Traffic information fusion sharing method and system based on multi-source heterogeneous data

Abstract

The invention relates to the technical field of intelligent traffic, in particular to a traffic information fusion sharing method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining basic traffic data of current road traffic participants based on a plurality of traffic data sources; constructing a traffic network topology according to the target city road network, and mapping the basic traffic data to obtain traffic spatio-temporal data; when a traffic event occurs, locking traffic spatio-temporal data from the current moment and analyzing event change characteristics; constructing a dynamic traffic network by taking the traffic spatio-temporal data as nodes and node attributes of the traffic network topology and the event change characteristics as characteristics of connecting lines between different nodes; and matching and sharing the real-time change of the dynamic traffic network in the sharing range according to the demand of the target sharing object. According to the method, the problems of dynamic decision lag and resource scheduling misalignment caused by difficulty in real-time quantitative prediction of traffic event propagation due to space-time reference and structural difference of multi-source heterogeneous traffic data are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a traffic information fusion and sharing method and system based on multi-source heterogeneous data. BACKGROUND

[0002] Traffic information fusion and sharing is the core link of intelligent transportation system, aiming to integrate multi-source heterogeneous data such as roadside sensors, floating cars, traffic control platforms and user terminals, generate global traffic situation through unified analysis, provide real-time decision support for traffic management departments, the public and third-party service providers, and ultimately realize the three goals of real-time event perception, accurate impact prediction and dynamic resource scheduling. The current mainstream technology adopts a hierarchical processing architecture: in the data layer, multi-source data is extracted by relying on ETL tools, and coarse-grained spatial alignment is achieved through coordinate conversion; in the event layer, a rule engine based on threshold is used for anomaly detection; in the sharing layer, the full-quantity road network state data is pushed through API interface at a fixed frequency.

[0003] However, in the process of data processing interaction, the above hierarchical processing architecture can abstractly process unrelated single data through basic coordinate projection transformation, but in traffic events, multi-source heterogeneous traffic data is usually formed, and there are multi-source data space-time, the complexity and correlation of data processing are greatly increased. It is difficult to dynamically and quantitatively predict the impact and evolution trend of traffic events through coordinate conversion combined with threshold anomaly detection, and it is also difficult to accurately understand the road network state data in time, resulting in decision lag and failure, and serious inaccuracy of resource scheduling. SUMMARY

[0004] In view of at least one of the above technical problems, the present application provides a traffic information fusion and sharing method and system based on multi-source heterogeneous data, which constructs a dynamic traffic network and analyzes the event change characteristics to realize real-time quantitative prediction and dynamic decision support of multi-source heterogeneous traffic data, and can effectively solve the problems in the background art.

[0005] The present application provides a traffic information fusion and sharing method based on multi-source heterogeneous data, comprising:

[0006] Obtaining basic traffic data of current road traffic participants based on multiple traffic data sources;

[0007] According to the traffic network topology of the target city, the basic traffic data is mapped to the traffic network topology in the unified space-time coordinate system to obtain traffic space-time data;

[0008] When a traffic event occurs, the traffic space-time data from the current time is locked and the event change characteristics of the traffic event are analyzed;

[0009] The traffic spatio-temporal data is taken as nodes and node attributes of the traffic network topology, and the event change feature is taken as a feature of a connection line between different nodes, and a dynamic traffic network is constructed.

[0010] According to the demand of the target shared object, real-time changes of the dynamic traffic network in the sharing range are matched and shared.

[0011] In some embodiments of the present application, the traffic network topology is established based on road geometric distribution and intersection connection relationship of the urban road network, the nodes of the traffic network topology correspond to each intersection, and the connection line between the nodes represents a passable path connecting these nodes based on the road geometric distribution;

[0012] The start and end positions of the passable path are defined as the nodes closest in distance in the passable path, and the traffic data source is arranged at each node.

