Traffic state monitoring method and computer program product

Abstract

This application provides a traffic condition monitoring method and computer program product. In this application embodiment, static attribute data and dynamic attribute data of the monitoring area within a monitoring time can be acquired; trajectory recognition is performed on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area; then, based on the static attribute data and navigation trajectory attributes, traffic state parameters of road nodes within the monitoring area within the monitoring time can be calculated; and based on the traffic state parameters of road nodes within the monitoring time, the traffic state of the road nodes within the monitoring time can be determined. In this application embodiment, based on the static attribute data of the monitoring area and the mining results of the navigation trajectory attributes within the monitoring area, traffic condition monitoring of all road nodes within the monitoring area can be achieved, which helps to improve the spatial coverage of traffic condition monitoring.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a traffic condition monitoring method and computer program product. Background Technology

[0002] With the continuous increase in motor vehicles, traffic congestion is becoming increasingly serious, and the pressure on traffic management is constantly increasing. Monitoring traffic conditions can provide traffic management departments with a timely understanding of traffic conditions and a basis for adopting effective traffic management measures.

[0003] In existing technologies, manual surveys and cross-sectional traffic flow monitoring are the main methods for traffic condition monitoring. However, manual surveys have insufficient spatial coverage. Cross-sectional traffic flow monitoring mainly monitors traffic flow at certain cross-sections, resulting in low spatial continuity and also insufficient spatial coverage. Summary of the Invention

[0004] This application provides a traffic condition monitoring method and computer program product to improve the spatial coverage of traffic condition monitoring.

[0005] This application provides a traffic condition monitoring method, including:

[0006] Acquire static attribute data of the monitoring area and dynamic attribute data of the monitoring area generated during the monitoring time;

[0007] The dynamic attribute data is used for trajectory recognition to determine the navigation trajectory attributes corresponding to the monitoring area;

[0008] Based on the static attribute data and the navigation trajectory attributes, calculate the traffic state parameters of the road nodes within the monitoring area during the monitoring time.

[0009] Based on the traffic state parameters, the traffic state of the road node during the monitoring period is determined.

[0010] This application also provides a computer device, including a memory and a processor. The memory stores a computer program; the processor is coupled to the memory and executes the computer program to perform the steps in the traffic condition monitoring method described above.

[0011] This application also provides a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps in the traffic condition monitoring method described above.

[0012] This application also provides a computer program product, including: a computer program; the computer program is executed by a processor to implement the steps in the above-described traffic condition monitoring method.

[0013] In this embodiment, static attribute data and dynamic attribute data of the monitoring area within the monitoring time can be acquired. Trajectory recognition is then performed on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area. Subsequently, traffic state parameters of road nodes within the monitoring area within the monitoring time can be calculated based on the static attribute data and navigation trajectory attributes. Furthermore, the traffic state of road nodes within the monitoring time can be determined based on these traffic state parameters. In this embodiment, based on the static attribute data of the monitoring area and the mining results of the navigation trajectory attributes within the monitoring area, traffic state monitoring of all road nodes within the monitoring area can be achieved, which helps improve the spatial coverage of traffic state monitoring. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0015] Figure 1 A schematic flowchart illustrating the traffic condition monitoring method provided in this application embodiment;

[0016] Figure 2 This is a schematic diagram of the overall framework of the traffic condition monitoring method provided in the embodiments of this application;

[0017] Figure 3 This is a schematic diagram illustrating the distribution of vehicle navigation trajectory data provided in the embodiments of this application;

[0018] Figure 4 This is a practical application example of the traffic condition monitoring method provided in the embodiments of this application;

[0019] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] To improve the spatial coverage of traffic condition monitoring, in some embodiments of this application, static attribute data and dynamic attribute data of the monitoring area within the monitoring time can be acquired. Trajectory recognition is then performed on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area. Subsequently, traffic state parameters of road nodes within the monitoring area within the monitoring time can be calculated based on the static attribute data and navigation trajectory attributes. Furthermore, the traffic state of road nodes within the monitoring time can be determined based on these traffic state parameters. In these embodiments, based on the static attribute data of the monitoring area and the mining results of the navigation trajectory attributes within the monitoring area, traffic condition monitoring of all road nodes within the monitoring area can be achieved, which helps to improve the spatial coverage of traffic condition monitoring.

[0022] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0023] It should be noted that the same reference numerals denote the same object in the following figures and embodiments. Therefore, once an object is defined in one figure or embodiment, it does not need to be discussed further in subsequent figures and embodiments.

[0024] Figure 1 This is a schematic flowchart illustrating the traffic condition monitoring method provided in an embodiment of this application. Figure 1 As shown, the method includes:

[0025] 101. Obtain the static attribute data of the monitoring area and the dynamic attribute data generated by the monitoring area during the monitoring time.

[0026] 102. Perform trajectory recognition on dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area.

[0027] 103. Based on static attribute data and navigation trajectory attributes, calculate the traffic status parameters of road nodes within the monitoring area during the monitoring period.

[0028] 104. Determine the traffic status of road nodes within the monitoring time based on traffic status parameters.

[0029] In this embodiment, the granularity of the monitoring area is not limited; the monitoring area can be any area to be monitored. For example, the monitoring area can be a national area, a city-level area, a district-level area, a county-level area, or any set of roads, etc. Similarly, the granularity of the monitoring time is not limited. Optionally, the monitoring time can be at the minute, hour, day, week, or month level, etc., enabling real-time monitoring of the current time or reviewing traffic conditions for historical monitoring times.

