacent network nodes, and performs linear classification processing on the travel time information of multiple adjacent traveling vehicles. Without adding hardware equipment, the traveling vehicles that have not stopped in the service area of ​​the previous network node can be batch-screened as target vehicles, and guidance instructions can be generated to timely guide the target vehicles to the service area of ​​the current network node for rest. The traffic flow on the highway can be comprehensively and timely controlled and managed, thereby reducing the probability of traffic accidents. 2. The present invention establishes a time dispersion graph with the starting time as the horizontal coordinate and the actual driving time as the vertical coordinate, and takes the minimum average dispersion degree of all discrete points in the time dispersion graph to the linear function as the optimization goal, which can more accurately classify the driving vehicles into two categories: those staying in the service area and those not staying in the service area, without considering the interference of environmental conditions and specific road conditions on linear classification; 3. The present invention dynamically updates the time discrete graph based on the driving time information of the driving vehicles collected in real time; when the newly added time width corresponding to the dynamic update of the time discrete graph reaches the set time sliding window width, the area corresponding to the newly added time width in the time discrete graph is linearly classified, and accurate classification can be performed as the congestion level changes; 4. The present invention dynamically configures parameter thresholds for network nodes while taking into account the actual reception capacity of each service area, thereby balancing the actual operation conditions of each service area, reducing extreme conditions such as congestion and idleness in different service areas, and to a certain extent being able to reasonably regulate the traffic flow on the highway, thereby making the transportation efficiency of the highway higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 is a flow chart of Embodiment 1 of the present invention; Figure 2 It is a system block diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0019] Example 1: Traffic flow control method based on in-transit information, such as Figure 1As shown, the following steps are included: S1: Construct a control network based on the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the ETC recording points located at the entrance side of the corresponding service areas and the closest distance; S2: 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; S3: performing linear classification processing on the travel time information of multiple traveling vehicles, and selecting the traveling vehicles that have not stopped in the service area of ​​the previous network node as target vehicles; S4: Obtain the start time recorded by the ETC record point of the target vehicle in the starting network node, where the starting network node is a network node farthest from the current network node during the continuous driving phase of the target vehicle; S5: determining 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 determining the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving stage; S6: Generate guidance instructions for target vehicles whose fatigue parameters are greater than or equal to the parameter threshold configured in the service area in the current network node; S7: Transmitting 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.

[0020] In step S1, the expressway is generally a two-way road, and the control network constructed by the present invention is only for one-way roads. The ETC (Electronic Toll Collection) recording points on the expressway usually refer to the ETC gantry system, which is distributed at various key locations on the expressway and is used to record the vehicle's driving trajectory and transaction information. The ETC gantry system is an important part of the electronic toll collection system. They can automatically identify and record the vehicle's travel information, including the vehicle's identity, travel time and location, etc.

[0021] The distribution of ETC gantries is usually designed based on the traffic flow, geographical environment and traffic management needs of the highway. For example, ETC gantries are set up at the entrances and exits of highways with heavy traffic, as well as at important transportation hubs or key nodes such as tunnels and bridges. This ensures comprehensive monitoring of vehicle travel paths and helps improve the operational efficiency and safety of highways.

[0022] The service area of ​​a highway is an important facility set up to meet the rest and supply needs of drivers and passengers during long-distance driving. The establishment of a service area is usually planned based on factors such as traffic flow, geographical environment, road network structure and traffic demand.

[0023] In actual applications, the number of service areas is smaller than the number of ETC recording points. Considering factors such as driving distance and driving time, the accuracy and timeliness of fatigue detection analysis at multiple ETC recording points close to the service area are poor, and it will occupy a large amount of network resources. Therefore, the present invention constructs a management and control network, with each service area as a network node, and each network node also includes the ETC recording point located at the entrance side of the corresponding service area and the closest distance, so as to guide the driver to enter the service area to stop and rest in time after fatigue detection and analysis is realized.

[0024] 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 has stopped in the service area, it is assumed that the driver has rested and no fatigue analysis is required; conversely, for vehicles that have not stopped in the service area, detection is required.

[0025] Under similar road conditions, whether a vehicle stops in a service area can be distinguished by the length of time between the vehicle passing through two ETC recording points. Therefore, it is necessary to collect the end time recorded by the ETC recording point of the traveling vehicle in the current network node and the starting time recorded by the ETC recording point in the previous network node to obtain the driving time information of the traveling vehicle.

[0026] The starting time recorded by the ETC recording point in the previous network node can be obtained by establishing communication between the two network nodes, and the current network node can actively read the starting 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, and all the information collected by the ETC recording point is stored in the database of the cloud platform center, and the time information required by each network node can be read from the database.

[0027] In step S3, since the traveling vehicles are divided into only two categories, namely, vehicles that stay in the service area and vehicles that do not stay in the service area, and there is an obvious linear boundary in the travel time information, a linear classification algorithm can be used for processing.