[0013] In some embodiments of the present application, the event change feature of the traffic event is analyzed, including:

[0014] The core node in the traffic network topology is locked based on the traffic spatio-temporal data of the traffic event;

[0015] The traffic change amount of each passable path associated with the core node is obtained, and the associated influence node is expanded layer by layer to generate a spatial influence domain;

[0016] The traffic change amount in the spatial influence domain is monitored in real time, and the evolution stage of the traffic event is divided;

[0017] The event change feature of the spatial influence domain is obtained based on the evolution stage, and the event change feature is updated in real time with the traffic change amount.

[0018] In some embodiments of the present application, the evolution stage of the traffic event is divided, including:

[0019] When the traffic change amount of the core node and the associated influence node in the spatial influence domain satisfies that the increment rate of three consecutive monitoring periods exceeds a first critical rate, it is defined as a burst stage;

[0020] When the traffic change amount satisfies that the decrement rate of three consecutive monitoring periods exceeds a second critical rate, and the boundary shrinkage of the spatial influence domain reaches a preset minimum boundary, it is defined as a dissipation stage.

[0021] When the burst stage or the dissipation stage condition is satisfied, the traffic change amount is converted to a fluctuation amplitude that continuously locates in a preset tolerance band and a fluctuation state that maintains at least five consecutive monitoring periods, and it is defined as a stable stage.

[0022] wherein the first critical rate and the second critical rate are based on the traffic change amount of each of the nodes in a historical same period.

[0023] In some embodiments of the present application, the real-time changes of the dynamic traffic network within the sharing range are matched and shared, including:

[0024] obtaining demand information of the target sharing object, the demand information including a sharing range, a sharing content type, and a sharing frequency;

[0025] obtaining real-time change data corresponding to the sharing range in the dynamic traffic network based on the demand information, wherein the real-time change data includes the real-time changed traffic spatio-temporal data and the event change characteristics;

[0026] format processing the real-time change data according to the sharing content type to generate standardized sharing data;

[0027] pushing the standardized sharing data to the target sharing object according to the sharing frequency.

[0028] In some embodiments of the present application, obtaining real-time change data corresponding to the sharing range in the dynamic traffic network based on the demand information includes:

[0029] filtering target nodes and associated connection lines in the dynamic traffic network whose spatial positions are within the sharing range based on the sharing range;

[0030] obtaining the traffic spatio-temporal data of the target nodes in a corresponding time range and synchronously obtaining the event change characteristics of the associated connection lines in the same time range according to the sharing frequency;

[0031] integrating the traffic spatio-temporal data and the event change characteristics into the real-time change data.

[0032] In some embodiments of the present application, pushing the standardized sharing data to the target sharing object according to the sharing frequency includes:

[0033] determining whether the sharing frequency is higher than a preset threshold, and if so, pushing the standardized sharing data to the target sharing object in real time according to a shortest time interval corresponding to the sharing frequency;

[0034] if not, aggregating the standardized sharing data of continuous multiple monitoring periods to generate aggregated data and then pushing according to the sharing frequency;

[0035] associating a current evolution stage label of the event change characteristics when pushing.

[0036] When the event change feature enters the outbreak stage or the dissipation stage, the immediate triggering of the real-time change data incremental push is ignored.

[0037] In some embodiments of the application, the basic traffic data includes:

[0038] Static attribute data, the static attribute data including the geometric topology of the road network, the speed limit area setting, the traffic sign and marking position;

[0039] Dynamic behavior data, the dynamic behavior data representing the real-time motion state and interaction behavior of the road traffic participants;

[0040] Environment perception data, the environment perception data including the monitoring data of the external environment by the sensor;

[0041] User interaction data, the user interaction data including the event information, the traffic condition feedback, the navigation query record, the route preference setting actively reported by the road traffic participants.

[0042] The application also provides a traffic information fusion and sharing system based on multi-source heterogeneous data, comprising:

[0043] A data acquisition module, acquiring the basic traffic data of the current road traffic participants based on multiple traffic data sources;

[0044] A road network topology module, constructing a traffic network topology according to the target city road network, and mapping the basic traffic data to the traffic network topology under the unified space-time coordinate system to obtain traffic space-time data;

[0045] A feature analysis module, locking the traffic space-time data from the current time and analyzing the event change feature of the traffic event when the traffic event occurs;

[0046] A dynamic correlation module, taking the traffic space-time data as the node and node attribute of the traffic network topology, and taking the event change feature as the feature of the connecting line between different nodes, to construct a dynamic traffic network;

[0047] A matching and sharing module, matching and sharing the real-time change of the dynamic traffic network within the sharing range according to the demand of the target sharing object.

