Urban traffic jam relieving method and system driven by big data of Internet of Vehicles

Abstract

The invention relates to the technical field of urban traffic, and provides an Internet of Vehicles big data-driven urban traffic congestion relieving method and an Internet of Vehicles big data-driven urban traffic congestion relieving system. The method comprises the steps of collecting road network distribution of a target area, performing road congestion association identification based on historical congestion records, and establishing a congestion association network; monitoring traffic flow data of each road through the Internet of Vehicles, identifying a congested lane and generating a congestion degree identifier; extracting associated lanes in the crowded associated network, and identifying signal lamps and reversible lane distribution; and a traffic flow guiding network is established, and regulation and optimization are executed in combination with the crowdedness degree, the signal lamps and the reversible lane distribution. The technical problem of low congestion diffusion and relieving efficiency caused by the fact that a traditional urban traffic management method cannot effectively recognize the congestion association between roads is solved, and the purpose that the traffic congestion association network driven by the big data of the Internet of Vehicles is combined with the dynamic regulation and control of the signal lamps and the reversible lanes is achieved. The technical effects of accurately identifying and efficiently relieving the urban road network congestion and improving the road passing efficiency are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban traffic, in particular to a vehicle networking big data driven urban traffic congestion relief method and system. BACKGROUND

[0002] Under the background of accelerating modern urbanization, urban road traffic demand is growing rapidly, and the number of motor vehicles is rising continuously. Traffic congestion has become a prominent problem that restricts the sustainable development of cities. Traditional traffic control methods mainly rely on manual experience or fixed time signal timing strategies, which lack real-time perception and dynamic regulation of complex traffic flow changes, and are difficult to adapt to changing road network environments and sudden congestion propagation trends. Especially during peak hours or special events, congestion in local sections can easily spread to surrounding roads through road network structures, leading to regional or even global traffic paralysis. With the development of vehicle networking, Internet of Things and big data technologies, vehicles can achieve interconnection and intercommunication with road infrastructure through sensors and communication devices, and massive traffic data can be collected and analyzed in real time. However, existing big data-based traffic management research mostly focuses on congestion detection or signal optimization for single-point roads, lacks identification of overall road network congestion propagation, and is difficult to effectively coordinate and regulate signal lights and tidal lane scheduling for associated roads, resulting in low traffic resource utilization efficiency and limited congestion relief effect. SUMMARY

[0003] The present application provides a vehicle networking big data driven urban traffic congestion relief method and system, aiming to solve the technical problem that traditional urban traffic management methods cannot effectively identify congestion correlation between roads, leading to low congestion diffusion and relief efficiency.

[0004] The first aspect of the present application provides a vehicle networking big data driven urban traffic congestion relief method, which comprises: collecting the distribution of urban road network in the target area, identifying the congestion correlation of each road based on historical congestion records, and establishing a congestion correlation network; monitoring the traffic data of each road of the urban road network distribution through a vehicle networking architecture, identifying the congestion lane and generating a congestion degree identifier; extracting the associated lane of the congestion lane in the congestion correlation network, identifying the signal light distribution and tidal lane distribution in the congestion lane and the associated lane; establishing a traffic flow guidance network for the congestion lane and the associated lane, combining the congestion degree identifier and the signal light distribution and tidal lane distribution, and performing signal light and tidal lane regulation optimization.

[0005] In another aspect of the present application, a vehicle networking big data driven urban traffic congestion mitigation system is provided, which comprises: a congestion correlation identification module: collecting urban road network distribution in a target area, identifying congestion correlation of each road based on historical congestion records, and establishing a congestion correlation network; a traffic flow monitoring module: monitoring traffic flow data of each road of the urban road network distribution through a vehicle networking architecture, identifying a congestion lane and generating a congestion degree identifier; a lane identification module: extracting a correlation lane of the congestion lane in the congestion correlation network, and identifying signal light distribution and tidal lane distribution in the congestion lane and the correlation lane; and a regulation and optimization module: establishing a traffic flow guidance network of the congestion lane and the correlation lane, and performing regulation and optimization of signal lights and tidal lanes in combination with the congestion degree identifier and the signal light distribution and the tidal lane distribution.