[0030] In step 101, static attribute data of the monitoring area can be obtained. Static attribute data refers to attribute data that remains unchanged or remains unchanged over a relatively long period. For example, Figure 2 The data shown includes road network data for the monitored area, the jurisdiction to which the monitored area belongs, and road node data within the monitored area. For example... Figure 2 As shown, road node data may include, but is not limited to, the topological structure data, morphological attribute data, and the turning attributes of the approach lanes contained in the road node. This data generally does not change. Road nodes can be road intersections (i.e., road junctions) or road endpoints, etc.

[0031] In this embodiment of the application, in order to determine the traffic status of the monitoring area, step 101 may also acquire dynamic attribute data generated by the monitoring area during the monitoring time. Dynamic attribute data refers to attribute data that changes in real time, such as... Figure 2 As shown, the monitored area includes vehicles, their navigation trajectory data, navigation behavior data, and road condition data within the monitored area. These attribute data change over time and are therefore called dynamic attribute data.

[0032] Since the dynamic attribute data generated in the monitoring area during the monitoring period can reflect the driving conditions of vehicles within the monitoring area to a certain extent, and the driving conditions of vehicles can reflect the road traffic conditions to a certain extent, based on this, such as Figure 1 and Figure 2 As shown, in step 102, trajectory recognition can be performed on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area.

[0033] In some embodiments, dynamic attribute data may include vehicle navigation trajectory data and the time when the navigation trajectory data was generated. Vehicle navigation trajectory data can reflect vehicle speed to some extent, and vehicle speed can reflect traffic conditions to some extent. In practical applications, vehicles report their real-time location information, which forms the vehicle's navigation trajectory data. During traffic jams, vehicles travel at slower speeds or even stop, resulting in smaller distances between the real-time location information reported by the vehicle and a higher spatial density of its navigation trajectory points.

[0034] Based on the above analysis, in this embodiment, the navigation trajectory data and the generation time of the navigation trajectory data for each vehicle can be obtained from the dynamic attribute data according to the vehicle identifier. Furthermore, the trajectory density of each vehicle can be calculated based on the navigation trajectory data and the generation time of the navigation trajectory.

[0035] Optionally, the navigation trajectory data of each vehicle can be processed according to the generation time of the navigation trajectory data of each vehicle to obtain the navigation trajectory distribution sequence of each vehicle. For any trajectory point P in the navigation trajectory distribution sequence, the number of trajectory points of vehicles within a preset neighborhood of trajectory point P is counted, which is taken as the trajectory density of that vehicle in the preset neighborhood of trajectory point A. Here, the preset neighborhood refers to the distance neighborhood, such as 1 meter, 3 meters, 5 meters, or 8 meters, etc. For ease of description, the preset neighborhood is defined as the ε neighborhood, and the preset distance is defined as the ε distance. That is, the number of trajectory points whose distance from trajectory point P is less than or equal to the ε distance is counted.

[0036] After determining the trajectory density of each vehicle, the navigation trajectory attributes corresponding to the monitoring area can be determined based on the trajectory density of each vehicle.

[0037] Optionally, the number of vehicle trajectory points within a preset neighborhood of any trajectory point P is used as a criterion to determine whether the number of vehicle trajectory points within the preset neighborhood of trajectory point P is greater than or equal to a preset threshold. If the determination result is yes, it is determined that the vehicle has stopped.

[0038] Furthermore, vehicle parking trajectory data corresponding to the monitoring area can be determined based on trajectory point P and other trajectory points that are density-reachable from trajectory point P, and this vehicle parking trajectory data can be used as the navigation trajectory attribute corresponding to the aforementioned monitoring area. Optionally, trajectory point P and other trajectory points that are density-reachable from trajectory point P can be determined as the vehicle parking trajectory data corresponding to the monitoring area. Alternatively, trajectory point P, the trajectory points of vehicles within a preset neighborhood of trajectory point P, other trajectory points that are density-reachable from trajectory point P, and the trajectory points of vehicles within a preset neighborhood of other trajectory points P can be determined as the vehicle parking trajectory data corresponding to the monitoring area.

[0039] The other trajectory points that can be reached by the density of trajectory point P can be interpreted as follows: For another core trajectory point Q of trajectory point P, if there are trajectory points whose distance to core trajectory point Q is ≤ ε that are also ≤ ε from trajectory point P, then core trajectory point Q is density reachable from trajectory point P. Here, a core trajectory point refers to a trajectory point whose trajectory point data within a preset neighborhood is greater than or equal to a preset quantity threshold.

[0040] To more clearly explain the process of determining the above vehicle parking trajectory data, in conjunction with, for example Figure 3The diagram illustrating the distribution of vehicle parking trajectory data is provided as an example. Assuming the preset neighborhood is ε-neighborhood and the preset quantity threshold is 5, for trajectory point P, if there are 6 trajectory points within the ε-neighborhood (greater than the quantity threshold 5), then trajectory point P is a core trajectory point. Since trajectory point P1 is within the ε-neighborhood of trajectory point P, trajectory point P1 is directly density-reachable from trajectory point P. Similarly, for trajectory point Q, if there are 5 trajectory points within the ε-neighborhood (equal to the quantity threshold 5), then trajectory point Q is also a core trajectory point. Furthermore, since trajectory point P is within the ε-neighborhood of trajectory point Q, trajectory point P1 is directly density-reachable from trajectory point Q, and trajectory point P is density-reachable from trajectory point Q. Likewise, trajectory point P is density-reachable from trajectory point R. Therefore, trajectory points P, Q, and R constitute a spatial cluster. Further, based on trajectory points P, Q, and R, the vehicle parking trajectory data corresponding to the monitoring area can be determined.

