A smart highway collaborative management and control and information push method and system

Abstract

The application discloses a kind of wisdom expressway collaborative management and control and informationization push method and system.The application solves the problem that management and control strategy and dynamic event are disconnected in prior art, and information push lacks customization.Solution includes: obtaining predicted vehicle average speed, and dividing six congestion levels;Obtain real-time traffic event type and location;Based on the generation of collaborative management and control strategy matched with congestion level and event type, including dynamic speed limit, emergency lane opening, differentiated management and control under construction / accident / weather scenario;Through vehicle portrait feature matching and location association, identify vehicles in event influence area;Convert management and control strategy into customized prompt information containing specific distance, lane and vehicle speed, and push to vehicle-mounted or mobile terminal.The application realizes dynamic fine generation of expressway management and control strategy and active push of terminal, effectively improves traffic efficiency and driving safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a method and system for collaborative management and information dissemination of intelligent highways. This application is a divisional application of patent application 2022115714155, filed on December 8, 2022, entitled "A system and method for traffic flow collection, prediction, management and information dissemination based on intelligent highways". Background Technology

[0002] As the social economy develops steadily, the demand for highways in my country is expanding, making management increasingly difficult. Highway congestion occurs frequently, seriously affecting residents' travel needs and even causing serious highway traffic accidents, thus hindering coordinated intercity development.

[0003] Existing technologies have shortcomings in terms of intelligence, connectivity, service provision, and sharing. First, current highway technologies mostly focus on single technological innovations in traffic flow data collection, prediction, control, and customized delivery, without integrating them into a complete framework. Second, existing smart highways only focus on single-scenario implementation, with low levels of coordination between people, vehicles, and roads, and road facilities and roadside facilities operating independently from each other and from each subsystem. Third, the means of disseminating travel information are relatively limited, mostly relying on broadcast announcements or variable message signs, with insufficient utilization of mobile networks and mobile smart terminals. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method for collaborative management and information dissemination of intelligent highways.

[0005] The above-mentioned technical objective of this invention is achieved through the following technical solution: a method for collaborative management and information dissemination of intelligent highways, comprising: Obtain the predicted average vehicle speed at the cross-section of the prediction point; The current traffic congestion level is determined by comparing the predicted average vehicle speed with a preset threshold range. Obtain real-time traffic information for highways, including event types and event locations; Based on traffic congestion level and event type, a corresponding collaborative management and control strategy is generated by matching or calculating. The collaborative management and control strategy includes at least one of speed limit, lane control status and warning distance. Obtain the real-time location information and vehicle identification of the vehicle in motion; The real-time location information of the vehicle is spatially correlated with the location of the event. If the vehicle is located within the influence area corresponding to the location of the event, the collaborative control strategy is transformed into customized prompt information and pushed to the terminal of the vehicle.

[0006] In a preferred embodiment, the present invention can be further configured to: determine the current traffic congestion level based on a comparison between the predicted average vehicle speed and a preset threshold range, specifically including: When the predicted average vehicle speed ∈ [90, +∞), it is determined to be unobstructed; When the predicted average vehicle speed ∈ [65, 90), it is considered to be relatively smooth. When the predicted average vehicle speed ∈ [50, 65), it is determined to be unobstructed; When the predicted average vehicle speed ∈ [40, 50), it is considered relatively congested; When the predicted average vehicle speed ∈ [20, 40), it is considered congested; When the predicted average vehicle speed ∈ [0, 20), it is considered a severe traffic jam.

[0007] In a preferred embodiment, the present invention can be further configured such that: the collaborative control strategy includes a refined dynamic speed control strategy, wherein the refined dynamic speed control strategy is as follows: When the traffic congestion level is lower than that of smooth traffic, an instruction is generated to update the upstream speed limit value so that the difference between the speed limit values ​​of adjacent sections within a 1-kilometer range upstream and downstream does not exceed 10 km / h.

