Traffic dispersion control method, system and equipment for road construction area

Abstract

The invention discloses a traffic dispersion control method, system and device for a road construction area, and relates to the related field of traffic control, and the method comprises the steps: executing the traffic data collection of a target area, and building the traffic characteristics of the target area; construction data of a construction area are obtained, construction influence fitting is carried out, and a three-level interference path space structure is established; configuring a target traffic flow state, and executing data acquisition of a navigation account main body after any navigation reaches a construction area; performing navigation dispersion prediction by using the behavior prediction model; acquiring a real-time traffic flow state of the target area, establishing an arrival time node according to a navigation arrival result, and performing traffic flow time sequence fitting update based on the arrival data flow, the prediction result and the real-time traffic flow state; and establishing a deviation identification result according to the target traffic flow state and the traffic flow time sequence fitting result, and generating feedback excitation correction navigation dispersion. The technical problem that existing traffic dispersion control is poor in dispersion effect is solved, and the technical effect of improving traffic dispersion efficiency and accuracy is achieved.

Description

Technical Field

[0001] This application relates to the field of traffic control, and particularly to a traffic guidance and control method, system, and device for road construction areas. Background Art

[0002] Traffic guidance and control in road construction areas is crucial for ensuring smooth urban traffic and reducing the impact of construction on public travel. Currently, the main method to solve this problem is to conduct traffic guidance planning based on the fixed information of the construction area or simple traffic flow statistics. However, the current methods have poor guidance effects because they fail to fully consider the dynamic changes of actual traffic flow, the complex diversity of construction impacts, and the differences in individual travel behaviors, making it difficult to effectively respond to sudden traffic conditions in construction areas.

[0003] In the current related technologies, there are technical problems with poor guidance effects in traffic guidance and control of road construction areas. Summary of the Invention

[0004] This application provides a traffic guidance and control method, system, and device for road construction areas. By collecting traffic data in the target area, analyzing and constructing the traffic characteristics of the target area, obtaining road construction data, combining the traffic characteristics of the target area, conducting fitting analysis of construction impacts, establishing a three-level interference path spatial structure model, based on the three-level interference path spatial structure model, configuring the target traffic flow state, when the navigation reaches the road construction area, collecting the data of the navigation account subject, constructing an account data set, using a behavior prediction model, combining the account data set with the construction data, conducting navigation guidance prediction, generating a prediction result, obtaining the traffic flow state of the target area in real time, generating a reach time node by combining the navigation reach result, forming a reach data stream, based on the reach data stream, prediction result, and real-time traffic flow state, conducting fitting update of the traffic flow time series, comparing the target traffic flow state with the fitting result of the traffic flow time series, identifying deviation situations and generating feedback incentives, and modifying the navigation guidance strategy according to the feedback incentives to implement traffic guidance management and other technical means, which solves the technical problem of poor guidance effects in the existing traffic guidance and control of road construction areas, and achieves the technical effects of improving traffic guidance efficiency and accuracy.

[0005] The present application provides a traffic guidance and control method for road construction areas, including: collecting traffic data of a target area, and establishing the traffic characteristics of the target area based on the traffic data collection results; obtaining construction data of the road construction area, fitting the construction impact according to the construction data and the traffic characteristics of the target area, and establishing a three-level interference path spatial structure; configuring the target traffic flow state based on the three-level interference path spatial structure, and after any navigation reaches the road construction area, collecting data of the navigation account subject to establish an account data set; using a behavior prediction model to perform navigation guidance prediction based on the account data set and the construction data to establish a prediction result; obtaining the real-time traffic flow state of the target area, establishing a touch time node according to the navigation reach result to form a touch data stream, and performing traffic flow time series fitting and updating based on the touch data stream, the prediction result, and the real-time traffic flow state; establishing a deviation identification result according to the target traffic flow state and the traffic flow time series fitting result and generating a feedback incentive, and correcting the navigation guidance according to the feedback incentive to perform traffic guidance management.

[0006] In a possible implementation manner, establishing a deviation identification result according to the target traffic flow state and the traffic flow time series fitting result and generating a feedback incentive, the following processing is performed: creating a target correction task by using the deviation identification result, where the target correction task is a guidance correction task for users with abnormal prediction behaviors in the prediction result; taking the target correction task as an achievement goal, performing feedback incentive optimization under the constraint of incentive cost balance, where the feedback incentives include navigation integral incentives, carbon footprint incentives, route priority incentives, visual social exposure incentives, and group incentives; generating a feedback incentive by using the feedback incentive optimization result.

[0007] In a possible implementation manner, correcting the navigation guidance according to the feedback incentive and performing traffic guidance management, the following processing is performed: when any navigation vehicle enters the road construction area, identifying the real-time congestion state of the road construction area to generate a congestion penalty coefficient; calculating the additional path cost under the corrected navigation guidance for the navigation vehicle to generate a cost compensation coefficient; performing green travel credit penalty management on the navigation vehicle according to the congestion penalty coefficient and the cost compensation coefficient.

[0008] In a possible implementation manner, when configuring the target traffic flow state according to the three-level interference path spatial structure, the following processing is performed: Locate the primary interference path area, secondary interference path area, and tertiary interference path area according to the three-level interference path spatial structure. The primary interference path area is the directly affected construction area, the secondary interference path area is the adjacent diversion area with an association degree meeting the association threshold, and the tertiary interference path area is the remote diversion area with an association degree not meeting the association threshold; Establish a path topology structure based on the primary interference path area, secondary interference path area, and tertiary interference path area; Use the target area traffic characteristics as fitting data, and perform traffic balance optimization for the three-level interference path based on the path topology structure to establish the target traffic flow state.

[0009] In a possible implementation manner, when establishing the target area traffic characteristics based on the traffic data collection result, the following processing is performed: Use the current time node as the time zero point, and create a first retrospective time window and a second retrospective time window. The first retrospective time window is a weekly retrospective window, and the second retrospective time window is a monthly retrospective window; After allocating the trust factors for the first retrospective time window and the second retrospective time window, perform traffic characteristic verification and identification of the traffic data collection result using the first retrospective time window and the second retrospective time window to establish the target area traffic characteristics.

[0010] In a possible implementation manner, when using the behavior prediction model to perform navigation diversion prediction based on the account data set and construction data and establish a prediction result, the following processing is performed: Use the extraction layer of the behavior prediction model to extract the data characteristics of the account data set. The data characteristics include historical compliance rate, response delay, path usage preference, and integral usage history; After extracting the construction data into construction characteristics, activate the time series prediction layer to perform navigation diversion compliance prediction based on the construction characteristics and the data characteristics to establish a prediction result.