[0006] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] The above-mentioned vehicle networking big data driven urban traffic congestion mitigation method first acquires urban road network information of a target area, identifies congestion correlation between roads in combination with historical congestion data, and establishes a congestion correlation network. Then, real-time monitoring of traffic flow of each road is performed by using vehicle networking, a congestion lane is determined and its congestion degree is marked. Subsequently, roads associated with the congestion lane are found out from the congestion correlation network, and signal light distribution and tidal lane conditions on these roads are identified. Finally, a traffic flow guidance network of the congestion lane and its associated lanes is constructed, and dynamic regulation and optimization of signal lights and tidal lanes are implemented in combination with congestion degree, signal light control and tidal lane distribution, so as to mitigate traffic congestion.

[0008] The above description is only a summary of the technical solutions of the present application. In order to enable the technical means of the present application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, features and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0010] Figure 1 FIG. 1 is a flowchart of a vehicle networking big data driven urban traffic congestion mitigation method in an embodiment.

[0011] Figure 2This is a diagram of an urban traffic congestion mitigation system architecture driven by vehicle-to-everything (V2X) big data in one embodiment.

[0012] Figure labeling: 11. Congestion association identification module; 12. Traffic flow monitoring module; 13. Lane identification module; 14. Control optimization module. Detailed Implementation

[0013] This application provides a method and system for alleviating urban traffic congestion driven by vehicle-to-everything (V2X) big data. This addresses the technical problem that traditional urban traffic management methods cannot effectively identify congestion correlations between roads, leading to congestion spread and low alleviation efficiency.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0016] Example 1, as Figure 1 As shown, this application provides a method for alleviating urban traffic congestion driven by vehicle-to-everything (V2X) big data, the method comprising:

[0017] Collect data on the urban road network distribution within the target area, identify congestion correlations for each road based on historical congestion records, and establish a congestion correlation network.

[0018] In this embodiment, the overall road network distribution within the target area is first comprehensively collected, including basic information such as road geometry, intersection locations, road grades, and the number of lanes, to construct a complete urban road network distribution. Then, for each road in the urban road network, congestion correlation is identified based on its corresponding historical traffic operation data, and the identification results are compared with a preset correlation degree. When the identification results between two roads exceed this preset correlation degree, they are determined to have a significant congestion correlation. Subsequently, these roads with congestion correlation are aggregated, and corresponding nodes and edges are set to establish the final congestion correlation network. This congestion correlation network can clearly reflect the dependence and influence of roads within the target area on congestion propagation, providing a foundation for subsequent real-time monitoring and dynamic control.

[0019] Further, the application provides collecting urban road network distribution in a target area, identifying congestion correlation of each road based on historical congestion records, and establishing congestion correlation network, including:

[0020] extracting a first congestion record of a first road in the historical congestion records; analyzing congestion propagation relationship based on the first congestion record, identifying associated road distribution with congestion propagation correlation degree greater than a preset correlation degree based on the urban road network distribution, and establishing a first congestion correlation network of the first road; and adding the first congestion correlation network into the congestion correlation network.

[0021] Preferably, first, historical congestion records in a target area in urban road network distribution are extracted from a historical congestion record database, and then corresponding congestion data is extracted from the historical congestion records as a first congestion record according to a unique ID of the first road in each road, which usually includes information such as time period, duration, road operating state parameters (such as speed, flow, density), and congestion range. Subsequently, the first congestion record is used to analyze congestion propagation effect of the first road in the actual road network, and in this process, other roads connected to or potentially affected by the first road in the road network are analyzed one by one based on the collected urban road network distribution, and congestion propagation correlation between them and the first road is calculated, which can be measured by time lag correlation, flow fluctuation consistency, etc. When the congestion propagation correlation of a road exceeds a preset correlation degree, it is determined that the road has a congestion propagation relationship with the first road, and at this time, the road is included in the associated road distribution. Then, according to each associated road in the associated road distribution, corresponding nodes and edges are set, and a first congestion correlation network corresponding to the first road is formed according to the nodes and edges, which is composed of the first road and all associated roads meeting the conditions. Finally, the first congestion correlation network is integrated into the congestion correlation network of the entire target area, and the above steps are repeatedly performed for other roads to gradually improve the congestion correlation network of the entire target area, thereby providing reliable data support for global congestion monitoring and mitigation strategies.

[0022] Further, the application provides analyzing congestion propagation relationship based on the first congestion record, identifying associated road distribution with congestion propagation correlation degree greater than a preset correlation degree based on the urban road network distribution, including:

[0023] The urban road network distribution is abstracted into a graph structure to generate a road network structure, wherein intersections, starting points and ending points of roads are nodes, and road segments are edges; the first congestion record is mapped to the road network structure, and time lag correlation of the remaining roads in the road network structure and the first road is analyzed, and a time lag correlation index is used as a congestion propagation correlation degree; according to the time lag correlation analysis result, roads with a congestion propagation correlation degree greater than a preset correlation degree are used to establish the associated road distribution.