[0008] In a preferred embodiment, the present invention can be further configured as follows: the collaborative control strategy includes a dynamic emergency expansion control strategy for emergency lanes, wherein the dynamic emergency expansion control strategy for emergency lanes is as follows: when the traffic congestion level is lower than that of relatively congested, an instruction to open the emergency lane is generated; when the traffic congestion level returns to free flow or above, an instruction to close the emergency lane is generated.

[0009] In a preferred embodiment, the present invention can be further configured as follows: the collaborative management and control strategy includes a traffic management and control strategy for construction events, wherein the traffic management and control strategy for construction events is as follows: when the traffic congestion level is lower than smooth and the event type is construction, an instruction to close the lane in the construction area is generated, an instruction to prompt merging into the adjacent lane is generated, and an instruction to set the speed limit within 300 meters before and after the construction point to 80 km / h is generated.

[0010] In a preferred embodiment, the present invention can be further configured as follows: the collaborative management and control strategy includes a traffic control strategy for traffic accident events, wherein the traffic control strategy for traffic accident events is as follows: when the traffic congestion level is lower than smooth flow and the event type is a traffic accident, an instruction to close the lane in the accident area is generated, and an instruction to issue a speed limit warning 600 meters upstream of the accident point and set the speed limit value to 80 km / h is issued.

[0011] In a preferred embodiment, the present invention can be further configured such that: the collaborative management strategy includes a traffic management strategy for fog events or a traffic management strategy for heavy rainfall events, wherein the traffic management strategy for fog events or heavy rainfall events includes: Calculate the maximum speed limit Vmax based on the minimum safe stopping sight distance model; The minimum safe parking sight distance model is: , Where S is the minimum safe stopping sight distance, corresponding to the measured visibility value; V is the driving speed; t1 is the driver's reaction time; t2 is the braking hysteresis time; L is the braking coefficient; g is the gravitational acceleration; u is the coefficient of friction between the wheel and the road surface; and i is the longitudinal slope of the road segment. The maximum speed limit Vmax is calculated using the following formula: .

[0012] In a preferred embodiment, the present invention can be further configured to: obtain the vehicle identifier of a moving vehicle, specifically including: Vehicle profile features are collected by roadside sensing devices. These features include license plate number, vehicle type, vehicle color, and vehicle weight. Receive vehicle information uploaded by the vehicle's terminal; The uploaded vehicle information is matched with the vehicle profile features, and the vehicle identification is confirmed after the verification is successful.

[0013] This invention also discloses a smart highway collaborative management and information dissemination system for implementing the aforementioned method. The system includes: Traffic condition perception unit is used to obtain the predicted average vehicle speed at the predicted point cross section and real-time traffic information of the highway. The strategy decision-making unit, connected to the traffic condition perception unit, is used to classify traffic congestion levels based on the predicted average vehicle speed, and to match or calculate corresponding collaborative control strategies based on the traffic congestion level and the event type in real-time road condition information. The vehicle matching unit is used to obtain the real-time location information and vehicle identification of the driving vehicle, and to spatially associate the real-time location information with the event location; The information push unit connects the strategy decision-making unit and the vehicle matching unit. It is used to transform the collaborative control strategy into customized prompt information and push customized prompt information to the terminals of successfully associated driving vehicles. The linkage mechanism includes an axial sliding sleeve sleeved outside the insulation section. The lower end of the sliding sleeve is in contact with the phase change heat absorption medium or connected through a push rod. The upper end of the sliding sleeve is connected to the heat dissipation fins of the condensation section through a connecting rod.

[0014] In a preferred embodiment, the present invention can be further configured such that the strategy decision unit includes: The system includes sub-modules for refined dynamic speed control, dynamic emergency expansion control of emergency lanes, traffic control for construction events, traffic control for traffic accidents, traffic control for heavy fog events, and traffic control for heavy rainfall events.