[0011] In a possible implementation manner, when performing traffic flow time series fitting and updating based on the reach data stream, the prediction result, and the real-time traffic flow state, the following processing is performed: Predict the passing proportion of non-navigation vehicles, and establish a first time series fitting compensation based on the passing proportion prediction result; Perform an impact analysis of non-vehicle passing according to the construction data, and generate an impact prediction of non-vehicles on the vehicle passing road based on the impact analysis result to establish a second time series fitting compensation; Perform compensation management on the traffic flow time series fitting result according to the first time series fitting compensation and the second time series fitting compensation.

[0012] In a possible implementation manner, after performing traffic diversion management, the following processing is performed: Monitor the passing of the road construction area; When the passing monitoring result meets the abnormal threshold, trigger a temporary closure instruction; Change the road construction area to a temporarily closed state according to the temporary closure instruction.

[0013] The present application also provides a traffic guidance and control system for a road construction area, including: a target area traffic characteristics establishment module, configured to collect traffic data of the target area and establish target area traffic characteristics based on the traffic data collection result; a construction impact fitting module, configured to obtain construction data of the road construction area and perform construction impact fitting according to the construction data and the target area traffic characteristics to establish a three-level interference path spatial structure; an account dataset establishment module, configured to configure the target traffic flow state with the three-level interference path spatial structure, and after any navigation reaches the road construction area, collect data of the navigation account subject to establish an account dataset; a navigation guidance prediction module, configured to perform navigation guidance prediction based on the account dataset and the construction data by using a behavior prediction model to establish a prediction result; a traffic flow time series fitting and updating module, configured to obtain the real-time traffic flow state of the target area, establish a reach time node according to the navigation reach result to form a reach data stream, and perform traffic flow time series fitting and updating based on the reach data stream, the prediction result, and the real-time traffic flow state; a navigation guidance correction module, configured to establish a deviation identification result according to the target traffic flow state and the traffic flow time series fitting result and generate a feedback incentive, and correct the navigation guidance according to the feedback incentive to perform traffic guidance management.

[0014] The present application also provides an electronic device, including: a memory, configured to store executable instructions; a processor, configured to implement the traffic guidance control method for a road construction area when executing the executable instructions stored in the memory.

[0015] It is intended to first collect traffic data of the target area through the traffic guidance control method, system and device for a road construction area proposed in the present application, establish target area traffic characteristics based on the traffic data collection result, then obtain construction data of the road construction area, perform construction impact fitting according to the construction data and the target area traffic characteristics to establish a three-level interference path spatial structure, then configure the target traffic flow state with the three-level interference path spatial structure, and after any navigation reaches the road construction area, collect data of the navigation account subject to establish an account dataset, and further perform navigation guidance prediction based on the account dataset and the construction data by using a behavior prediction model to establish a prediction result, then obtain the real-time traffic flow state of the target area, establish a reach time node according to the navigation reach result to form a reach data stream, perform traffic flow time series fitting and updating based on the reach data stream, the prediction result, and the real-time traffic flow state, and finally establish a deviation identification result according to the target traffic flow state and the traffic flow time series fitting result and generate a feedback incentive, and correct the navigation guidance according to the feedback incentive to perform traffic guidance management. The technical effect of improving the efficiency and accuracy of traffic guidance is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of this application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0017] Figure 1 It is a schematic flow chart of the traffic guidance control method for the road construction area provided by the embodiments of this application.

[0018] Figure 2 It is a schematic structural diagram of the traffic guidance control system for the road construction area provided by the embodiments of this application.

[0019] Figure 3 It is a schematic structural diagram of an electronic device provided by the embodiments of this application.

[0020] Explanation of reference numerals: Target area traffic feature establishment module 10, Construction impact fitting module 20, Account dataset establishment module 30, Navigation guidance prediction module 40, Traffic flow time series fitting and updating module 50, Navigation guidance correction module 60, Input device 301, Processor 302, Memory 303, Output device 304. Detailed implementation manners

[0021] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the detailed implementation manners of this application.

[0022] In order to make the purpose, technical solutions and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0023] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. 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 need not be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0024] Embodiments of this application provide a traffic guidance and control method for road construction areas, as Figure 1 shown, the method includes: Step S100, perform traffic data collection for the target area and establish the traffic characteristics of the target area based on the traffic data collection results.

[0025] Specifically, install a variety of sensors in the target area (the scope is larger than the road construction area), including but not limited to: traffic flow sensors, cameras, GPS data collectors, meteorological sensors, etc. Among them, traffic flow sensors (such as loop detectors, microwave radar detectors) are used to detect the number and speed of vehicles passing by. Cameras are used for license plate recognition and vehicle trajectory tracking. GPS data collectors (vehicle-built-in GPS or mobile phone navigation applications) are used to collect vehicle position and speed data. Meteorological sensors are used to monitor weather conditions, which will affect traffic flow.

[0026] Use edge computing devices (such as small servers) to perform preliminary data processing and compression at the sensor end. Transmit the data to the cloud or local data center for storage and further analysis. Divide the traffic data into two time windows, a short-time window and a long-time window, for processing and analysis, and extract traffic characteristics, such as average vehicle speed, traffic flow, congestion index, etc.

[0027] For example, on a main road in a certain city, install a microwave radar detector every 50 meters and a high-definition camera every 100 meters. These devices collect vehicle speed and license plate information in real time and transmit the data to the local server through a wireless network.

[0028] In a possible implementation, to establish the traffic characteristics of the target area based on the traffic data collection results, step S100 further includes step S110 of creating a first retrospective time window and a second retrospective time window with the current time node as the time zero point. The first retrospective time window is a weekly retrospective window, and the second retrospective time window is a monthly retrospective window. Specifically, with the current time node (such as the moment when the system starts or data collection begins) as the time zero point, time windows are created. Among them, the first retrospective time window (weekly retrospective window) is a time range that retrogresses 7 days (one week) from the current time node. The second retrospective time window (monthly retrospective window) is a time range that retrogresses 30 days (one month) from the current time node. In the data collection system, according to the current timestamp, the specific start and end times of these two time windows are calculated.

[0029] For example, assume the current time node is 12:00 on May 19, 2025. Then: The start and end times of the first retrospective time window (weekly retrospective window) are from 12:00 on May 12, 2025 to 12:00 on May 19, 2025. The start and end times of the second retrospective time window (monthly retrospective window) are from 12:00 on April 19, 2025 to 12:00 on May 19, 2025.