[0041] After determining the navigation trajectory attributes corresponding to the monitoring area, since these attributes represent the navigation trajectory attributes of the entire monitoring area and are not associated with specific road nodes, in order to determine the traffic status of the road nodes, such as... Figure 1 and Figure 2 As shown, in step 103 of this embodiment, the traffic state parameters of road nodes within the monitoring area during the monitoring time can be calculated based on the static attribute data of the monitoring area and the navigation trajectory attributes corresponding to the monitoring area during the monitoring time.

[0042] Optionally, in order to associate navigation trajectory attributes with specific road nodes, road network data of the monitoring area can be obtained from static attribute data; and vehicle parking trajectory data corresponding to each road node in the monitoring area can be determined based on vehicle parking trajectory data and the road network data; further, traffic state parameters of each road node during the monitoring time can be calculated based on the vehicle parking trajectory data corresponding to each road node.

[0043] Optionally, for any road node A, the entrance lane data of road node A can be obtained from the road network data; and the parking position and driving direction of the vehicle can be calculated based on the vehicle parking trajectory data and the generation time of the vehicle parking trajectory data. For example, the center position information of the vehicle parking trajectory data can be calculated as the parking position of the vehicle; furthermore, the driving direction of the vehicle can be determined based on the generation time of the vehicle parking trajectory data and the change of the vehicle parking position.

[0044] Furthermore, the parking location of a vehicle can be matched with the entrance lane data of road node A to identify vehicles whose parking location is in the entrance lane corresponding to road node A; and the parking trajectory data of vehicles whose parking location is in the entrance lane corresponding to road node A can be determined as the parking trajectory data of vehicles corresponding to road node A.

[0045] Furthermore, traffic state parameters of road node A during the monitoring period can be calculated based on the vehicle parking trajectory data corresponding to road node A.

[0046] Traffic state parameters refer to parameters that reflect the traffic status of road nodes. In this embodiment, the specific content of the traffic state parameters is not limited. In some embodiments, such as... Figure 2 As shown, traffic state parameters may include one or more of the following: vehicle queue length, delay duration, number of stops, delay index, service level level, imbalance index, overflow index, and confidence level for road nodes. "Multiple" refers to two or more parameters.

[0047] For the traffic state parameters mentioned above, the longer the vehicle queue length, the worse the traffic state of the corresponding road node. The longer the delay time, the worse the traffic state of the corresponding road node. The more stops, the worse the traffic state of the corresponding road node. The higher the delay index, the worse the traffic state of the corresponding road node. The lower the level of service, the worse the traffic state of the corresponding road node. The imbalance index is the worse the traffic state of imbalanced road nodes compared to unimbalanced ones. The higher the overflow index, the worse the traffic state of the corresponding road node. A higher confidence level indicates a higher accuracy in calculating the other traffic state parameters mentioned above.

[0048] Accordingly, in this embodiment of the application, the traffic state parameters of road node A during the monitoring period are calculated based on the vehicle parking trajectory data corresponding to road node A, which can be achieved by at least one of the following calculation methods:

[0049] Calculation Method 1: For any road node A, calculate the parking position of the vehicle corresponding to road node A based on the vehicle parking trajectory data; calculate the queue length of the vehicle corresponding to road node A based on the parking position of the vehicle corresponding to road node A. Queue length = distance between the last vehicle in the queue and the stop line corresponding to the road node.

[0050] Calculation Method 2: From the vehicle parking trajectory data corresponding to any road node A, determine the parking trajectory data for each vehicle; from the parking trajectory data for each vehicle, select the earliest and latest parking trajectory points for each vehicle; and calculate the time difference between the earliest and latest parking trajectory points as the vehicle delay time. Furthermore, based on the vehicle delay time corresponding to road node A, the delay time corresponding to road node A can be calculated.

[0051] Optionally, the delay time of road node A is equal to the average vehicle delay time of the vehicles corresponding to road node A.

[0052] Calculation Method 3: Based on the vehicle parking trajectory data corresponding to any road node A, count the number of times a single vehicle stops at road node A; based on the number of vehicles and the number of times a single vehicle stops at road node A, calculate the total number of stops at road node A.

[0053] Optionally, the number of stops at road node A is equal to the average number of stops per vehicle at road node A.

[0054] Calculation Method 4: The delay index of road node A can be calculated based on the delay time corresponding to any road node A.

[0055] Optionally, an empirical value can be added to the delay duration of road node A as a delay index for road node A. For example, the delay index for road node A = delay duration of road node A + number of stops at road node A * empirical coefficient. Optionally, the empirical coefficient = 10.

[0056] Calculation Method 5: The service level level (SLA) of any road node A can be determined based on its delay index. Optionally, the delay index of road node A can be matched against a preset correspondence between delay indices and SLA levels to determine the SLA corresponding to the delay index of road node A, which will then be used as the SLA of road node A. Optionally, the SLA of a road node can be set to level AF; where the SLA corresponding to level AF decreases sequentially.

[0057] Calculation Method 6: Based on the service level level (SLL) of any approach road to road node A, the imbalance index of road node A can be determined, and whether road node A is unbalanced can be determined. Road node imbalance can be defined as: a set number of approach roads to the road node have substandard SLL levels. For example, one approach road to the road node may have a substandard SLL level, while the rest of the approach roads have standard SLL levels. Optionally, approach roads with SLL levels E and F can be identified as substandard approach roads.