[0024] Optionally, the collected urban road network distribution is first subjected to structured abstraction processing, that is, intersections, starting points and ending points of roads in the urban road network distribution are all abstracted into nodes in a graph structure, and road segments between different nodes are abstracted into edges, thereby generating a complete road network structure. Subsequently, the historical congestion record of the first road is mapped to the corresponding road edge as a starting reference object for analysis, and the remaining roads in the road network structure are sequentially subjected to time lag correlation analysis. In this process, by comparing the traffic state of the first road at a certain time with the traffic state of other roads at a plurality of time lags, a correlation coefficient is calculated, and the maximum value of the correlation coefficient is found by adjusting the lag time, and the maximum value is used as a time lag correlation index to reflect the closeness of the congestion state of the first road to the other roads. Finally, the calculated time lag correlation index is used as a congestion propagation correlation degree, roads with a congestion propagation correlation degree greater than a preset correlation degree are screened out, and the roads are determined to have a significant dependence on the congestion propagation of the first road, and the roads are included in the associated road distribution of the first road, thereby providing a data basis for subsequent establishment of a congestion association network.

[0025] Further, the time lag correlation analysis calculates the correlation between the state of the first road at time t and the state of the remaining roads at time t+Δt, adjusts the time lag Δt, screens the correlation coefficient corresponding to the maximum correlation, and generates a time lag correlation index.

[0026] Optionally, when performing the time lag correlation analysis, the traffic operation state data of the first road in the continuous time period, such as vehicle speed, traffic flow, lane occupancy, etc., is first obtained, and these state parameters are arranged in the form of time series. At the same time, the corresponding state parameters of the remaining roads in the road network in the same time range are extracted to form multiple groups of comparable time series data. Then, for these operation state data, each data is subtracted from the corresponding mean value, and then divided by the corresponding standard deviation to adjust the operation state data to the same dimension. Then, taking the state data of the first road at time t as the reference, select the state data of a remaining road at time t+Δt for Pearson correlation calculation, wherein Δt is the time lag parameter, which can be set to 1 minute to 30 minutes, and the adjustment step can be two minutes. Specifically, it can be set according to actual needs. For example, when the first road extracts data at t, t1, t2, and Δt is one time unit, the data of another road is t1, t2, t3, by calculating the Pearson correlation of the two groups of data, the correlation coefficient of the two roads can be obtained. Then, the time lag parameter is iteratively adjusted, and the corresponding correlation coefficients are calculated to measure the response degree of the remaining road to the congestion state of the first road at different time delays. Then, the maximum value of the correlation coefficient is selected from all calculation results, and this maximum correlation coefficient represents the congestion propagation correlation strength between the road and the first road. Finally, the maximum correlation coefficient is defined as the time lag correlation index, which is used as the basis for judging the congestion propagation correlation degree to determine which roads in the remaining roads have a congestion propagation relationship with the first road, and accurately reveal the time sequence and propagation of the congestion state between roads.

[0027] Table 1: Example of operation state data

[0028]

[0029] As above, Table 1 is an example of operation state data, which shows the operation state data of Road A and Road B in the same time period, including vehicle speed, traffic flow, and lane occupancy, providing data support for subsequent time lag correlation analysis.

[0030] Through the vehicle networking architecture, the traffic data of each road in the city road network distribution is monitored, the congestion lane is identified, and the congestion degree identifier is generated.

[0031] In one embodiment, first rely on the Internet of Vehicles architecture, the real-time traffic flow data of each road in the target area of the city road network is collected. The vehicle uploads its running information (including instantaneous speed, acceleration, position, direction of travel and traffic flow data, etc.) to the Internet of Vehicles platform through the installed vehicle terminal, GPS positioning device and vehicle-road cooperation communication module. At the same time, the road side monitoring equipment such as video detector, ground magnetic inductor, road side unit RSU will also synchronously collect the traffic flow state to form the traffic flow data of each road. Then, the collected traffic flow data is analyzed in real time, and the speed threshold and vehicle density threshold are used as the basis for judgment to determine whether a road is in a congested state, for example, when the average speed is <30km / h and the density is ≥30 vehicles / km, it means that the road is in a congested state, at this time, the road will be determined as a congested lane. Then, according to the specific values of the average speed and the vehicle density, the corresponding congestion degree identifier of the congested lane is generated, for example, when 20km / h≤average speed<30km / h and 30 vehicles / km≤density<40 vehicles / km, it is light congestion; when 10km / h≤average speed<20km / h and 40 vehicles / km≤density<60 vehicles / km, it is moderate congestion; when average speed<10km / h and 60 vehicles / km≤density, it is heavy congestion. Through the above steps, not only the automatic identification of the congested lane in the road network can be realized, but also the quantifiable congestion degree basic data for subsequent congestion correlation analysis and control optimization can be provided.