[0030] Step S120, after assigning the trust factors to the first retrospective time window and the second retrospective time window, perform traffic characteristic verification and identification on the traffic data collection results with the first retrospective time window and the second retrospective time window to establish the traffic characteristics of the target area. Specifically, the trust factor is a weight value used to measure the importance of data in different time windows. For example, a relatively high trust factor (such as 0.7) can be assigned to the first retrospective time window (weekly retrospective window) because the data in the recent week can better reflect the current traffic conditions; a relatively low trust factor (such as 0.3) can be assigned to the second retrospective time window (monthly retrospective window) because although the data for one month can provide a longer-term trend, its timeliness is weaker. Feature extraction is performed on the traffic data within the two time windows respectively, such as calculating the average vehicle speed, traffic flow, congestion index, etc. Statistical analysis or machine learning methods are used to verify and identify the traffic characteristics of the two time windows. For example, through clustering analysis or anomaly detection algorithms, it is judged whether the data conforms to the expected traffic pattern. The traffic characteristics of the two time windows are weighted and averaged according to the trust factor to obtain the comprehensive traffic characteristics. For example, if the average vehicle speed in the first retrospective time window is 40 km / h and the average vehicle speed in the second retrospective time window is 35 km / h, then the comprehensive average vehicle speed is: 0.7×40 + 0.3×35 = 38.5 km / h. This implementation method can more accurately establish the traffic characteristics of the target area by introducing two retrospective time windows and trust factors and combining short-term and long-term data.

[0031] Step S200: Obtain the construction data of the road construction area, perform construction impact fitting based on the construction data and the traffic characteristics of the target area, and establish a three-level interference path spatial structure.

[0032] Specifically, obtain the construction plan through construction management software or manual input, including construction time, construction scope, number of occupied lanes, etc. Use traffic simulation software (such as VISSIM or Synchro) to combine the traffic characteristics of the target area and the construction data to simulate the impact of construction on traffic flow. Based on the construction impact fitting results, establish a three-level interference path spatial structure. The three-level interference path spatial structure is a traffic guidance area divided according to the construction impact. Among them, the primary induction area is the area closest to the road construction area, where vehicles need to detour directly or decelerate. The secondary buffer area is the area slightly farther from the road construction area, where vehicles may need to adjust their driving speed or lanes. The tertiary monitoring area is the area farther from the road construction area, mainly used to monitor traffic flow changes and give early warnings. These areas are dynamic and will be adjusted according to real-time traffic flow and construction progress.

[0033] For example, a certain road section is undergoing pavement repair, and the construction area occupies one lane. The construction data includes the construction time from 9 am to 5 pm and the occupied lane number is lane 2. The traffic department uses VISSIM software to combine the previously established traffic characteristics of the target area (such as average vehicle speed and traffic volume) to simulate the impact of construction on traffic flow. According to the simulation results, 200 meters in front of the road construction area is designated as the primary induction area, 500 meters in front as the secondary buffer area, and 1000 meters in front as the tertiary monitoring area. These areas will be dynamically adjusted according to real-time traffic flow conditions.

[0034] Step S300: Configure the target traffic flow state with the three-level interference path spatial structure, and after any navigation reaches the road construction area, perform data collection on the navigation account entity to establish an account dataset.

[0035] Specifically, the target traffic flow state refers to the expected traffic flow distribution, speed, flow rate and other indicators in and around the road construction area. These indicators are set according to the construction impact and traffic guidance objectives. For example, the target traffic flow state may include: vehicle speed (the vehicle speed in front of the road construction area should be maintained above 30 km / h), traffic flow (the traffic flow passing through the road construction area per hour should be controlled within 1000 vehicles), congestion probability (the congestion probability in the road construction area should be less than 30%). According to the three-level interference path spatial structure (primary induction area, secondary buffer area, tertiary monitoring area), dynamically adjust and optimize the traffic flow state to achieve the goal of traffic guidance. For example, in the primary induction area, the traffic signal duration can be adjusted, temporary traffic signs or variable message signs can be set up to guide vehicles to detour or slow down, and through the intelligent transportation system (ITS) platform, the induction information can be sent to the vehicle navigation system to prompt users to detour or slow down in advance; in the secondary buffer area, the traffic signal duration can be adjusted to optimize the traffic flow distribution, reduce the traffic flow entering the primary induction area, and through variable message signs or broadcasts, remind drivers of the construction ahead and suggest adjusting the route in advance; in the tertiary monitoring area, the traffic flow state can be monitored in real time through a sensor network, early warnings of possible congestion can be given, and according to the real-time monitoring data, the traffic guidance strategy can be dynamically adjusted, such as adjusting the signal duration or issuing new navigation suggestions.

[0036] When any vehicle uses a navigation system (such as in-vehicle navigation, mobile phone navigation) to approach the road construction area, with the permission or authorization of the user, collect the user's driving data through the navigation software, including: historical navigation behavior (whether the user often selects the route recommended by the navigation software, whether the user will choose to detour to avoid congestion), real-time navigation behavior (the actual driving route and speed of the user when approaching the road construction area), user feedback (whether the user manually adjusts the navigation route, whether the user gives feedback on the navigation suggestions, such as "not taking the recommended route"). Among them, encryption technology is used to protect the privacy of user data. The collected navigation data is stored in the data center to form an account data set.

[0037] In a possible implementation, when configuring the target traffic flow state according to the three-level interference path spatial structure, step S300 further includes step S310 of locating the primary interference path area, secondary interference path area, and tertiary interference path area according to the three-level interference path spatial structure. The primary interference path area is the directly constructed impact area, the secondary interference path area is the adjacent diversion area with a correlation degree meeting the correlation threshold, and the tertiary interference path area is the remote diversion area with a correlation degree not meeting the correlation threshold. Specifically, the primary interference path area is the area directly affected by construction, which is the road construction area itself and its immediate adjacent lanes or sections. The secondary interference path area is the area with a relatively high correlation degree with the primary interference path area (for example, vehicles may be guided to these areas for detouring), but not directly affected by construction. The correlation degree can be calculated based on factors such as traffic flow and distance. When the correlation degree exceeds a certain threshold, it is classified as the secondary interference path area. The tertiary interference path area is the area at a certain distance from the road construction area, with a relatively low correlation degree (not reaching the threshold of the secondary interference path area), but still requires remote diversion. Use Geographic Information System (GIS) technology combined with construction data and traffic flow data to accurately divide these three areas. Simulate the impact of construction on traffic flow through traffic simulation software (such as VISSIM) to determine the boundaries of each area.

[0038] Step S320, based on the primary interference path area, secondary interference path area, and tertiary interference path area, establish a path topology structure. Specifically, use graph theory methods, regard each interference path area as a node in the graph, and the road connections as edges. Establish the connection relationships between the nodes to represent the possible paths for vehicles to travel from one area to another. According to the traffic flow and road network, determine the weights between the nodes (such as distance, travel time, congestion probability). Use GIS data and traffic simulation results to construct a complete path topology structure.

[0039] For example, assume that the primary interference path area is node A, the secondary interference path areas are nodes B and C, and the tertiary interference path areas are nodes D and E. The connection relationships between the nodes are as follows: A is connected to B and C (indicating that vehicles can directly detour from the construction area to the adjacent areas). B is connected to D, and C is connected to E (indicating that vehicles can continue to detour from the adjacent areas to the remote areas). The weight of each connecting edge is calculated based on the road length and the estimated travel time.