[0058] Calculation Method 7: Based on the queue length of any road node A determined by Calculation Method 1 above and the distance between road node A and its upstream road nodes, the overflow index corresponding to road node A can be calculated. Optionally, the overflow length where the queue length of road node A exceeds the distance between road node A and its upstream road nodes can be determined as the overflow index of road node A.

[0059] Calculation method 8: The confidence level of road node A can be calculated based on the number of vehicles corresponding to any road node A; the more vehicles corresponding to road node A, the higher the confidence level of road node A.

[0060] It is worth noting that the above calculation methods 1-8 can be implemented individually or in combination. Existing methods monitor traffic conditions through cross-sectional flow monitoring, using monitoring index units. To address this technical problem, the embodiments of this application can employ a combination of multiple calculation methods from 1-8. "Multiple" refers to two or more methods. For embodiments combining multiple calculation methods, compared to single-index monitoring, the traffic condition of road nodes can be determined from multi-dimensional traffic condition parameters, which helps improve the accuracy of the determined traffic condition of road nodes. Furthermore, determining the traffic condition of road nodes from multiple dimensions of traffic condition parameters allows for mutual verification between these parameters, helping to improve the quality and reliability of monitoring data, and further enhancing the accuracy of the subsequently determined traffic condition of road nodes.

[0061] After calculating the traffic state parameters of the road nodes, in step 104, the traffic state of the road nodes during the monitoring time can be determined based on the traffic state parameters of the road nodes within the monitoring area during the monitoring time.

[0062] Optionally, a data dashboard can be generated to display the traffic status of road nodes during the monitoring period, based on the traffic status parameters of the road nodes. Further, such as... Figure 4 As shown, a data dashboard can be displayed to show the traffic status of road nodes during the monitoring period.

[0063] In this context, a data dashboard refers to a format that displays data in an intuitive and visual way. In this embodiment, the format of the data dashboard is not limited. Optionally, the format of the data dashboard may include at least one of the following: image dashboard, text dashboard, number dashboard, progress bar dashboard, graphical dashboard, and list dashboard. Graphical dashboards include at least one of the following: scatter plot dashboard, line graph dashboard, line chart dashboard, histogram dashboard, pie chart dashboard, bullet chart dashboard, area chart dashboard, and waterfall chart dashboard.

[0064] For example, such as Figure 4 As shown, the system can display real-time traffic status parameters of road nodes in the form of text dashboards or list dashboards; it can also statistically analyze the changing trends of traffic status parameters within a monitoring period and display these trends in a graphical dashboard. Figure 4 The illustration is merely an example of how the trend of traffic status parameters changes over the monitoring period, and does not constitute a limitation.

[0065] It is worth noting that the aforementioned monitoring time can be either real-time or historical. Real-time monitoring allows for real-time traffic status monitoring of road nodes, ensuring the timeliness of traffic status monitoring. In this embodiment, different time-scales of monitoring time can also be set to achieve real-time traffic status monitoring with different time granularities. For example, as... Figure 2 As shown, the monitoring time can be at the minute level to achieve minute-level traffic status monitoring of road nodes; of course, the monitoring time can also be at the hour level to achieve hourly or half-hourly traffic status monitoring of road nodes, etc. For historical monitoring time, it is possible to review and analyze the historical traffic status of road nodes, providing a reference for traffic management departments to formulate effective traffic management measures. For example, ... Figure 2 As shown, the historical monitoring time can be the past hour, week, or month, enabling historical traffic status monitoring of road nodes at the past hour, week, or month level. The historical monitoring time can also be past weekdays, holidays, or peak / off-peak hours, enabling historical traffic status monitoring of past weekdays, holidays, or peak / off-peak hours.

[0066] In the above embodiment of determining the traffic status of road nodes using multiple dimensions of traffic status parameters, the traffic status of road nodes can be reflected from different dimensions of traffic status parameters, enabling more refined monitoring of the traffic status of road nodes and providing more granular monitoring of traffic status.

[0067] In this embodiment, static attribute data and dynamic attribute data of the monitoring area within the monitoring time can be acquired. Trajectory recognition is then performed on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area. Subsequently, traffic state parameters of road nodes within the monitoring area within the monitoring time can be calculated based on the static attribute data and navigation trajectory attributes. Furthermore, the traffic state of road nodes within the monitoring time can be determined based on these traffic state parameters. In this embodiment, based on the static attribute data of the monitoring area and the mining results of the navigation trajectory attributes within the monitoring area, traffic state monitoring of all road nodes within the monitoring area can be achieved, which helps improve the spatial coverage of traffic state monitoring.

[0068] Manual surveys are inefficient and rely on sampling, resulting in insufficient temporal and spatial coverage. The traffic condition monitoring method provided in this application, however, can automatically monitor the traffic condition of all road nodes within a monitoring area, significantly improving monitoring efficiency compared to manual surveys. Furthermore, the monitoring time of this improved traffic condition monitoring method can be any time period, which, compared to the time sampling method used in manual surveys, helps improve temporal coverage. To achieve more refined traffic condition monitoring of road nodes, in addition to monitoring the overall traffic condition of road nodes as described above, this application embodiment can also monitor the traffic condition of road nodes by entrance and turn, achieving multi-granularity traffic condition monitoring and thus more refined monitoring. The following is an exemplary description of the specific implementation method of monitoring the traffic condition of road nodes by entrance and turn, as improved in this application embodiment.