[0032] Extract the associated lane of the congested lane in the congestion correlation network, identify the signal light distribution and the tidal lane distribution in the congested lane and the associated lane.

[0033] In one embodiment, when the congested lane in the target area is identified through the Internet of Vehicles monitoring, the node corresponding to the congested lane in the congestion correlation network is located, and other nodes connected to the node through the congestion propagation relationship are read, so as to obtain the associated lanes that may be affected or have an impact on them. Then, the traffic facility element identification of the extracted congested lane and its associated lanes is carried out in the road infrastructure database, and the signal light distribution and the tidal lane distribution of these lanes are obtained to fully master the signal light control pattern and the tidal lane use of the congested lane and its associated lanes, so as to provide the necessary basic information and environmental parameters for subsequent construction of traffic flow guidance network and execution of dynamic control.

[0034] Establish the traffic flow guidance network of the congested lane and the associated lane, combine the congestion degree identifier and the signal light distribution and the tidal lane distribution, and execute the control optimization of the signal light and the tidal lane.

[0035] In one embodiment, after identifying the congestion lane and its associated lanes, a traffic flow guidance network is established to reflect the vehicle flow direction and connection relationship in the direction of vehicle travel. Subsequently, in this traffic flow guidance network, different signal timing schemes and tidal lane direction adjustments are iteratively simulated in combination with the generated congestion degree identification, signal light distribution, and tidal lane distribution, the improvement degree of congestion relief effect of each scheme is evaluated, a set of optimal signal light and tidal lane joint regulation schemes is determined, and the scheme is applied to the actual road network to realize dynamic regulation and efficient dredging of the congestion lane and its associated lanes.

[0036] Further, the application provides establishing the traffic flow guidance network of the congestion lane and the associated lane, performing signal light and tidal lane regulation optimization in combination with the congestion degree identification, signal light distribution, and tidal lane distribution, including:

[0037] For the congestion lane and the associated lane, lane connection is performed according to traffic flow guidance to generate the traffic flow guidance network; according to the signal light distribution, division is performed according to the control lane group controlled by each signal light to generate a signal light-lane group mapping; according to the tidal lane distribution, the current direction state of each tidal lane is detected; based on the signal light-lane group mapping, the current direction state of each tidal lane, and in combination with the congestion degree identification, regulation simulation optimization is performed to generate a congestion relief scheme; and the signal light and tidal lane regulation is performed with the congestion relief scheme.

[0038] Preferably, for the identified congestion lane and its associated lane, the lane connection is performed according to the actual driving direction of the vehicle on the road network and the reachable path, the lanes are taken as nodes, and the traffic flow guiding relationship is taken as a directed edge, so as to construct a traffic flow guiding network, which can completely reflect the flow rule and traffic connection relationship of the vehicle in the congestion area and its periphery. Subsequently, according to the collected signal lamp distribution, the lanes controlled by each signal lamp are grouped and divided to generate a mapping relationship between the signal lamp and the lane group, so as to clearly define the actual control range of the traffic flow by different signal lamps, and then the state information of the roadside unit, video monitoring camera, geomagnetic sensor and the like at the position of each tidal lane is obtained according to the position of each tidal lane recorded in the tidal lane distribution, so as to detect the current direction state of each tidal lane and identify whether each tidal lane is in forward traffic or reverse traffic. Then, the signal lamp-lane group mapping relationship, the direction state of each tidal lane and the congestion degree identifier of the lane are collectively input into a simulation module to perform control optimization in the digital twin model. In this optimization process, the signal lamp timing parameters and the direction state of the tidal lane are adjusted through iteration, the improvement effect of congestion relief under different schemes is calculated, and according to the calculation result, a congestion relief scheme that can make the congestion degree reduction value reach or exceed the preset threshold is selected, and the congestion relief scheme is issued to the traffic control system to specifically perform timing adjustment of the corresponding signal lamp and switching of the direction of the tidal lane, so as to realize dynamic regulation and efficient dredging of the congestion lane and its associated lane in the target area.

[0039] Further, the application provides that the control priority of the signal lamp is higher than the control priority of the tidal lane during the control simulation optimization.