[0040] Step S330: Using the traffic characteristics of the target area as fitting data, perform traffic balance optimization for the three-level interference paths based on the path topology structure to establish the target traffic flow state. Specifically, use optimization algorithms (such as genetic algorithms, simulated annealing algorithms) combined with the traffic characteristics of the target area (such as vehicle speed, traffic flow) to find the optimal traffic flow distribution plan on the path topology structure. The optimization goal is to maximize the traffic efficiency of the entire area while minimizing congestion as much as possible. According to the optimization results, determine the traffic flow state (such as traffic flow, vehicle speed, congestion probability) of each area. Use these states as the basis for traffic guidance.

[0041] For example, assume that the traffic characteristics of the target area show that the current average vehicle speed is 30 km / h and the traffic flow is 800 vehicles per hour. Through the optimization algorithm, find the optimal traffic flow distribution plan on the path topology structure: In the area of the first-level interference path, it is recommended that vehicles slow down to 20 km / h and the traffic flow be controlled at 500 vehicles per hour. In the area of the second-level interference path, guide vehicles to detour and the traffic flow distribution be 200 vehicles per hour. In the area of the third-level interference path, issue prompts for early deceleration or route adjustment, and the traffic flow distribution be 100 vehicles per hour. This implementation method can more accurately identify the scope of the construction impact and dynamically optimize the traffic flow distribution through the division of the three-level interference path area, the establishment of the path topology structure, and the traffic balance optimization.

[0042] Step S400: Use the behavior prediction model to perform navigation guidance prediction based on the account dataset and construction data, and establish the prediction results.

[0043] Specifically, based on the user's historical navigation behavior and construction data, construct a behavior prediction model. The goal of this model is to predict the navigation choices of users when facing road construction areas (for example, whether to follow navigation suggestions, whether to choose to detour, etc.). Use machine learning algorithms (such as logistic regression, decision trees, or deep learning models) to train the behavior prediction model. The input features include: the user's historical navigation behavior (whether often choose the recommended route), the detailed information of the road construction area (such as construction time, number of occupied lanes, expected congestion level). The output is the probability of the user following the navigation suggestion (for example, the probability that the user chooses to detour is 80%). Generate the navigation guidance prediction results according to the probability of the user following the navigation suggestion. The prediction results can include: the possible routes that the user may choose, the time when the user arrives at the construction area, whether the user may choose to detour, etc. Store the prediction results in the data center for subsequent traffic guidance management.

[0044] In a possible implementation, the navigation guidance prediction based on the account dataset and construction data is performed using the behavior prediction model to establish a prediction result. Step S400 further includes step S410 of using the extraction layer of the behavior prediction model to extract the data features of the account dataset. The data features include historical compliance rate, response latency, path usage preference, and integral usage history. Specifically, the historical compliance rate refers to the proportion of a user's compliance with navigation suggestions during past navigation processes. For example, if a user has selected the route recommended by the navigation 8 times out of the past 10 navigations, the historical compliance rate is 80%. The response latency refers to the time it takes for a user to respond to a navigation suggestion. For example, the time interval from when the navigation software pushes a suggestion to when the user actually starts to execute (such as changing the route). The path usage preference refers to a user's preference for different types of paths, such as whether they tend to choose expressways or avoid congested sections. The integral usage history refers to whether a user has used the integral system of the navigation software (such as redeeming discounts or obtaining priority recommendations). The extraction layer in the behavior prediction model is responsible for extracting the above features from the account dataset and using these features as the input data for subsequent predictions. Among them, the extraction layer can be implemented using machine learning algorithms (such as feature selection algorithms) or convolutional / fully connected layers in deep learning.

[0045] For example, assume that the user's account dataset contains the following information: historical compliance rate: 80%; response latency: an average of 30 seconds; path usage preference: tend to choose expressways and avoid congested sections; integral usage history: used points to redeem priority recommendation services within the past month. The extraction layer of the behavior prediction model extracts this information as the features for subsequent predictions.

[0046] In step S420, after extracting the construction data as construction features, the time series prediction layer is activated to perform navigation guidance compliance prediction based on the construction features and the data features, and a prediction result is established. Specifically, the extraction layer of the behavior prediction model extracts key features from the construction data, such as: construction time (specific time period of construction), number of occupied lanes (number of lanes occupied by construction), and expected congestion level (congestion probability predicted based on the construction scope and historical data). The time series prediction layer is responsible for combining the construction features and user behavior features (the data features) to predict the user's compliance behavior with the navigation guidance suggestions. The time series prediction layer uses time series analysis methods (such as LSTM, GRU) or deep learning models (such as Transformer) to process time series data. The prediction result includes whether the user will choose to detour, the time to respond to the navigation suggestion, etc. This implementation method can more accurately predict the user's compliance behavior with the navigation suggestion by extracting user behavior features and construction features. This accurate prediction can reduce congestion caused by users not following the navigation suggestion. According to the prediction result, the navigation system can dynamically adjust the navigation suggestion, such as pushing the detour route in advance, reminding the user multiple times, etc., so as to improve the effect of navigation guidance.

[0047] In step S500, the real-time traffic flow state of the target area is obtained, the touch time node is established according to the navigation touch result, a touch data stream is formed, and the traffic flow time series is fitted and updated based on the touch data stream, the prediction result, and the real-time traffic flow state.

[0048] Specifically, the traffic flow state of the target area is monitored in real time through a sensor network (such as traffic flow sensors, cameras, GPS data, etc.), including vehicle speed, traffic flow, congestion level, etc. For example, in the road construction area and its surrounding areas, the vehicle speed and traffic flow data are obtained in real time through high-definition cameras and microwave radar detectors. When a vehicle uses the navigation system to reach the road construction area, the time point of the vehicle's arrival is recorded. These time nodes are used as part of the touch data stream. For example, the navigation system records that user X arrives at the road construction area at 9:05 am, and user Y arrives at the road construction area at 9:10 am. The touch time nodes of all vehicles are aggregated to form a time series data stream. These data streams are used to analyze the frequency and time distribution of vehicles arriving at the road construction area. For example, the touch data stream shows that during the period from 9:00 am to 9:30 am, an average of 5 vehicles arrive at the construction area per minute. Combining the touch data stream, the prediction result (navigation guidance compliance prediction), and the real-time traffic flow state, time series analysis methods (such as ARIMA model, LSTM, etc.) are used to fit and update the traffic flow state. Through the fitting and update, the traffic guidance strategy is dynamically adjusted to ensure that the guidance measures can adapt to the real-time traffic conditions.