[0015] The beneficial effects of this invention are as follows: This invention uses an SSA-optimized LSTM vehicle speed prediction model to predict average vehicle speed, determines congestion at prediction points, and formulates collaborative management strategies based on congestion and real-time highway traffic conditions. It matches the real-time highway conditions of the vehicle's location with the collaborative management strategies and sends corresponding prompts to the vehicle terminal. This invention sends customized management measures to the vehicle terminal, meeting various response needs during vehicle travel for different traffic scenarios such as congestion, construction, traffic accidents, fog, and heavy rainfall. This invention simultaneously achieves traffic flow data collection, prediction, management, and customized push notifications, and is not simply a superposition of these functions. This invention effectively predicts large-scale traffic flow, helping traffic management departments provide decision-making basis and suggestions, and provides customized push services to drivers based on vehicle information, thereby alleviating highway traffic congestion and improving intercity travel efficiency. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the traffic flow collection, prediction, control, and information dissemination process based on intelligent highways as described in this invention. Figure 2 The structural block diagram of the traffic flow collection, prediction, control and information push system based on smart highways described in the invention; Figure 3 A diagram showing the relationship between predicted traffic flow on highways and traffic flow on upstream sections. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The division of modules in this application is a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the indirect coupling or communication connection between modules may be electrical or other similar forms, which are not limited in this application.

[0019] like Figure 2As shown, the present invention discloses a traffic flow collection, prediction, control and information push system based on smart highways, specifically including a highway vehicle information perception module, a cross-sectional traffic flow prediction module and a highway active collaborative control module, and the highway vehicle information perception module, the cross-sectional traffic flow prediction module and the highway active collaborative control module all communicate with the smart highway cloud system.

[0020] The highway vehicle information perception module is used to collect vehicle profile features of each vehicle on the highway. The highway vehicle information perception module can be a high-definition camera, radar or other sensing device. In this embodiment, a high-definition camera is used and is installed at the entrance and exit of each ramp.

[0021] The cross-sectional traffic flow prediction module is used to predict the congestion situation of any cross-section at any prediction point.

[0022] The highway proactive collaborative management and control module includes a refined dynamic speed control submodule, an emergency lane dynamic emergency expansion management submodule, a construction event traffic management submodule, a traffic accident event traffic management submodule, a fog event traffic management submodule, and a heavy rainfall event traffic management submodule. Each module formulates a management and control strategy based on the specific road conditions.

[0023] like Figure 1 As shown, the present invention provides a method for traffic flow collection, prediction, control, and information dissemination based on intelligent highways, which specifically includes the following steps: S1, the highway vehicle information perception module collects vehicle profile features of each vehicle on the highway and uploads them to the intelligent highway cloud system. On S101, there are two ways for vehicles to enter the expressway: one is to pay the toll through the manual lane when passing through the toll station, and the car will pick up a toll card at the entrance toll station; the other is for vehicles equipped with ETC, which can directly enter the expressway through the ETC lane. By using these two ways for vehicles to enter the expressway, it can be ensured that all vehicle data on the expressway is collected.

[0024] S102, the high-definition camera at the ramp entrance collects the license plate number Car_number, vehicle type Car_type, vehicle color Car_colour, and vehicle weight Car_weight of two types of vehicles, and records the vehicle profile features C={license:Car_number;type:Car_type;colour:Car_colour;weight:Car_weight}.

[0025] S2, the cross-sectional traffic flow prediction module, predicts the congestion situation at any prediction point cross section and stores the prediction status in the intelligent highway cloud system. S201, such as Figure 3 As shown, high-precision cameras at the ramp entrances and upstream main road sections record the upstream traffic flow from the predicted point on the highway. This includes two sources: upstream ramp traffic flow and upstream main road traffic flow. The current traffic flow Ro at the predicted point is then obtained as follows: R o =R MAIN +∑ RENTi -∑R EXITj (1) Among them, R o To predict the flow rate at the current moment, R MAIN ΣR represents the current flow rate of the upstream main road segment. ENTi ΣR represents the inflow of all upstream inlet ramps at the current moment. EXITj Let i represent the outflow of all upstream exit ramps at the current moment, i represent the number of upstream inlet ramps, and j represent the number of upstream exit ramps.