[0040] Optionally, during the control simulation optimization, a preset control priority strategy needs to be followed, which stipulates that the control priority of the signal lamp is higher than the control priority of the tidal lane, that is, timing optimization of the signal lamp is performed first, and then the direction adjustment of the tidal lane is performed according to the need, so that more efficient traffic congestion relief effect can be achieved under the premise of ensuring traffic safety and control stability.

[0041] Further, the application provides that the control simulation optimization is performed based on the signal lamp-lane group mapping, the current direction state of each tidal lane, and the congestion degree identifier, to generate a congestion relief scheme, which includes:

[0042] The signal light-lane group mapping, the current direction state of each tidal lane, and the congestion degree identifier are used to build a road traffic twin; the minimum green light time constraint of the sidewalk length configuration of each signal light in the signal light distribution is collected; under the minimum green light time constraint, iterative regulation and optimization of the signal light are performed through the road traffic twin to determine a first signal light regulation scheme in which the congestion reduction value meets a preset threshold, and the congestion relief scheme is generated.

[0043] Optionally, first, based on the mapping relationship of the signal light-lane group, the current direction state of each tidal lane, and the congestion degree identifier of the lane, a road traffic twin that can truly reflect the traffic operation of the target area is constructed. This road traffic twin is a digital simulation model that maps the actual road network structure, signal light control logic, tidal lane running direction, and real-time congestion degree to a virtual environment, so that subsequent regulation and optimization can be verified and evaluated in virtual simulation. Subsequently, the signal light distribution in the target area is analyzed, the length information of the pedestrian crosswalk at each signal light intersection is collected, and the length information of the pedestrian crosswalk is divided by the preset average walking speed of pedestrians to obtain the minimum green light time required for pedestrians to safely cross the street. The minimum green light time is used as a rigid constraint condition in the regulation and optimization process to ensure that pedestrian safety is guaranteed under any optimization scheme. Then, under this constraint condition, the road traffic twin is used to iteratively regulate and optimize the signal light by dynamically adjusting the green light time length of each phase, the red-green light switching sequence, and the phase coordination mode, and recording the simulated average vehicle speed, vehicle density, and flow rate after adjustment. The difference between the data that should increase after adjustment, such as the average vehicle speed and flow rate, and the data before adjustment is calculated. For data that should decrease after adjustment, the data before adjustment is subtracted from it. The calculated difference values are then normalized and weighted summed to obtain the congestion reduction value under different regulation schemes. When the congestion reduction value of a certain scheme reaches or exceeds the preset threshold, the scheme is determined as the first signal light regulation scheme, and this first signal light regulation scheme is output as the congestion relief scheme to guide the signal light control in the actual road network, thereby effectively relieving road traffic congestion while ensuring pedestrian safety.

[0044] Further, the present application provides that after the iterative regulation and optimization of the signal light through the road traffic twin, it further includes:

[0045] If the iterative regulation optimization meets the preset convergence condition, the congestion degree reduction value does not meet the preset threshold, a second signal lamp regulation scheme in the convergence is generated, and a diverging lane of the congestion lane is identified in the associated lane; according to the current direction state of each tidal lane, a tidal lane whose direction is opposite to the direction of the traffic flow entering the diverging lane is located; through the road traffic twin, a tidal lane regulation scheme meeting the preset threshold is identified by changing the direction state of the identified tidal lane on the basis of the second signal lamp regulation scheme; and the congestion relief scheme is generated by the second signal lamp regulation scheme and the tidal lane regulation scheme.

[0046] Optionally, when the signal lamp is iteratively regulated and optimized, if it is detected that the optimization process has met the preset convergence condition, such as congestion degree reduction value convergence or maximum iteration number, but the calculated congestion degree reduction value still does not reach the set preset threshold, at this time, the signal lamp regulation result in the convergence is automatically retained as the second signal lamp regulation scheme. Subsequently, the topological relationship between the congestion lane and its associated lane in the established congestion associated network is further analyzed, and a diverging lane capable of sharing part of the traffic pressure is identified, that is, a road capable of providing an alternative driving path for the vehicles of the congestion lane. Then, in combination with the real-time direction state of each tidal lane, a tidal lane whose direction is opposite to the direction of the traffic flow entering the diverging lane is located, and these lanes are identified as controllable objects. Then, based on the road traffic twin, the direction state of the identified tidal lane is changed in sequence for dynamic simulation, and the congestion degree reduction value under different adjustment schemes is evaluated by using the same method as described above. When a scheme can make the congestion degree reduction value reach or exceed the preset threshold, the scheme is determined as an effective tidal lane regulation scheme. Finally, the second signal lamp regulation scheme and the determined tidal lane regulation scheme are combined to generate a complete congestion relief scheme, and the congestion relief scheme is sent to the traffic control system to realize efficient dredging and global optimization of the congestion section.