[0028] As for the linear classification processing of the two categories, the linear classification processing in the present invention includes the following steps: calculating the actual driving time of the corresponding driving vehicle by the difference between the end time and the starting time of the driving vehicle; establishing a time discrete graph with the starting time as the horizontal coordinate and the actual driving time as the vertical coordinate; setting the linear function T=a, where T is the actual driving time and a is a constant value; taking the minimum average discrete degree from all discrete points in the time discrete graph to the linear function as the optimization goal, solving the optimal linear function; the driving vehicle corresponding to the discrete point located below the optimal linear function is the target vehicle.

[0029] In addition, considering that the degree of highway congestion varies greatly at different times, in scenarios where the degree of congestion changes rapidly, the optimal linear function value is determined by the overall situation, and it is impossible to accurately classify and process according to the degree of congestion. To this end, the present invention dynamically updates the time discrete graph based on the travel time information of the traveling vehicles collected in real time; when the newly added time width corresponding to the dynamic update of the time discrete graph reaches the set time sliding window width, the area corresponding to the newly added time width in the time discrete graph is linearly classified.

[0030] In step S4, attributes may 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 the traveling vehicle is not screened as a target vehicle at a network node, the attribute value is 1.

[0031] If the attribute value sequence of a moving vehicle at five consecutive network nodes is {0,0,1,0,0}, it means that the moving vehicle has stopped at the third network node for a rest. When fatigue detection and analysis is performed at the fifth network node, the third network node is used as the starting network node.

[0032] In step S5, the continuous driving phase of the target vehicle should be the phase between the service area in the starting network node and the ETC recording point in the current network node, but 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 starting time and the end time of the target vehicle.

[0033] In the present invention, the fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the 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 mileage corresponding to the corresponding continuous driving stage; when the 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.

[0034] As an optional implementation, the calculation expression of the fatigue parameter of the target vehicle is specifically: ; in, represents the fatigue parameter of the target vehicle; Indicates the continuous driving time of the target vehicle; Indicates the 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 mileage and fatigue parameters.

[0035] As another optional implementation, the calculation expression of the fatigue parameter of the target vehicle is specifically: ; in, represents the fatigue parameter of the target vehicle; Indicates the continuous driving time of the target vehicle; Indicates the mileage corresponding to the continuous driving stage; Indicates the average speed of the target vehicle; represents the correlation function between the continuous driving time and the fatigue parameter; represents the correlation function between mileage and fatigue parameters; Represents the correlation function between the average driving speed and the fatigue parameter.

[0036] It should be noted that the above-mentioned correlation functions can be obtained by statistical analysis based on relevant statistical data, and can be represented by linear functions or nonlinear functions, without limitation here.

[0037] In step S6, the parameter thresholds configured for the service area may be set as fixed empirical values, such as being set based on days or hours as periods.

[0038] In addition, the parameter threshold can also be dynamically designed according to the service capacity of the service area. The calculation expression of the parameter threshold is as follows: ; in, Represents a network node The parameter thresholds configured in the service area; Represents a network node The vehicle reception capacity designed for the central service area; Indicates the standard vehicle reception capacity of the service area in the network node; represents the standard fatigue parameter value; represents a natural number; Represents a network node The proportion of remaining vehicles received by the central service area.

[0039] It should be noted that the standard fatigue parameter value is a constant value, which is related to the recommended data of the expressway section, and the recommended data of the expressway section can also provide reference data for the fatigue parameter calculation of the target vehicle. For example, the speed limit of a certain expressway is 100km / h, and it is recommended to take a rest after driving 200 kilometers at a speed of 100km / h. The corresponding continuous driving time is 2h, and the standard fatigue parameter value in this case can be defined as 0.8. Assuming that a car drives 300 kilometers at a speed of 110km / h, it can be concluded that the continuous driving time is about 2.7h. , and When all are linear functions, 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.

[0040] The present invention dynamically configures parameter thresholds for network nodes while taking into account the actual reception capacity of each service area, thereby balancing the actual operation conditions of each service area, reducing extreme conditions such as congestion and idleness in different service areas, and reasonably regulating the traffic flow on the highway to a certain extent, thereby making the transportation efficiency of the highway higher.

[0041] In step S7, after the cloud platform center or each network node generates the guidance instruction, the guidance instruction can be transmitted by short-term communication between the network node and the target vehicle.

[0042] Example 2: A traffic flow control system based on in-transit information, the system is used to implement the traffic flow control method based on in-transit information recorded in Example 1, such as Figure 2 As shown, it includes a network construction module, an information collection module, a classification processing module, a time calling module, a fatigue analysis module, an instruction generation module and a vehicle control module.