[0069] In this embodiment, to monitor the traffic status of road nodes by entrance lanes, for any entrance lane A1 of any road node A within the monitoring area, road network data of the monitoring area can be obtained from the static attribute data of the monitoring area; and entrance lane data of entrance lane A1 can be obtained from the road network data. The entrance lane data may include: the geographical location information of the entrance lane and the direction of the entrance lane. Furthermore, the vehicle parking trajectory data corresponding to entrance lane A1 can be determined based on vehicle parking trajectory data within the monitoring area and the entrance lane data of entrance lane A1.

[0070] Optionally, the parking position of a vehicle can be calculated based on the vehicle parking trajectory data and the time when the vehicle parking trajectory data was generated. Further, the parking position of a vehicle can be matched with the entrance lane data corresponding to entrance lane A1 to identify vehicles whose parking position is located in entrance lane A1; the vehicle parking trajectory data corresponding to the vehicle whose parking position is located in entrance lane A1 is then determined as the vehicle parking trajectory data for entrance lane A1.

[0071] Furthermore, based on the vehicle parking trajectory data of approach lane A1, the traffic state parameters of approach lane A1 during the monitoring period can be calculated to obtain... Figure 2 The diagram shows one or more traffic state parameters obtained by matching the entrance. For details on calculating the traffic state parameters of entrance lane A1 during the monitoring period, please refer to the relevant content in calculation methods 1-8 above, which will not be repeated here.

[0072] Furthermore, such as Figure 2 As shown, the traffic status of approach lane A1 during the monitoring period can be determined based on the traffic status parameters of approach lane A1 during the monitoring period, thereby realizing traffic status monitoring of approach lane A1, that is, realizing traffic status monitoring of approach lanes at road nodes.

[0073] Optionally, a data dashboard can be generated to display the traffic status of approach lane A1 during the monitoring period, based on the traffic status parameters of approach lane A1 during the monitoring period. Furthermore, the data dashboard can be displayed to show the traffic status of approach lane A1 during the monitoring period.

[0074] In addition to monitoring traffic conditions at road junctions by entrance as described above, this embodiment of the application can also monitor traffic conditions at road junctions by turning direction. The main implementation methods are as follows:

[0075] To achieve traffic status monitoring for turning points at road nodes, step 103 can be implemented as follows: Based on vehicle identification, obtain navigation behavior data for each vehicle from the dynamic attribute data of the monitoring area. Navigation behavior data refers to data reflecting vehicle navigation behavior, such as navigation actions (e.g., left turn, right turn, or straight ahead), yaw behavior, route selection behavior, etc. This navigation behavior data reflects vehicle navigation actions to a certain extent. Therefore, based on the navigation behavior data of each vehicle, the navigation action of each vehicle can be predicted; and from the navigation actions of each vehicle, the navigation action of the vehicle corresponding to approach lane A1 can be obtained. Furthermore, for any navigation action supported by the first approach lane (left turn, right turn, or straight ahead, etc., defined as the first navigation action for ease of description), based on the navigation action of the vehicle corresponding to approach lane A1, obtain the vehicle parking trajectory data corresponding to the vehicle with the first navigation action from the vehicle parking trajectory data corresponding to approach lane A1; furthermore, based on the vehicle parking trajectory data corresponding to the vehicle with the first navigation action, calculate the traffic state parameters of approach lane A1 under the first navigation action during the monitoring time, and obtain... Figure 2 The diagram shows one or more traffic state parameters obtained by matching navigation actions. For details on the specific implementation of calculating the traffic state parameters of approach lane A1 under the first navigation action within the monitoring time, please refer to the relevant content in calculation methods 1-8 above, which will not be repeated here.

[0076] Furthermore, such as Figure 2 As shown, the traffic status of approach lane A1 under the first navigation action can be determined based on the traffic status parameters of approach lane A1 under the first navigation action within the monitoring time, thereby realizing traffic status monitoring of road nodes under the sub-navigation actions, that is, traffic status monitoring of road nodes under the sub-navigation actions is realized.

[0077] Optionally, a data dashboard can be generated based on the traffic status parameters of approach lane A1 under the first navigation action within the monitoring period. Furthermore, the data dashboard can be displayed to show the traffic status of approach lane A1 under the first navigation action within the monitoring period. Figure 4The illustrations use left turns, right turns, and straight-ahead movements as examples of the first navigation actions, but this is not intended to be limiting.

[0078] Of course, when monitoring traffic conditions at road nodes for different entry points and navigation actions, the monitoring time can be either real-time or historical. Real-time monitoring allows for real-time traffic condition monitoring of road node entry points and navigation actions, ensuring the timeliness of traffic condition monitoring. In this embodiment, different time-scale monitoring times can also be set to achieve real-time traffic condition monitoring of entry points and navigation actions at different time granularities.

[0079] Based on historical monitoring time, it is possible to review and analyze the historical traffic status of road nodes, including entrances and road actions, providing a reference for traffic management departments to formulate effective traffic management measures.

[0080] It should be noted that the execution subject of each step of the method provided in the above embodiments can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 101 and 102 can be device A; or the execution subject of step 101 can be device A, and the execution subject of step 102 can be device B; and so on.

[0081] Furthermore, some processes described in the above embodiments and accompanying drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel.

[0082] Accordingly, embodiments of this application also provide a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause one or more processors to perform the steps in the traffic condition monitoring method described above.