[0047] Further, the application provides that when the direction state of the identified tidal lane is changed, the lane with the smallest traffic flow is prioritized to start until the congestion degree reduction value meets the preset threshold.

[0048] Optionally, when it is necessary to further alleviate congestion by adjusting the direction of the tidal lane, the direction state of the tidal lane with the smallest traffic flow is preferentially selected for change, because the direction switching of the low-flow lane has less disturbance to the overall traffic operation, and can maximize the efficiency of the shunting under the premise of ensuring traffic safety and stability. Specifically, all identified adjustable tidal lanes are sorted in descending order of real-time traffic flow, and the direction switching is performed from the lane with the smallest traffic flow. The running state after adjustment is simulated in the road traffic twin, and the improvement effect of the congestion reduction value is evaluated. When it is detected that a certain adjustment scheme makes the congestion reduction value reach or exceed the preset threshold, further direction adjustment is stopped, and the scheme is directly identified as the tidal lane control scheme, which together with the signal light control scheme forms the final congestion alleviation scheme. If the congestion reduction value does not meet the preset threshold after the adjustment reaches the maximum iteration number, the adjustment scheme with the largest congestion reduction value in the adjustment process is output as the tidal lane control scheme, which ensures that the relatively optimal alleviation measure can be provided under the existing conditions, and the second signal light control scheme is combined to form a complete congestion alleviation scheme for actual execution.

[0049] In summary, the embodiments of the present application have at least the following technical effects:

[0050] The embodiments of the present application first collect the urban road network distribution in the target area, identify the congestion correlation of each road based on historical congestion records, and establish a congestion correlation network; then, through the Internet of Vehicles architecture, the vehicle flow data of each road of the urban road network distribution is monitored, the congestion lane is identified, and the congestion degree identifier is generated; then, the associated lanes of the congestion lane are extracted in the congestion correlation network, and the signal light distribution and the tidal lane distribution in the congestion lane and the associated lanes are identified; finally, the vehicle flow guidance network of the congestion lane and the associated lanes is established, and the signal light and the tidal lane are controlled and optimized in combination with the congestion degree identifier and the signal light distribution and the tidal lane distribution. These technical effects collectively solve the technical problem that the traditional urban traffic management method cannot effectively identify the congestion correlation between roads, leading to congestion diffusion and low alleviation efficiency, and achieve the technical effects of utilizing the congestion correlation network driven by the Internet of Vehicles big data, dynamically adjusting the signal light and the tidal lane, accurately identifying and efficiently alleviating the congestion of the urban road network, and improving the road traffic efficiency.

[0051] Embodiment two, based on the same inventive concept as the urban traffic congestion alleviation method driven by the Internet of Vehicles big data in the preceding embodiments, like Figure 2As shown, the present application provides a vehicle networking big data driven urban traffic congestion relief system, which comprises: a congestion correlation identification module 11: collecting the urban road network distribution in the target area, identifying the congestion correlation of each road based on the historical congestion records, and establishing a congestion correlation network; a traffic flow monitoring module 12: monitoring the traffic flow data of each road of the urban road network distribution through the vehicle networking architecture, identifying the congestion lane and generating the congestion degree identifier; a lane identification module 13: extracting the associated lane of the congestion lane in the congestion correlation network, identifying the signal light distribution and the tidal lane distribution in the congestion lane and the associated lane; a regulation and optimization module 14: establishing the traffic flow guidance network of the congestion lane and the associated lane, combining the congestion degree identifier and the signal light distribution and the tidal lane distribution, and performing regulation and optimization of the signal light and the tidal lane.

[0052] Further, the congestion correlation identification module 11 is further used to perform the following method:

[0053] In the historical congestion records, the first congestion record of the first road is extracted; the propagation relationship of congestion is analyzed based on the first congestion record, the associated road distribution with the congestion propagation correlation degree greater than the preset correlation degree is identified based on the urban road network distribution, the first congestion correlation network of the first road is established, and the first congestion correlation network is added to the congestion correlation network.