[0026] S202, the average vehicle speed at the current moment of the prediction point is recorded by millimeter-wave radar and denoted as S0; Table 1 shows the traffic flow and average vehicle speed at the current time of a prediction point on a certain highway selected in this embodiment during a certain time period:

[0027] S203, a vehicle speed prediction model based on the Sparrow Search Algorithm (SSA) and optimized from a Long Short Term Memory (LSTM) network, is given by inputting the current traffic flow R. o Given the current average vehicle speed S0, predict the average vehicle speed at the prediction point at the next time (the next time after the current time) to obtain the predicted average vehicle speed S'0. SSA optimizes the LSTM vehicle speed prediction model as follows: (1) Data processing: The current traffic flow and average vehicle speed data are divided into a test set and a training set in a 4:1 ratio; (2) Model initialization: Initialize the parameters of the SSA algorithm, including the sparrow population location and the maximum number of iterations; use the number of hidden nodes of LSTM and the learning rate as the optimization objectives of the SSA algorithm; (3) Initial calculation: Based on the parameters of the initial SSA algorithm, calculate and sort the fitness values ​​of the initial sparrow population, and find the best and worst fitness values. (4) Iterative calculation: Based on the best and worst fitness values ​​obtained from the initial calculation, update the positions of the discoverer, follower and watcher; recalculate the current best value. If it is better than the best value of the previous iteration, perform the update operation again. Otherwise, do not update; continue iterating until the conditions are met, and finally obtain the globally optimal fitness value and the globally optimal solution value. (5) Construct the updated LSTM model: Construct an LSTM model using the optimal number of hidden layer nodes and learning rate obtained from the SSA algorithm, train it using the training set, and then test it using the test set to obtain the vehicle speed prediction model. (6) Model results: The vehicle speed prediction model takes the current flow rate Ro and the current average vehicle speed S0 as input and obtains the predicted average vehicle speed S'0.

[0028] To verify the accuracy of the vehicle speed prediction model based on SSA-optimized LSTM, two metrics, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to evaluate the prediction results of the SSA-optimized LSTM vehicle speed prediction model. Specifically: RMSE refers to the square root of the average of the squared deviations between the predicted and actual values, and is used to measure the deviation between the predicted and actual values. (3) (4) RMSE refers to the square root of the average of the squared deviations between the predicted and actual values, used to measure the deviation between the predicted and actual values. MAE refers to the average of the absolute deviations between the predicted and actual values, which can prevent errors from canceling each other out. m is the size of the training set. For the predicted vehicle speed, y i This is the actual vehicle speed data.

[0029] The predicted average vehicle speed and the current average vehicle speed (respectively) will be used as... Substituting the values ​​of into formulas (3) and (4), we obtain an RMSE value of 14.562 and an MAE value of 7.324, which shows that the model prediction accuracy is good.

[0030] S204 uses the predicted average vehicle speed S'0 to determine the congestion situation at the prediction point. Combined with the basic service level analysis indicators and grading standards for highways, the traffic congestion situation is divided into 6 levels as shown in Table 2:

[0031] S205, the cross-sectional traffic flow prediction module stores the congestion situation of the predicted cross-section to the smart highway cloud system.

[0032] S3, based on congestion conditions and real-time highway traffic conditions, formulates collaborative management strategies. (1) Refined dynamic speed control When the congestion level is lower than smooth (relatively congested, congested, or severely congested), the intelligent highway cloud system sends an instruction to the vehicle terminal to update the upstream speed limit value, so that the difference between the speed limit values ​​within 1 kilometer between the upstream and downstream is no more than 10 km / h.

[0033] (2) Dynamic emergency expansion and control of emergency lanes When the congestion level is lower than that of moderate congestion (congested or severe congestion), the intelligent highway cloud system controls the opening of the emergency lane. If the congestion level returns to normal, the emergency lane will be closed immediately.