[0049] In a possible implementation, for the traffic flow time series fitting update based on the reach data stream, the prediction result, and the real-time traffic flow state, step S500 further includes step S510 of predicting the passing proportion of non-navigation vehicles and establishing a first time series fitting compensation based on the passing proportion prediction result. Specifically, traffic data of vehicles is collected through a sensor network (such as traffic flow sensors, cameras). Machine learning algorithms (such as clustering analysis) are used to distinguish navigation vehicles and non-navigation vehicles. For example, the driving paths of navigation vehicles usually conform to navigation suggestions, while the driving paths of non-navigation vehicles are more random. Historical data and real-time data are used to predict the passing proportion of non-navigation vehicles in the target area. For example, by analyzing the ratio of non-navigation vehicles to navigation vehicles in the past week and combining the current traffic conditions, the proportion of non-navigation vehicles in the current period is predicted. According to the passing proportion prediction result, the traffic flow time series fitting result is compensated. If the proportion of non-navigation vehicles is relatively high, it will have an additional impact on the traffic flow and needs to be considered in the fitting model.

[0050] Step S520: Analyze the impact of non-vehicle passage based on the construction data, generate a prediction of the impact of non-vehicles on the vehicle passage road based on the impact analysis result, and establish a second time series fitting compensation. Specifically, the construction data is analyzed to identify the impact of construction on non-vehicle passage (such as pedestrians, bicycles, construction vehicles, etc.). For example, construction may cause the sidewalk to narrow, and pedestrians and bicycles may need to occupy part of the lane. Traffic simulation software (such as VISSIM) is used to simulate the impact of non-vehicle passage on the vehicle passage road. According to the construction data and the simulation results, the degree of impact of non-vehicle passage on the vehicle passage road is predicted. For example, the probability and time of pedestrians and bicycles occupying the lane are predicted. According to the prediction result of the impact of non-vehicle passage, the traffic flow time series fitting result is compensated. If the impact of non-vehicle passage on the vehicle passage road is relatively large, it needs to be adjusted in the fitting model.

[0051] Step S530: Manage the compensation of the traffic flow time series fitting result according to the first time series fitting compensation and the second time series fitting compensation. Specifically, the first time series fitting compensation (the impact of non-navigation vehicles) and the second time series fitting compensation (the impact of non-vehicle passage) are combined to comprehensively compensate the traffic flow time series fitting result. Weighted average or other statistical methods are used to integrate the two compensation factors into the traffic flow time series fitting model. According to the compensated traffic flow time series fitting result, the traffic guidance strategy is dynamically adjusted. For example, if the impacts of non-navigation vehicles and non-vehicle passage are relatively large, new navigation suggestions may need to be issued. This implementation can more comprehensively reflect the actual traffic conditions and dynamically adjust the traffic guidance strategy by introducing the impact analysis of non-navigation vehicles and non-vehicle passage and managing the compensation of the traffic flow time series fitting result.

[0052] Step S600: Establish a deviation recognition result based on the target traffic flow state and the traffic flow time series fitting result, generate a feedback incentive, correct the navigation guidance according to the feedback incentive, and execute traffic guidance management.

[0053] Specifically, by using machine learning or statistical analysis methods (such as anomaly detection algorithms), compare the target traffic flow state and the traffic flow time series fitting result to identify whether the actual state of the traffic flow deviates from the expectation. If there is a significant difference between the traffic flow time series fitting result and the target traffic flow state, it is considered that a deviation has occurred. For example, the target traffic flow state result shows that the vehicle speed in the road construction area should be 30 km / h during the period from 9:00 to 9:30 in the morning, but the vehicle speed in the traffic flow time series fitting result is only 25 km / h, indicating that the traffic flow state deviates from the expectation. According to the deviation recognition result, generate a feedback incentive signal for adjusting the navigation guidance strategy. The feedback incentive can include adjusting the navigation suggestion, publishing new traffic guidance information, etc. For example, due to the deviation of the traffic flow state from the expectation, the system generates a feedback incentive signal and suggests that the navigation system push a better detour route to the user. According to the feedback incentive signal, dynamically adjust the navigation guidance strategy, including updating the navigation path, publishing new traffic guidance information, etc. Through the intelligent transportation system platform, monitor the traffic flow state in real time and dynamically adjust the guidance strategy according to the feedback incentive signal. When necessary, traffic management personnel can manually intervene to optimize the guidance plan.

[0054] In a possible implementation manner, in the step of establishing a deviation recognition result based on the target traffic flow state and the traffic flow time series fitting result and generating a feedback incentive, step S600 further includes step S610: create a target correction task by using the deviation recognition result, and the target correction task is a guidance correction task for users with abnormal prediction behaviors in the prediction result. Specifically, through the update of the traffic flow time series fitting, identify the deviation between the actual traffic flow state and the prediction result, and determine which users' navigation behaviors deviate from the prediction result. For example, the user does not detour according to the navigation suggestion, or the response time is too long. According to the deviation recognition result, create a target correction task for guiding the navigation behaviors of these users to return to the expected guidance strategy. Determine the number of users to be corrected and the specific correction target. For example, assume that through deviation recognition, it is found that 20% of the users do not detour according to the navigation suggestion, resulting in increased congestion in the road construction area. Create a target correction task with the goal of guiding 10% of these deviated users to choose the recommended detour route to relieve congestion.

[0055] Step S620: With the target calibration task as the achievement goal, perform feedback incentive optimization under the constraint of balanced incentive cost. The feedback incentives include navigation point incentive, carbon footprint incentive, route priority incentive, visual social exposure incentive, and group incentive. Specifically, determine the cost budget for incentive measures. For example, the cost of navigation point incentive, the reward amount of carbon footprint incentive, etc. Evaluate the cost-benefit of different incentive measures to ensure that the incentive measures achieve the target calibration task while keeping the cost under control. Use optimization algorithms (such as linear programming, genetic algorithms) to find the optimal incentive combination under the constraint of balanced incentive cost. The available incentive measures include: navigation point incentive, carbon footprint incentive, route priority incentive, visual social exposure incentive, and group incentive. Among them, the navigation point incentive means that if the user selects the recommended route, they can obtain points, which can be used to exchange for discounts or services. The carbon footprint incentive means that if the user selects an environmentally friendly route (such as detouring to avoid congestion), they can obtain a carbon footprint reward, which can be used for environmental protection activities or to exchange for rewards. The route priority incentive means that if the user selects the recommended route, they can obtain the right of priority passage. For example, enter the express lane. The visual social exposure incentive means that if the user selects the recommended route, they can obtain exposure on the social platform, enhancing the user's social recognition. The group incentive means that through the group reward mechanism, encourage the user group to select the recommended route. For example, a certain percentage of users in the group selecting the recommended route can obtain additional rewards. The optimization goal is to maximize the achievement rate of the target calibration task while minimizing the incentive cost.