[0054] Further, the congestion correlation identification module 11 is further used to perform the following method:

[0055] The urban road network distribution is abstracted as a graph structure to generate a road network structure, wherein the intersection, the starting point and the ending point of the road are nodes, and the road section is an edge; the first congestion record is mapped to the road network structure, the time lag correlation of the remaining roads and the first road in the road network structure is analyzed, the time lag correlation index is used as the congestion propagation correlation degree, and the road with the congestion propagation correlation degree greater than the preset correlation degree is established according to the time lag correlation analysis result.

[0056] Further, the congestion correlation identification module 11 is further used to perform the following method:

[0057] The time lag correlation analysis calculates the correlation between the state of the first road at time t and the state of the remaining roads at time t+Δt, adjusts the time lag Δt, selects the correlation coefficient corresponding to the maximum correlation value, and generates the time lag correlation index.

[0058] Further, the regulation and optimization module 14 is further used to perform the following method:

[0059] According to the traffic flow guidance, lane connection is performed on the congestion lane and the associated lane to generate the traffic flow guidance network; according to the signal lamp distribution, each signal lamp control is divided into a control lane group to generate a signal lamp-lane group mapping; according to the tidal lane distribution, the current direction state of each tidal lane is detected; based on the signal lamp-lane group mapping, the current direction state of each tidal lane, and the congestion degree identifier, a congestion relief scheme is generated through regulation simulation optimization; and the congestion relief scheme is used to perform regulation of the signal lamp and the tidal lane.

[0060] Further, the regulation optimization module 14 is further configured to perform the following method:

[0061] During the regulation simulation optimization, the regulation priority of the signal lamp is higher than that of the tidal lane.

[0062] Further, the regulation optimization module 14 is further configured to perform the following method:

[0063] The signal lamp-lane group mapping, the current direction state of each tidal lane, and the congestion degree identifier are used to build a road traffic twin; the minimum green light time constraint of each signal lamp in the signal lamp distribution is collected; under the minimum green light time constraint, the road traffic twin is used to perform iterative regulation optimization of the signal lamp to determine a first signal lamp regulation scheme in which the congestion degree reduction value meets a preset threshold, and the congestion relief scheme is generated.

[0064] Further, the regulation optimization module 14 is further configured to perform the following method:

[0065] If the iterative regulation optimization meets a preset convergence condition and the congestion degree reduction value does not meet the preset threshold, a second signal lamp regulation scheme at the time of convergence is generated, and a shunt lane of the congestion lane is identified in the associated lane; according to the current direction state of each tidal lane, an identified tidal lane whose current direction is opposite to the direction of the lane into the shunt lane is located; through the road traffic twin, a tidal lane regulation scheme that meets the preset threshold is identified by changing the direction state of the identified tidal lane based on the second signal lamp regulation scheme; and the congestion relief scheme is generated based on the second signal lamp regulation scheme and the tidal lane regulation scheme.

[0066] Further, the regulation optimization module 14 is further configured to perform the following method:

[0067] When the direction state of the identified tidal lane is changed, the lane with the smallest traffic flow is prioritized to start until the congestion degree reduction value meets the preset threshold.

[0068] It should be noted that the above-mentioned embodiment sequence of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0069] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0070] The present application and the drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalent technology, the present application intends to include these modifications and changes.