[0083] This application also provides a computer program product, including a computer program. When the computer program is executed by a processor, it can implement the steps in the traffic condition monitoring method described above. In this application, the specific implementation form of the computer program product is not limited. In some embodiments, the computer program product can be implemented as traffic monitoring application software, which can be used or installed by traffic management departments. In other embodiments, the computer program product can be implemented as navigation application software, or it can also be implemented as life service software, such as ride-hailing application software or food delivery application software. Of course, the computer program product provided in this application can also be deployed in the cloud to implement traffic monitoring service software. For example, it can be implemented as a SaaS-based traffic monitoring service software.

[0084] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of this application. For example... Figure 5 As shown, the computer device may include a memory 50a and a processor 50b. The memory 50a is used to store computer programs.

[0085] The processor 50b is coupled to the memory 50a and is used to execute a computer program for: acquiring static attribute data of the monitoring area and dynamic attribute data generated in the monitoring area during the monitoring time; performing trajectory recognition on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area; calculating the traffic state parameters of road nodes in the monitoring area during the monitoring time based on the static attribute data and navigation trajectory attributes; and determining the traffic state of road nodes during the monitoring time based on the traffic state parameters.

[0086] Optionally, when performing trajectory recognition on dynamic attribute data, the processor 50b is specifically used to: obtain the navigation trajectory data of each vehicle and the generation time of the navigation trajectory data from the dynamic attribute data based on the vehicle identifier; calculate the trajectory density of each vehicle based on the navigation trajectory data and the generation time of the navigation trajectory; and determine the navigation trajectory attribute corresponding to the monitoring area based on the trajectory density of each vehicle.

[0087] Furthermore, when calculating the trajectory density of vehicles, the processor 50b specifically performs the following steps: processing the navigation trajectory data of each vehicle according to the generation time of the navigation trajectory data of each vehicle to obtain the corresponding navigation trajectory distribution sequence of each vehicle; for any trajectory point in the navigation trajectory distribution sequence, counting the number of trajectory points of vehicles in a preset neighborhood of any trajectory point, as the trajectory density of each vehicle in the preset neighborhood of any trajectory point.

[0088] Optionally, when determining the navigation trajectory attributes corresponding to the monitoring area, the processor 50b is specifically used to: determine whether the number of vehicle trajectory points in the preset neighborhood of any trajectory point is greater than or equal to a preset number threshold; if the determination result is yes, determine that the vehicle has stopped, and determine any trajectory point and other trajectory points that can be reached by any trajectory point density; and determine the corresponding vehicle parking trajectory data in the monitoring area based on any trajectory point and other trajectory points that can be reached by any trajectory point density.

[0089] In some embodiments, the navigation trajectory attributes include: vehicle parking trajectory data. When calculating the traffic state parameters of road nodes within the monitoring area during the monitoring time, the processor 50b is specifically configured to: obtain road network data of the monitoring area from static attribute data; determine the vehicle parking trajectory data corresponding to each road node within the monitoring area based on the vehicle parking trajectory data and the road network data; and calculate the traffic state parameters of each road node during the monitoring time based on the vehicle parking trajectory data corresponding to that road node.

[0090] Accordingly, when determining the vehicle parking trajectory data corresponding to each road node within the monitoring area, the processor 50b specifically performs the following: for any road node, it obtains the entrance lane data of that road node from the road network data; calculates the parking position and driving direction of the vehicle based on the vehicle parking trajectory data and the generation time of the vehicle parking trajectory data; matches the vehicle parking position with the entrance lane data of any road node to determine the vehicle whose parking position is located in the entrance lane corresponding to any road node; and determines the vehicle parking trajectory data of the vehicle whose parking position is located in the entrance lane corresponding to any road node, which is the vehicle parking trajectory data corresponding to any road node.

[0091] Optionally, when calculating the traffic state parameters of the road node during the monitoring period, it is specifically used for:

[0092] For any given road node, calculate the parking position of the vehicle corresponding to that road node based on the vehicle parking trajectory data; and calculate the queue length of the vehicle corresponding to that road node based on the parking position of the vehicle corresponding to that road node.

[0093] And / or,

[0094] From the vehicle parking trajectory data corresponding to any road node, determine the vehicle parking trajectory data corresponding to each vehicle; from the vehicle parking trajectory data corresponding to each vehicle, select the earliest and latest parking trajectory points; calculate the time difference between the earliest and latest parking trajectory points as the vehicle delay time corresponding to that vehicle; based on the vehicle delay time corresponding to any road node, calculate the delay time corresponding to any road node.

[0095] And / or,

[0096] Based on the vehicle parking trajectory data corresponding to any road node, count the number of times a single vehicle stops at any road node; based on the number of vehicles and the number of times a single vehicle stops at any road node, calculate the total number of stops at any road node.

[0097] And / or,

[0098] Calculate the delay index for any road node based on the delay duration corresponding to any target road node;

[0099] And / or,

[0100] Determine the service level level of any road node based on its delay index.

[0101] And / or,

[0102] Based on the service level level of the approach road corresponding to any road node, determine the imbalance index of any road node.

[0103] And / or,

[0104] Calculate the overflow index corresponding to any road node based on the queue length and the distance between any road node and its upstream road node.

[0105] And / or,

[0106] The confidence level of any road node is calculated based on the number of vehicles corresponding to that road node; the higher the number of vehicles, the higher the confidence level.