[0056] For example, assume that the target calibration task is to guide 10% of the deviated users to select the recommended detour route. The incentive cost budget is 1000 points (assuming points are the virtual currency of the navigation software). Through the optimization algorithm, the system decides to adopt the following incentive combination: Navigation point incentive: Reward 50 points to users who select the recommended route; Carbon footprint incentive: Reward 30 points to users who select the environmentally friendly route; Visual social exposure incentive: Display the user's environmentally friendly behavior on the social platform for users who select the recommended route. This incentive combination is expected to guide 10% of the deviated users to select the recommended route while keeping the incentive cost within the budget.

[0057] Step S630: Generate feedback incentives using the feedback incentive optimization results. Specifically, generate specific incentive measures based on the feedback incentive optimization results. Push the incentive measures to the user through the navigation software. For example, display information such as point rewards and environmental protection rewards on the navigation interface. During the navigation process, the user selects the recommended route according to the incentive measures. The system monitors the user's navigation behavior in real time to confirm whether the user responds to the incentive measures. Evaluate the effectiveness of the incentive measures. For example, determine how many users are actually guided to select the recommended route. According to the evaluation results, dynamically adjust the incentive measures to improve the incentive effect. For example, if it is found that the incentive effect is not good, the system can dynamically adjust the incentive measures, such as increasing point rewards or introducing new incentive mechanisms. This implementation method can effectively guide users to select the recommended route and improve the response rate of users to navigation suggestions by creating a target correction task, performing feedback incentive optimization under the constraint of incentive cost balance, and generating specific feedback incentive measures.

[0058] In a possible implementation, in step S600 of correcting the navigation guidance according to the feedback incentive and performing traffic guidance management, it further includes step S640: When any navigation vehicle enters the road construction area, identify the real-time congestion state of the road construction area and generate a congestion penalty coefficient. Specifically, use a sensor network (such as traffic flow sensors, cameras, GPS data, etc.) to monitor the traffic flow state of the road construction area in real time, including vehicle speed, traffic volume, congestion degree, etc. Analyze the real-time data through machine learning algorithms (such as clustering analysis, anomaly detection) or traffic simulation software (such as VISSIM) to identify the current congestion state. Generate a congestion penalty coefficient according to the severity of the congestion state. For example, the higher the congestion degree, the larger the penalty coefficient. The penalty coefficient can be a numerical value used to quantify the impact of the current congestion on vehicle passage.

[0059] For example, assume that the real-time monitoring data of the construction area shows that the current vehicle speed is 15 km / h, the traffic volume is 1200 vehicles per hour, and the congestion degree is relatively high. Through the congestion state identification algorithm, a congestion penalty coefficient of 1.5 is generated (indicating that the current congestion degree is relatively high and has a greater impact on passage).

[0060] Step S650: Calculate the additional path cost under the corrected navigation guidance for the navigation vehicle and generate a cost compensation coefficient. Specifically, calculate the additional path cost according to the actual driving path of the navigation vehicle and the corrected navigation guidance suggestions. For example, if the vehicle selects a detour route, calculate the additional time and distance of the detour route compared to the original route. Generate a cost compensation coefficient according to the additional path cost. For example, if the additional path cost is relatively high, the compensation coefficient is also relatively high, which is used to encourage users to select the recommended route.

[0061] For example, assume that the navigation vehicle selects a detour route, which increases the driving time by 10 minutes and the distance by 5 kilometers compared to the original route. Through additional path cost calculation, a cost compensation coefficient of 1.2 is generated (indicating that the additional cost of the detour route is relatively high and a certain compensation is required).

[0062] Step S660, perform green travel credit penalty management for the navigation vehicle according to the congestion penalty coefficient and the cost compensation coefficient. Specifically, manage the green travel credit of the navigation vehicle according to the congestion penalty coefficient and the cost compensation coefficient. If the vehicle selects a detour route in a congested situation, a certain credit reward is given; if the vehicle does not detour as recommended by the navigation, the corresponding credit score is deducted. The credit management measures may include: if the user selects the recommended route, green points can be obtained, and the points can be used to exchange for environmental protection rewards or discounts. If the user's credit score is too low, subsequent navigation suggestions for certain congested sections may not be visible, or they may be displayed as impassable. According to the user's credit score, a city travel recommendation label is given, such as "green travel user". Users with a high credit score can enjoy a higher probability of obtaining parking coupons, etc.

[0063] For example, assume that the navigation vehicle selects a detour route in a congested situation, the congestion penalty coefficient is 1.5, and the cost compensation coefficient is 1.2. Based on these two coefficients, the system gives the user a certain green point reward (such as 50 points). If the user has a high credit score, the system will give the label of "green travel user" and increase the probability of obtaining a parking coupon for the user. This implementation method can effectively encourage users to select the recommended route and reduce congestion in the construction area through real-time congestion status recognition, additional path cost calculation, and green travel credit management.

[0064] In a possible implementation manner, after performing the traffic guidance management, the method further includes: monitoring the passage of the road construction area; when the passage monitoring result meets the abnormal threshold, triggering a temporary closure instruction; and changing the road construction area to a temporarily closed state according to the temporary closure instruction.

[0065] Specifically, a sensor network (such as traffic flow sensors, cameras, GPS data, etc.) is used to continuously monitor the traffic status of the road construction area, including vehicle speed, traffic flow, congestion level, etc. The intelligent transportation system (ITS) platform analyzes the monitoring data in real time to identify whether there are abnormal situations. For example, the ITS platform analyzes the monitoring data once every minute to determine whether the traffic status is normal. Abnormal thresholds are set, such as the vehicle speed is lower than 10 km / h and the duration exceeds 10 minutes, or the traffic flow exceeds a certain upper limit. When the monitoring data reaches or exceeds the abnormal threshold, the system automatically triggers a temporary closure instruction. According to the temporary closure instruction, the temporary closure information is published through variable message signs, navigation software, radio, etc., to remind drivers that the construction area is closed and recommend a detour. By allowing vehicles within the road construction area to drive out but prohibiting external vehicles from entering, the traffic can be quickly restored to smoothness. This implementation method can quickly identify and relieve the congestion in the road construction area through real-time traffic monitoring, abnormal threshold triggering mechanism, and temporary closure measures.

[0066] In the embodiments of the present application, by collecting the traffic data of the target area, analyzing and constructing the traffic characteristics of the target area, obtaining the road construction data, combining the traffic characteristics of the target area, conducting construction impact fitting analysis, establishing a three-level interference path space structure model, based on the three-level interference path space structure model, configuring the target traffic flow state, when the navigation reaches the road construction area, collecting the navigation account subject data, constructing an account data set, using a behavior prediction model, combining the account data set with the construction data, conducting navigation guidance prediction, generating a prediction result, obtaining the traffic flow state of the target area in real time, combining the navigation reach result to generate a reach time node, forming a reach data stream, based on the reach data stream, the prediction result and the real-time traffic flow state, conducting the fitting update of the traffic flow time series, comparing the target traffic flow state with the traffic flow time series fitting result, identifying the deviation situation and generating a feedback incentive, and modifying the navigation guidance strategy according to the feedback incentive, implementing technical means such as traffic guidance management, etc., to solve the technical problem of poor guidance effect existing in the traffic guidance control of the existing road construction area, and achieving the technical effect of improving the traffic guidance efficiency and accuracy.