[0043] 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 ETC recording points located at the entrance side of the corresponding service areas and the closest distance; the information collection module is used to collect the end time recorded by the ETC recording point in the current network node and the starting time recorded by the ETC recording point in the previous network node of the traveling vehicle to obtain the travel time information of the traveling vehicle; the classification processing module is used to perform linear classification processing on the travel time information of multiple traveling vehicles, and screen out the traveling vehicles that have not stopped in the service area in the previous network node as target vehicles; the time calling module is used to obtain the target vehicle at The start time recorded by the ETC recording point in the starting network node, where the starting network node is the network node farthest from the current network node during the continuous driving phase of the target vehicle; a fatigue analysis module, 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 determine the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving phase; an instruction generation module, used 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; a vehicle control module, used to transmit the guidance instruction to the corresponding target vehicle to guide the target vehicle to enter the service area in the current network node for rest.

[0044] 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, the traffic flow control method based on in-transit information as described in Example 1 is implemented.

[0045] The present invention also records a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the traffic flow control method based on in-transit information as described in Example 1.

[0046] 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 adjacent traveling vehicles. Without adding hardware equipment, the present invention can batch screen out traveling vehicles that have not stopped in the service area in the previous network node as target vehicles, and generate guidance instructions to promptly guide the target vehicles to the service area in the current network node for rest. It can comprehensively and timely control and manage the traffic flow on the highway, thereby reducing the probability of traffic accidents.

[0047] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0048] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0051] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The traffic flow control method based on on-the-way information is characterized by: The following steps are involved: A control network is constructed based on the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the ETC recording points located at the entrance side of the corresponding service areas and the closest distance. 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; Performing linear classification processing on the travel time information of multiple vehicles, and selecting the vehicles that have not stopped in the service area of ​​the previous network node as target vehicles; Obtain the start time recorded by the ETC record 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 phase 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 determine the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving stage; Generate guidance instructions for target vehicles whose fatigue parameters are greater than or equal to the parameter threshold configured in the service area in the current network node; The guidance instruction is transmitted to the corresponding target vehicle to guide the target vehicle to drive into the service area in the current network node for rest.

2. The method for controlling traffic flow based on in-transit information according to claim 1 is characterized in that: The linear classification process is performed on the travel time information of multiple traveling vehicles to select the traveling vehicles that have not stopped in the service area of ​​the previous network node as the target vehicles, which specifically includes the following steps: The actual travel time of the corresponding vehicle is calculated based on the difference between the end time and the starting time of the vehicle; A time-discrete graph is established with the starting time as the horizontal axis and the actual travel time as the vertical axis; Set the linear function T=a, where T is the actual driving time and a is a constant value; Taking the minimum average discrete degree of all discrete points in the time discrete graph to the linear function as the optimization goal, the optimal linear function is solved; The moving vehicle corresponding to the discrete point below the optimal linear function is the target vehicle.

3. The traffic flow control method based on in-transit information according to claim 1 is characterized in that: The method further includes: Dynamically update the time discrete graph based on the travel time information of the traveling vehicles collected in real time; When the newly added time width corresponding to the dynamic update of the time discrete graph reaches the set time sliding window width, linear classification processing is performed on the area corresponding to the newly added time width in the time discrete graph.

4. The traffic flow control method based on in-transit information according to claim 1 is characterized in that: The fatigue parameter of the target vehicle is determined by the continuous driving time of the target vehicle and the 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 mileage corresponding to the corresponding continuous driving stage; When the 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 method for controlling traffic flow based on in-transit information according to claim 4 is characterized in that: The calculation expression of the fatigue parameter of the target vehicle is specifically: ； in, represents the fatigue parameter of the target vehicle; Indicates the continuous driving time of the target vehicle; Indicates the 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 mileage and fatigue parameters.

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

7. The method for controlling traffic flow based on in-transit information according to claim 1 is characterized in that: The calculation expression of the parameter threshold is specifically: ； in, Represents a network node The parameter thresholds configured in the service area; Represents a network node The vehicle reception capacity designed for the central service area; Indicates the standard vehicle reception capacity of the service area in the network node; represents the standard fatigue parameter value; represents a natural number; Represents a network node The proportion of remaining vehicles received by the central service area.

8. The traffic flow control system based on in-transit information is characterized by: The system is used to implement the traffic flow control method based on in-transit information as described in any one of claims 1 to 7, including: A network construction module is used to construct a control network based on the distribution information of ETC recording points and service areas. The network nodes in the control network are composed of service areas and the ETC recording points located at the entrance side of the corresponding service area and the closest distance; The information collection module is used to 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; A classification processing module is used to perform linear classification processing on the travel time information of multiple traveling vehicles, and select the traveling vehicles that have not stopped in the service area of ​​the previous network node as target vehicles; The time calling module 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 farthest from the current network node during the continuous driving phase of the target vehicle. A 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 determine the fatigue parameter of the target vehicle in combination with the mileage corresponding to the continuous driving stage; An instruction generation module, used to generate a guidance instruction for a target vehicle whose fatigue parameter is greater than or equal to a parameter threshold configured in the service area in the current network node; The vehicle control module is used to transmit guidance instructions to the corresponding target vehicle to guide the target vehicle to enter the service area in the current network node for rest.

9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the traffic flow control method based on in-transit information as described in any one of claims 1-7 is implemented.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the traffic flow control method based on in-transit information as described in any one of claims 1 to 7.

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