Claims

1. A method for relieving urban traffic congestion driven by Internet of Vehicles big data, characterized in that, The method comprises the following steps: Collecting the urban road network distribution in the target area, identifying the congestion correlation of each road based on the historical congestion records, and establishing a congestion correlation network; Through the vehicle networking architecture, the vehicle flow data of each road of the urban road network distribution is monitored to identify the congestion lane and generate the congestion degree identifier; In the congestion correlation network, the correlation lane of the congestion lane is extracted, and the signal light distribution and the tidal lane distribution in the congestion lane and the correlation lane are identified; A vehicle flow guidance network of the congestion lane and the correlation lane is established, and the signal light and the tidal lane are controlled and optimized in combination with the congestion degree identifier and the signal light distribution and the tidal lane distribution. 2.The IoV big data driven urban traffic congestion mitigation method of claim 1, wherein, Collecting the urban road network distribution in the target area, identifying the congestion correlation of each road based on the historical congestion records, and establishing a congestion correlation network, comprising: Extracting the first congestion record of the first road from the historical congestion records; Based on the first congestion record, the propagation relationship of congestion is analyzed, and based on the urban road network distribution, the correlation road distribution with a congestion propagation correlation degree greater than a preset correlation degree is identified, and a first congestion correlation network of the first road is established; The first congestion correlation network is added to the congestion correlation network. 3.The IoV big data driven urban traffic congestion mitigation method of claim 2, wherein, Based on the first congestion record, the propagation relationship of congestion is analyzed, and based on the urban road network distribution, the correlation road distribution with a congestion propagation correlation degree greater than a preset correlation degree is identified, comprising: The urban road network distribution is abstracted into a graph structure to generate a road network structure, wherein the intersections, the starting points and the ending points of the roads are nodes, and the road segments are edges; The first congestion record is mapped to the road network structure, and the time lag correlation between the remaining roads and the first road in the road network structure is analyzed, and the time lag correlation index is used as the congestion propagation correlation degree; According to the time lag correlation analysis result, the road with a congestion propagation correlation degree greater than a preset correlation degree is established as the correlation road distribution. 4.The IoV big data driven urban traffic congestion mitigation method of claim 3, wherein, The time lag correlation analysis calculates the correlation between the state of the first road at time t and the state of the remaining roads at time t+Δt, adjusts the time lag Δt, selects the correlation coefficient corresponding to the maximum correlation value, and generates the time lag correlation index. 5.The IoV big data driven urban traffic congestion mitigation method of claim 1, wherein, Establishing the vehicle flow guidance network of the congestion lane and the correlation lane, combining the congestion degree identifier and the signal light distribution and the tidal lane distribution, and performing control and optimization of the signal light and the tidal lane, comprising: For the congestion lane and the correlation lane, the lane connection is performed according to the vehicle flow guidance to generate the vehicle flow guidance network; According to the signal light distribution, the control lane group controlled by each signal light is divided to generate a signal light-lane group mapping; According to the tidal lane distribution, the current direction state of each tidal lane is detected; Based on the signal light-lane group mapping, the current direction state of each tidal lane, and in combination with the congestion degree identifier, a congestion relief scheme is generated through control simulation optimization; The congestion relief scheme is used to control the signal light and the tidal lane. 6.The IoV big data driven urban traffic congestion mitigation method of claim 5, wherein, During the control simulation optimization, the control priority of the signal light is higher than that of the tidal lane.

7. The IoT big data driven urban traffic congestion mitigation method of claim 6, wherein, Based on the signal lamp-lane group mapping, the current direction state of each tidal lane, and the congestion degree identification, a congestion relief scheme is generated through simulation optimization control, including: Building a road traffic twin based on the signal lamp-lane group mapping, the current direction state of each tidal lane, and the congestion degree identification; Collecting the minimum green light time constraint of each signal lamp in the signal lamp distribution; Under the minimum green light time constraint, iterative control optimization of the signal lamp is performed through the road traffic twin to determine a first signal lamp control scheme that satisfies a preset threshold of congestion reduction value, and the congestion relief scheme is generated. 8.The IoV big data driven urban traffic congestion mitigation method of claim 7, wherein, After the iterative control optimization of the signal lamp through the road traffic twin, it further includes: If the iterative control optimization satisfies a preset convergence condition and the congestion reduction value does not satisfy the preset threshold, a second signal lamp control scheme at the time of convergence is generated, and a diverging lane of the congestion lane is identified in the associated lane; According to the current direction state of each tidal lane, an identified tidal lane whose current direction is opposite to the direction of the diverging lane is located; Through the road traffic twin, a tidal lane control scheme that satisfies the preset threshold is identified by changing the direction state of the identified tidal lane based on the second signal lamp control scheme; The congestion relief scheme is generated based on the second signal lamp control scheme and the tidal lane control scheme. 9.The IoV big data driven urban traffic congestion mitigation method of claim 8, wherein, When changing the direction state of the identified tidal lane, it is preferred to start from the lane with the smallest traffic flow until the congestion reduction value satisfies the preset threshold.

10. A vehicular internet big data driven urban traffic congestion mitigation system characterized in that, The system is used to perform the city traffic congestion relief method driven by the Internet of Vehicles big data as claimed in any one of claims 1-9, including: A congestion association identification module: collecting the city road network distribution in the target area, identifying the congestion association of each road based on historical congestion records, and establishing a congestion association network; A traffic monitoring module: monitoring the traffic data of each road of the city road network distribution through the Internet of Vehicles architecture, identifying the congestion lane and generating the congestion degree identification; A lane identification module: extracting the associated lane of the congestion lane in the congestion association network, identifying the signal lamp distribution and tidal lane distribution in the congestion lane and the associated lane; A control optimization module: establishing a traffic flow guiding network of the congestion lane and the associated lane, combining the congestion degree identification, signal lamp distribution, and tidal lane distribution, and performing control optimization of the signal lamp and tidal lane.

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