[0067] In the above text, reference is made to Figure 1 The traffic guidance control method for the road construction area according to the embodiments of the present invention is described in detail. Next, reference will be made to Figure 2 Describe the traffic guidance control system for the road construction area according to the embodiments of the present invention.

[0068] The traffic guidance and control system for a road construction area according to an embodiment of the present invention is used to solve the technical problem of poor guidance effect existing in the traffic guidance and control of existing road construction areas, and achieve the technical effect of improving the efficiency and accuracy of traffic guidance. The traffic guidance and control system for a road construction area includes: a target area traffic characteristic establishment module 10, a construction impact fitting module 20, an account data set establishment module 30, a navigation guidance prediction module 40, a traffic flow time series fitting and updating module 50, and a navigation guidance correction module 60.

[0069] The target area traffic characteristic establishment module 10 is used to collect the traffic data of the target area and establish the target area traffic characteristics based on the traffic data collection results; the construction impact fitting module 20 is used to obtain the construction data of the road construction area and perform construction impact fitting according to the construction data and the target area traffic characteristics to establish a three-level interference path spatial structure; the account data set establishment module 30 is used to configure the target traffic flow state with the three-level interference path spatial structure, and after any navigation reaches the road construction area, perform data collection of the navigation account subject to establish an account data set; the navigation guidance prediction module 40 is used to perform navigation guidance prediction based on the account data set and the construction data by using a behavior prediction model to establish a prediction result; the traffic flow time series fitting and updating module 50 is used to obtain the real-time traffic flow state of the target area, establish a reach time node according to the navigation reach result to form a reach data stream, and perform traffic flow time series fitting and updating based on the reach data stream, the prediction result, and the real-time traffic flow state; the navigation guidance correction module 60 is used to establish a deviation identification result according to the target traffic flow state and the traffic flow time series fitting result and generate a feedback incentive, and correct the navigation guidance according to the feedback incentive to perform traffic guidance management.

[0070] Next, the specific configuration of the navigation guidance correction module 60 will be described in detail. As described above, a deviation identification result is established according to the target traffic flow state and the traffic flow time series fitting result and a feedback incentive is generated. The navigation guidance correction module 60 may further include: a target correction task creation unit for creating a target correction task by using the deviation identification result, and the target correction task is a guidance correction task for users with abnormal prediction behaviors in the prediction result; a feedback incentive optimization unit for taking the target correction task as the achievement goal and performing feedback incentive optimization under the constraint of incentive cost balance, and the feedback incentives include navigation integral incentives, carbon footprint incentives, route priority incentives, visual social exposure incentives, and group incentives; a feedback incentive generation unit for generating a feedback incentive by using the feedback incentive optimization result.

[0071] Among them, for correcting navigation guidance according to the feedback incentive and implementing traffic guidance management, the navigation guidance correction module 60 may further include: a real-time congestion status recognition unit configured to, when any navigation vehicle enters the road construction area, recognize the real-time congestion status of the road construction area and generate a congestion penalty coefficient; a cost calculation unit configured to calculate the additional path cost under the corrected navigation guidance of the navigation vehicle and generate a cost compensation coefficient; and a green travel credit penalty management unit configured to perform green travel credit penalty management of the navigation vehicle according to the congestion penalty coefficient and the cost compensation coefficient.

[0072] Next, the specific configuration of the account dataset establishment module 30 will be described in detail. As described above, with the three-level interference path space structure configured for the target traffic flow state, the account dataset establishment module 30 may further include: an interference path area positioning unit configured to locate the primary interference path area, the secondary interference path area, and the tertiary interference path area according to the three-level interference path space structure, where the primary interference path area is the directly affected construction area, the secondary interference path area is the adjacent guidance area with an association degree meeting the association threshold, and the tertiary interference path area is the remote guidance area with an association degree not meeting the association threshold; a path topology structure establishment unit configured to establish a path topology structure based on the primary interference path area, the secondary interference path area, and the tertiary interference path area; and a traffic balance optimization unit configured to use the target area traffic characteristics as fitting data and perform traffic balance optimization of the tertiary interference path based on the path topology structure to establish the target traffic flow state.

[0073] Next, the specific configuration of the target area traffic characteristics establishment module 10 will be described in detail. As described above, with the traffic data collection results used to establish the target area traffic characteristics, the target area traffic characteristics establishment module 10 may further include: a retrospective time window creation unit configured to use the current time node as the time zero point to create a first retrospective time window and a second retrospective time window, where the first retrospective time window is a weekly retrospective window and the second retrospective time window is a monthly retrospective window; and a traffic characteristics verification and recognition unit configured to, after assigning trust factors to the first retrospective time window and the second retrospective time window, perform traffic characteristics verification and recognition of the traffic data collection results using the first retrospective time window and the second retrospective time window to establish the target area traffic characteristics.

[0074] Next, the specific configuration of the navigation guidance prediction module 40 will be described in detail. As described above, the navigation guidance prediction is performed using the behavior prediction model based on the account dataset and the construction data to establish a prediction result. The navigation guidance prediction module 40 may further include: a data feature extraction unit for extracting the data features of the account dataset using the extraction layer of the behavior prediction model, where the data features include historical compliance rate, response delay, path usage preference, and integral usage history; a navigation guidance compliance prediction unit for extracting the construction data as construction features and then activating the time series prediction layer to perform navigation guidance compliance prediction based on the construction features and the data features to establish a prediction result.

[0075] Next, the specific configuration of the traffic flow time series fitting and updating module 50 will be described in detail. As described above, the traffic flow time series is fitted and updated based on the reach data stream, the prediction result, and the real-time traffic flow state. The traffic flow time series fitting and updating module 50 may further include: a first time series fitting compensation establishment unit for predicting the passing ratio of non-navigation vehicles and establishing a first time series fitting compensation based on the passing ratio prediction result; a second time series fitting compensation establishment unit for performing an impact analysis of non-vehicle passing according to the construction data, generating an impact prediction of non-vehicles on the vehicle passing road based on the impact analysis result, and establishing a second time series fitting compensation; a compensation management unit for compensating and managing the traffic flow time series fitting result according to the first time series fitting compensation and the second time series fitting compensation.

[0076] Among them, after the traffic guidance management is executed, the system may further include: a passing monitoring module for monitoring the passing of the road construction area; a temporary closure instruction triggering module for triggering a temporary closure instruction when the passing monitoring result meets the abnormal threshold; a status change module for changing the road construction area to a temporarily closed state according to the temporary closure instruction.