[0107] In other embodiments, the navigation trajectory attributes include vehicle parking trajectory data. When calculating traffic state parameters of road nodes within the monitoring area during the monitoring time, the processor 50b specifically performs the following: for the first entrance of any road node, it obtains road network data of the monitoring area from static attribute data; obtains entrance lane data of the first entrance lane from the road network data; determines the vehicle parking trajectory data corresponding to the first entrance lane based on the vehicle parking trajectory data and the entrance lane data of the first entrance lane; and calculates the traffic state parameters of the first entrance lane of any road node during the monitoring time based on the vehicle parking trajectory data corresponding to the first entrance lane.

[0108] Accordingly, when processor 50b calculates the traffic state parameters of road nodes within the monitoring area during the monitoring time, it specifically performs the following: Based on vehicle identification, it obtains navigation behavior data for each vehicle from dynamic attribute data; based on the navigation behavior data for each vehicle, it predicts the navigation action of each vehicle; from the navigation actions of each vehicle, it obtains the navigation action of the vehicle corresponding to the first approach lane; for the first navigation action supported by the first approach lane, based on the navigation action of the vehicle corresponding to the first approach lane, it obtains the vehicle parking trajectory data corresponding to the vehicle with the first navigation action from the vehicle parking trajectory data corresponding to the first approach lane; and based on the vehicle parking trajectory data corresponding to the vehicle with the first navigation action, it calculates the traffic state parameters of the first approach lane under the first navigation action during the monitoring time.

[0109] In this embodiment of the application, when the processor 50b determines the traffic status of the road node within the monitoring time, it is specifically used to: generate a data dashboard to display the traffic status of the road node within the monitoring time based on the traffic status parameters of the road node within the monitoring time.

[0110] Optionally, the processor 50b may also: display the data dashboard for showing the traffic status of the road node during the monitoring period via the display component 50c; or, send the data corresponding to the data dashboard for showing the traffic status of the road node during the monitoring period to the terminal requesting the traffic status of the road node via the communication component 50d.

[0111] In some alternative implementations, such as Figure 5 As shown, the computer device may also include optional components such as a power supply component 50e and an audio component 50f. Figure 5 The diagram only shows some components and does not mean that the computer device must contain them. Figure 5 The inclusion of all components does not imply that a computer device can only include... Figure 5 The components shown.

[0112] The computer device provided in this embodiment can acquire static attribute data and dynamic attribute data of the monitoring area within the monitoring time; and perform trajectory recognition on the dynamic attribute data to determine the navigation trajectory attributes corresponding to the monitoring area; then, based on the static attribute data and navigation trajectory attributes, it can calculate the traffic state parameters of road nodes within the monitoring area within the monitoring time; and based on the traffic state parameters of road nodes within the monitoring time, it can determine the traffic state of road nodes within the monitoring time. In this embodiment, based on the static attribute data of the monitoring area and the mining results of the navigation trajectory attributes within the monitoring area, traffic state monitoring of all road nodes within the monitoring area can be realized, which helps to improve the spatial coverage of traffic state monitoring.

[0113] In this embodiment, the memory is used to store computer programs and can be configured to store various other data to support operation on its host device. The processor can execute the computer programs stored in the memory to implement corresponding control logic. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0114] In the embodiments of this application, the processor can be any hardware processing device capable of executing the above-described method logic. Optionally, the processor can be a central processing unit (CPU), a graphics processing unit (GPU), or a microcontroller unit (MCU); it can also be a field-programmable gate array (FPGA), a programmable array logic (PAL), a general array logic (GAL), a complex programmable logic device (CPLD), or other programmable devices; or it can be an advanced reduced instruction set (RISC) processor (ARM) or a system on chip (SOC), etc., but is not limited thereto.

[0115] In this embodiment, the communication component is configured to facilitate wired or wireless communication between its host device and other devices. The device housing the communication component can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G, 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In another exemplary embodiment, the communication component may also be implemented based on Near Field Communication (NFC), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), Bluetooth (BT), or other technologies.

[0116] In embodiments of this application, the display component may include a liquid crystal display (LCD) and a touch panel (TP). If the display component includes a touch panel, the display component may be implemented as a touchscreen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may not only sense the boundaries of touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation.

[0117] In this embodiment, a power supply component is configured to provide power to various components of the device in which it resides. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply component resides.

[0118] In embodiments of this application, the audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), which is configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals. For example, in devices with voice interaction capabilities, voice interaction with the user can be achieved through the audio component.

[0119] It should be noted that the terms "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0125] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0126] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0127] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0128] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A traffic condition monitoring method, wherein, include: Acquire static attribute data of the monitoring area and dynamic attribute data of the monitoring area generated during the monitoring time; Based on the vehicle identifier, the navigation trajectory data of each vehicle and the generation time of the navigation trajectory data are obtained from the dynamic attribute data; The trajectory density of each vehicle is calculated based on the navigation trajectory data of each vehicle and the time when the navigation trajectory was generated. Based on the trajectory density of each vehicle, the navigation trajectory attributes corresponding to the monitoring area are determined, wherein the navigation trajectory attributes include vehicle parking trajectory data; Based on the static attribute data and the navigation trajectory attributes, calculate the traffic state parameters of the road nodes within the monitoring area during the monitoring time. Based on the traffic state parameters, the traffic state of the road node during the monitoring period is determined.

2. The method according to claim 1, wherein, in, The step of calculating the trajectory density of the vehicles based on the navigation trajectory data of each vehicle and the generation time of the navigation trajectory data includes: Based on the generation time of the navigation trajectory data of each vehicle, the navigation trajectory data of each vehicle is processed to obtain the corresponding navigation trajectory distribution sequence of each vehicle. For any trajectory point in the navigation trajectory distribution sequence, the number of trajectory points of the vehicle in a preset neighborhood of the trajectory point is counted, which is used as the trajectory density of each vehicle in the preset neighborhood of the trajectory point.