[0034] (3) Traffic control for construction-related incidents When the congestion level is lower than smooth (relatively congested, congested, or severely congested), and there is a construction event on that section of road, the intelligent highway cloud system will send dynamic lane control and variable speed limit policy control instructions to the vehicle terminal. The dynamic lane control specifically involves closing the lane in the construction area and reminding vehicles in that lane to merge into other lanes. The variable speed limit policy specifically involves setting the speed limit for 300 meters before and after the construction site to 80 km / h and providing a notification for the closed lanes in the construction section.

[0035] (4) Traffic control for traffic accident incidents When the congestion level is lower than that of "smooth traffic" (relatively congested, congested, or severely congested), and there is a traffic accident on that section of road, the intelligent highway cloud system sends dynamic lane control, variable speed limit strategies, and emergency lane opening instructions to the vehicle terminals. Dynamic lane control specifically involves closing the lane in the accident area and reminding vehicles in that lane to merge into other lanes. The variable speed limit strategy involves sending a warning message to vehicles 600 meters upstream of the disabled vehicle, setting the speed limit to 80 km / h. When the congestion level at the predicted point cross-section is lower than that of "relatively congested" (congested or severely congested), the emergency lane is opened, with a speed limit of 60 km / h. If the congestion level at the predicted point cross-section returns to "smooth traffic," the emergency lane is immediately closed.

[0036] (5) Traffic control during fog events: The speed limit for fog scenarios is determined based on the minimum safe stopping sight distance. The specific calculation method is as follows: Minimum safe stopping sight distance: S = S1 + S2 + S3 (5) In the formula: S is the minimum safe stopping sight distance (m); S1 is the distance traveled within the reaction time (m); S2 is the braking distance (m); S3 is the safe distance (m); Specifically: (6) In the formula: V is the vehicle speed (km / h), t1 is the driver's reaction time (s), t2 is the braking system hysteresis time (s), usually (t1+t2) is selected as 2.5s; L is the braking coefficient, generally selected between 1.2 and 1.4, in this embodiment 1.3 is selected; g is the gravitational acceleration, taken as 9.8m / s². 2 u is the coefficient of friction between the wheel and the road surface, which is 0.30 to 0.44 when the road surface is wet; i is the longitudinal slope of the road section (%), which is positive for uphill and negative for downhill; for safety reasons, S3 = 5m is selected.

[0037] In general, actual visibility measurements are used as an important indicator of safe stopping sight distance. The visibility index is used to calculate the reasonable driving speed under corresponding weather conditions, thereby regulating the maximum driving speed on highways. (7) In the formula, Vmax is the maximum speed limit when the visibility is S, the coefficient of friction between the wheel and the road surface is u, and the longitudinal slope of the road segment is i (S and i are taken as the maximum values ​​in the calculation).

[0038] Taking a coefficient u = 0.4, and combining it with the classified fog weather levels (reflected in the minimum safe stopping sight distance) and the longitudinal slope of the road section, after rounding and consolidation, the safe driving speed limits for highways under different fog weather conditions can be obtained, as shown in the table below:

[0039] (6) Traffic control during heavy rainfall events The classification of heavy rainfall scenarios and their corresponding visibility levels are shown in the table below:

[0040] Based on the methods used in traffic control during fog events, the maximum safe driving speed for different visibility levels (specific values ​​are considered as the minimum safe stopping sight distance) and slopes in rainy weather can be calculated. In rainy weather, a coefficient u = 0.35 is used. After rounding and consolidation, the speed limits for safe driving on highways under different levels of rainy weather conditions can be obtained, as shown in the table below:

[0041] S4 sends customized navigation information to the vehicle based on the real-time conditions of the highway where the vehicle is located.