[0077] The traffic guidance control system for the road construction area provided by the embodiment of the present invention can execute the traffic guidance control method for the road construction area provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0078] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included individual units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0079] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device. Figure 3It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The displayed electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. The electronic device is presented in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. Among them, the processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product. This program product has a set (at least one) of program modules, and these program modules are configured to execute the functions of various embodiments of the present application.

[0080] The memory 303 shown in the embodiments of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, infrared rays, semiconductor systems, devices or components, or any combination of the above, for storing software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the traffic guidance control method in the road construction area in the embodiments of the present invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 303, that is, implements the above-mentioned traffic guidance control method for the road construction area.

[0081] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application may be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

Claims

1. A traffic diversion control method for road construction areas, characterized in that, The method includes: Performing traffic data collection in the target area, and establishing the traffic characteristics of the target area based on the traffic data collection results; Obtaining the construction data of the road construction area, fitting the construction impact according to the construction data and the traffic characteristics of the target area, and establishing a three-level interference path spatial structure; Configuring the target traffic flow state with the three-level interference path spatial structure, and after any navigation reaches the road construction area, performing data collection of the navigation account subject to establish an account dataset; Using a behavior prediction model to perform navigation guidance prediction based on the account dataset and construction data, and establishing a prediction result; Obtaining the real-time traffic flow state of the target area, establishing a touch time node according to the navigation reach result to form a touch data stream, and performing traffic flow time series fitting update based on the touch data stream, the prediction result, and the real-time traffic flow state; Establishing a deviation identification result based on the target traffic flow state and the traffic flow time series fitting result and generating a feedback incentive, and correcting the navigation guidance according to the feedback incentive to perform traffic guidance management.

2. The traffic guidance and control method for a road construction area according to claim 1, characterized in that The establishing a deviation identification result based on the target traffic flow state and the traffic flow time series fitting result and generating a feedback incentive includes: Using the deviation identification result to create a target correction task, where the target correction task is a guidance correction task for users with abnormal prediction behaviors in the prediction result; Taking the target correction task as the achievement goal, performing feedback incentive optimization under the constraint of incentive cost balance, where the feedback incentives include navigation point incentive, carbon footprint incentive, route priority incentive, visual social exposure incentive, group incentive; Generating a feedback incentive using the feedback incentive optimization result.

3. The traffic guidance and control method for road construction areas as described in claim 1, characterized in that, The correcting the navigation guidance according to the feedback incentive to perform traffic guidance management includes: When any navigation vehicle enters the road construction area, performing real-time congestion state identification of the road construction area to generate a congestion penalty coefficient; Calculating the additional path cost under the corrected navigation guidance for the navigation vehicle to generate a cost compensation coefficient; Performing green travel credit penalty management for the navigation vehicle according to the congestion penalty coefficient and the cost compensation coefficient.

4. The traffic diversion control method for a road construction area according to claim 1, characterized in that, The configuring the target traffic flow state with the three-level interference path spatial structure includes: Locating the first-level interference path area, the second-level interference path area, and the third-level interference path area according to the three-level interference path spatial structure. The first-level interference path area is the direct construction impact area, the second-level interference path area is the adjacent guidance area with an association degree meeting the association threshold, and the third-level interference path area is the remote guidance area with an association degree not meeting the association threshold; Establishing a path topology structure based on the first-level interference path area, the second-level interference path area, and the third-level interference path area; Taking the traffic characteristics of the target area as the fitting data, performing traffic balance optimization of the three-level interference path based on the path topology structure to establish a target traffic flow state.

5. The traffic diversion control method for a road construction area according to claim 1, characterized in that, The establishing the traffic characteristics of the target area based on the traffic data collection results includes: Taking the current time node as the time zero point, creating a first retrospective time window and a second retrospective time window. The first retrospective time window is a weekly retrospective window, and the second retrospective time window is a monthly retrospective window; After allocating the trust factors of the first retrospective time window and the second retrospective time window, perform the verification and identification of the passing characteristics of the passing data collection results with the first retrospective time window and the second retrospective time window to establish the passing characteristics of the target area.

6. The traffic guidance and control method for a road construction area according to claim 1, characterized in that The navigation guidance prediction based on the account data set and construction data using the behavior prediction model to establish the prediction result includes: Use the extraction layer of the behavior prediction model to extract the data characteristics of the account data set, and the data characteristics include historical compliance rate, response delay, path usage preference, and integral usage history; After extracting the construction data as construction characteristics, activate the time series prediction layer to perform navigation guidance compliance prediction based on the construction characteristics and the data characteristics to establish the prediction result.

7. The traffic diversion control method for a road construction area according to claim 1, wherein The traffic flow time series fitting update based on the reach data stream, the prediction result, and the real-time traffic flow state includes: Predict the passing proportion of non-navigated vehicles, and establish the first time series fitting compensation based on the passing proportion prediction result; Conduct an impact analysis of non-vehicle passing according to the construction data, and generate an impact prediction of non-vehicles on the vehicle passing road based on the impact analysis result to establish the second time series fitting compensation; Compensation management of the traffic flow time series fitting result according to the first time series fitting compensation and the second time series fitting compensation.

8. The traffic guidance and control method for a road construction area according to claim 1, characterized in that, After performing the traffic guidance management, it includes: Monitor the passing of the road construction area; When the passing monitoring result meets the abnormal threshold, trigger a temporary closure instruction; Change the road construction area to a temporarily closed state according to the temporary closure instruction.

9. A traffic guidance and control system for a road construction area, characterized in that, The system is used to implement the traffic guidance control method for the road construction area described in any one of claims 1-8, and the system includes: A target area passing characteristic establishment module, which is used to perform passing data collection of the target area and establish the passing characteristics of the target area based on the passing data collection result; A construction impact fitting module, which is used to obtain the construction data of the road construction area, perform construction impact fitting according to the construction data and the passing characteristics of the target area, and establish a three-level interference path space structure; An account data set establishment module, which is used to configure the target traffic flow state with the three-level interference path space structure, and perform data collection of the navigation account subject after any navigation reaches the road construction area to establish an account data set; A navigation guidance prediction module, which is used to perform navigation guidance prediction based on the account data set and construction data using the behavior prediction model to establish the prediction result; A traffic flow time series fitting update module, which is used to obtain the real-time traffic flow state of the target area, establish a reach time node according to the navigation reach result to form a reach data stream, and perform traffic flow time series fitting update based on the reach data stream, the prediction result, and the real-time traffic flow state; A navigation guidance correction module, which is used to establish a deviation identification result and generate a feedback incentive according to the target traffic flow state and the traffic flow time series fitting result, correct the navigation guidance according to the feedback incentive, and perform traffic guidance management.

10. An electronic device, characterized in that, The electronic device includes: A memory for storing executable instructions; A processor, when executing the executable instructions stored in the memory, implements the traffic guidance control method for the road construction area according to any one of claims 1 to 8.

Images