3. The method according to claim 2, wherein, Based on the trajectory density of the vehicles, the navigation trajectory attributes corresponding to the monitoring area are determined, including: Determine whether the number of trajectory points of the vehicle within a preset neighborhood of any trajectory point is greater than or equal to a preset number threshold; If the judgment result is yes, it is determined that the vehicle has stopped, and the density of any trajectory point and other trajectory points reachable by the density of any trajectory point are determined. Based on any given trajectory point and other trajectory points whose density is reachable from that trajectory point, determine the corresponding vehicle parking trajectory data within the monitoring area.

4. The method according to claim 1, wherein, The navigation trajectory attributes include: vehicle parking trajectory data. The step of calculating traffic state parameters of road nodes within the monitoring area during the monitoring time based on the static attribute data and the navigation trajectory attributes includes: Obtain the road network data of the monitoring area from the static attribute data; Based on the vehicle parking trajectory data and the road network data, determine the vehicle parking trajectory data corresponding to each road node within the monitoring area; Based on the vehicle parking trajectory data corresponding to each road node, the traffic state parameters of that road node are calculated during the monitoring period.

5. The method according to claim 4, wherein, The step of determining the vehicle parking trajectory data corresponding to each road node within the monitoring area based on the vehicle parking trajectory data and the road network data includes: For any road node, obtain the entrance road data of that road node from the road network data; Based on the vehicle parking trajectory data and the time when the vehicle parking trajectory data was generated, the parking position and driving direction of the vehicle are calculated. The parking location of the vehicle is matched with the entrance lane data of any road node to determine the vehicle whose parking location is located in the entrance lane corresponding to any road node. The vehicle parking trajectory data of the vehicle whose parking location is located in the entrance lane corresponding to any of the road nodes is determined as the vehicle parking trajectory data corresponding to any of the road nodes.

6. The method according to claim 4, wherein, The step of calculating the traffic state parameters of each road node within the monitoring time period based on the vehicle parking trajectory data corresponding to each road node includes: For any road node, calculate the parking position of the vehicle corresponding to the road node based on the vehicle parking trajectory data corresponding to the road node; calculate the queue length of the vehicle corresponding to the road node based on the parking position of the vehicle corresponding to the road node. And / or, From the vehicle parking trajectory data corresponding to any road node, determine the vehicle parking trajectory data corresponding to each vehicle; from the vehicle parking trajectory data corresponding to each vehicle, select the earliest and latest parking trajectory points; calculate the time difference between the earliest and latest parking trajectory points as the vehicle delay time corresponding to that vehicle; calculate the delay time corresponding to any road node based on the vehicle delay time corresponding to the vehicle at any road node. And / or, Based on the vehicle parking trajectory data corresponding to any road node, count the number of times a single vehicle parks at any road node; based on the number of vehicles and the number of times a single vehicle parks at any road node, calculate the total number of times a vehicle parks at any road node. And / or, Calculate the delay index of any road node based on the delay duration corresponding to any road node; And / or, Based on the delay index of any road node, determine the service level level of any road node; And / or, Based on the service level level of the approach road corresponding to any road node, determine the imbalance index of any road node; And / or, Calculate the overflow index corresponding to any road node based on the queue length and the distance between any road node and its upstream road node; And / or, The confidence level of any road node is calculated based on the number of vehicles corresponding to that road node; wherein, the higher the number of vehicles, the higher the confidence level.

7. The method according to claim 1, wherein, The navigation trajectory attributes include: vehicle parking trajectory data. The step of calculating traffic state parameters of road nodes within the monitoring area during the monitoring time based on the static attribute data and the navigation trajectory attributes includes: For the first entrance of any road node, obtain the road network data of the monitoring area from the static attribute data; Obtain the entrance data of the first entrance road from the road network data; Based on the vehicle parking trajectory data and the entrance lane data of the first entrance lane, determine the vehicle parking trajectory data corresponding to the first entrance lane; Based on the vehicle parking trajectory data corresponding to the first approach lane, calculate the traffic state parameters of the first approach lane of any road node during the monitoring period.

8. The method according to claim 7, wherein, The step of calculating the traffic state parameters of road nodes within the monitoring area during the monitoring time based on the static attribute data and the navigation trajectory attributes further includes: Based on the vehicle identifier, navigation behavior data for each vehicle is obtained from the dynamic attribute data; Based on the navigation behavior data of each vehicle, predict the navigation action of each vehicle; From the navigation actions of each vehicle, obtain the navigation actions of the vehicle corresponding to the first entrance lane; For the first navigation action supported by the first entrance lane, based on the navigation action of the vehicle corresponding to the first entrance lane, the vehicle parking trajectory data corresponding to the vehicle with the first navigation action is obtained from the vehicle parking trajectory data corresponding to the first entrance lane. Based on the vehicle parking trajectory data corresponding to the vehicle in the first navigation action, the traffic state parameters of the first entrance lane under the first navigation action are calculated within the monitoring time.

9. The method according to any one of claims 1-8, wherein, Determining the traffic state of the road node within the monitoring time period based on the traffic state parameters of the road node within the monitoring time period includes: Based on the traffic status parameters of the road nodes during the monitoring period, a data dashboard is generated to display the traffic status of the road nodes during the monitoring period.

10. A computer program product comprising: A computer program; said computer program is executed by a processor to perform the steps of the method according to any one of claims 1-9.

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