[0042] After passing the high-definition camera at the entrance ramp, the driver manually uploads the vehicle's license plate number to the intelligent highway cloud system via the vehicle terminal. Once the vehicle information is successfully matched with the collected vehicle profile feature information, the intelligent highway cloud system determines the real-time status of the vehicle on the highway based on the vehicle's GPS information, matches it with the collaborative management strategy in the intelligent highway cloud system, and then sends a corresponding prompt to the vehicle terminal. The vehicle terminal of this invention is a combination of mobile terminal and in-vehicle terminal. The vehicle terminal provides prompts in conjunction with the highway customized information release module. When the vehicle is traveling on a normal road, the vehicle terminal only performs the navigation function and provides warnings for dangerous or fatigued driving. When the congestion level of the road ahead is lower than that of smooth traffic (relatively congested, congested, or severely congested), the vehicle terminal will display: "Congestion ahead () kilometers, speed limit () km / h"; When the congestion level of the road ahead is lower than that of relatively smooth (congested or severely congested), the vehicle terminal will display: "Congestion ahead () kilometers, emergency lane is open in this section, speed limit () km / h"; When there is a construction section 300 meters ahead, the vehicle terminal will display: "Construction is underway 300 meters ahead. Lane () is closed. Vehicles may merge into lane () with a speed limit of 80 km / h." When a traffic accident occurs 600 meters ahead and the congestion level is lower than smooth (relatively congested, congested, or severely congested), the vehicle terminal will display: "A serious accident has occurred 600 meters ahead. Lane () is closed. The speed limit on the main road is 80 km / h." When the congestion level is lower than relatively congested (congested or severely congested), the vehicle terminal will display: "A serious accident has occurred 600 meters ahead. Lane () is closed. The emergency lane can be used for passage. The speed limit on the main road is 80 km / h, and the speed limit on the emergency lane is 60 km / h." When the highway is in foggy weather, the vehicle terminal will display: "Today is foggy, the speed limit on this section of the road is () km / h, please drive carefully." When it is raining on the highway, the vehicle terminal will display the message: "Today is rainy, the speed limit on this section of the road is () km / h, please drive carefully."

[0043] In summary, through theoretical analysis and case studies, this invention provides a complete traffic flow collection, prediction, control, and information delivery system based on smart highways. This system can effectively collect highway traffic flow information, determine highway congestion status, propose smart traffic control measures, and send customized information to mobile terminals to meet control requirements.

[0044] The embodiments described above are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for collaborative management and information dissemination of intelligent highways, characterized in that, include: Obtain the predicted average vehicle speed at the cross-section of the prediction point; The current traffic congestion level is determined based on the comparison between the predicted average vehicle speed and the preset threshold range. Obtain real-time traffic information for highways, including event types and event locations; Based on the traffic congestion level and the event type, a corresponding collaborative management and control strategy is generated by matching or calculating. The collaborative management and control strategy includes at least one of speed limit, lane control status and warning distance. Obtain the real-time location information and vehicle identification of the vehicle in motion; The real-time location information of the vehicle is spatially correlated with the location of the event. If the vehicle is located within the influence area corresponding to the location of the event, the collaborative control strategy is transformed into customized prompt information and the customized prompt information is pushed to the terminal of the vehicle.

2. The intelligent highway collaborative management and information dissemination method according to claim 1, characterized in that, The step of determining the current traffic congestion level based on the comparison result between the predicted average vehicle speed and the preset threshold range specifically includes: When the predicted average vehicle speed ∈ [90, +∞), it is determined to be unobstructed; When the predicted average vehicle speed ∈ [65, 90), it is determined to be relatively smooth. When the predicted average vehicle speed ∈ [50, 65), it is determined to be unobstructed; When the predicted average vehicle speed ∈ [40, 50), it is determined to be relatively congested; When the predicted average vehicle speed ∈ [20, 40), it is determined to be congested; When the predicted average vehicle speed ∈ [0, 20), it is determined to be a severe traffic jam.

3. The intelligent highway collaborative management and information dissemination method according to claim 2, characterized in that, The collaborative control strategy includes a refined dynamic speed control strategy, which is as follows: When the traffic congestion level is lower than that of smooth traffic, an instruction is generated to update the upstream speed limit value so that the difference between the speed limit values ​​of adjacent sections within a 1-kilometer range upstream and downstream does not exceed 10 km / h.

4. The intelligent highway collaborative management and information dissemination method according to claim 2, characterized in that, The collaborative management and control strategy includes a dynamic emergency expansion management and control strategy for emergency lanes. The dynamic emergency expansion management and control strategy for emergency lanes is as follows: when the traffic congestion level is lower than that of relatively congested, an instruction to open the emergency lane is generated; when the traffic congestion level returns to free flow or above, an instruction to close the emergency lane is generated.

5. The intelligent highway collaborative management and information dissemination method according to claim 2, characterized in that, The collaborative management and control strategy includes a traffic management strategy for construction-related events. The traffic management strategy for construction-related events is as follows: when the traffic congestion level is lower than smooth and the event type is construction, an instruction is generated to close the lanes in the construction area, an instruction is given to merge into the adjacent lanes, and an instruction is given to set the speed limit within 300 meters before and after the construction point to 80 km / h.

6. The intelligent highway collaborative management and information dissemination method according to claim 2, characterized in that, The collaborative management and control strategy includes a traffic control strategy for traffic accident events. The traffic control strategy for traffic accident events is as follows: when the traffic congestion level is lower than smooth flow and the event type is a traffic accident, an instruction is generated to close the lanes in the accident area, and an instruction is issued to issue a speed limit warning 600 meters upstream of the accident point and set the speed limit value to 80 km / h.

7. The intelligent highway collaborative management and information dissemination method according to claim 1, characterized in that, The coordinated management and control strategy includes traffic control strategies for fog events or heavy rainfall events. The traffic control strategies for fog events or heavy rainfall events include: Calculate the maximum speed limit Vmax based on the minimum safe stopping sight distance model; The minimum safe parking sight distance model is as follows: ， Where S is the minimum safe stopping sight distance, corresponding to the measured visibility value; V is the driving speed; t1 is the driver's reaction time; t2 is the braking hysteresis time; L is the braking coefficient; g is the gravitational acceleration; u is the coefficient of friction between the wheel and the road surface; and i is the longitudinal slope of the road segment. The maximum speed limit Vmax is calculated using the following formula: 。 8. The intelligent highway collaborative management and information dissemination method according to claim 1, characterized in that, The acquisition of the vehicle identification number of the driving vehicle specifically includes: Vehicle profile features of passing vehicles are collected through roadside sensing devices. These features include license plate number, vehicle type, vehicle color, and vehicle weight. Receive vehicle information uploaded by the vehicle's terminal; The uploaded vehicle information is matched with the vehicle profile features, and the vehicle identification is confirmed after successful verification.

9. A smart highway collaborative management and information dissemination system, characterized in that, The system for implementing the method according to any one of claims 1 to 8, the system comprising: Traffic condition perception unit is used to obtain the predicted average vehicle speed at the predicted point cross section and real-time traffic information of the highway. The strategy decision unit, connected to the traffic state perception unit, is used to classify traffic congestion levels based on the predicted average vehicle speed, and to match or calculate corresponding collaborative management and control strategies based on the traffic congestion levels and the event types in the real-time traffic information. The vehicle matching unit is used to acquire the real-time location information and vehicle identification of the driving vehicle, and to spatially associate the real-time location information with the event location; The information push unit, which connects the strategy decision unit and the vehicle matching unit, is used to convert the collaborative management and control strategy into customized prompt information and push the customized prompt information to the terminals of successfully associated driving vehicles.

10. The intelligent highway collaborative management and information push system according to claim 9, characterized in that, The strategy decision-making unit includes: The system includes sub-modules for refined dynamic speed control, dynamic emergency expansion control of emergency lanes, traffic control for construction events, traffic control for traffic accidents, traffic control for heavy fog events, and traffic control for heavy rainfall events.

Images