[0048] In some embodiments of the application, the matching and sharing module includes:

[0049] A demand acquisition unit, acquiring the demand information of the target sharing object, the demand information including the sharing range, the sharing content type and the sharing frequency;

[0050] The range corresponding unit obtains real-time change data corresponding to the sharing range in the dynamic traffic network based on the demand information, wherein the real-time change data includes real-time change traffic space-time data and event change characteristics;

[0051] The standard processing unit performs format processing on the real-time change data according to the sharing content type, and generates standardized sharing data;

[0052] The data pushing unit pushes the standardized sharing data to the target sharing object according to the sharing frequency.

[0053] The beneficial effects of the present application are: by constructing a dynamic traffic network and analyzing event change characteristics, real-time quantitative prediction and dynamic decision support of multi-source heterogeneous data are achieved, effectively solving the problem that multi-source heterogeneous traffic data is difficult to be quantitatively predicted in real time due to the space-time reference and structural differences, causing dynamic decision lag and resource scheduling error. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0055] Figure 1 The flowchart of the traffic information fusion and sharing method based on multi-source heterogeneous data in the present application;

[0056] Figure 2 The flowchart of analyzing event change characteristics of traffic events in the present application;

[0057] Figure 3 The flowchart of matching and sharing of real-time changes of dynamic traffic network in the sharing range in the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0060] The application provides a traffic information fusion sharing method based on multi-source heterogeneous data Figure 1 The traffic information fusion sharing method based on multi-source heterogeneous data shown in the figure comprises the following steps:

[0061] S10: Obtain basic traffic data of current road traffic participants based on multiple traffic data sources;

[0062] S20: Construct a traffic network topology according to a target city road network, and map the basic traffic data to the traffic network topology in a unified space-time coordinate system to obtain traffic space-time data;

[0063] S30: When a traffic event occurs, lock the traffic space-time data from the current time and analyze the event change characteristics of the traffic event;

[0064] S40: Take the traffic space-time data as the nodes and node attributes of the traffic network topology, and take the event change characteristics as the characteristics of the connecting lines between different nodes, and construct a dynamic traffic network;

[0065] S50: According to the demand of a target sharing object, match and share the real-time changes of the dynamic traffic network within the sharing range.

[0066] Specifically, first, the basic traffic data of current road traffic participants is obtained through multiple traffic data sources, which can include vehicle GPS data, road monitoring cameras, sensing devices, and mobile phone applications, etc. These data sources can provide heterogeneous data types such as location, speed, timestamp, etc., so preprocessing is needed to ensure data consistency and accuracy. Next, according to the road network of the target city, a detailed traffic network topology is constructed, which not only considers the physical structure and connection relationship of the road, but also maps the basic traffic data to a unified space-time coordinate system. This process involves complex data conversion and mapping techniques, the purpose of which is to integrate the information provided by heterogeneous data sources into a standardized space-time framework to obtain traffic space-time data. When a traffic event occurs, traffic space-time data is monitored to lock the data to identify and analyze the dynamic change characteristics of the traffic event. For example, an accident can cause congestion on a certain road to intensify, affecting the traffic flow of the surrounding road network. By comparing the traffic space-time data before and after the event, the change characteristics of the event can be identified, including changes in traffic flow, average vehicle speed, and expansion of congestion areas, etc. Based on the traffic space-time data, a dynamic traffic network is constructed, in which the nodes of the road network topology correspond to the location and attributes of traffic participants, and the connection lines between the nodes represent the change characteristics of the event. This dynamic network reflects the influence degree and range of the traffic event in real time and can be used to further analyze the interaction between different nodes and the congestion propagation pattern. Finally, according to the needs of the target sharing object, the real-time changes of the dynamic traffic network within the sharing range are realized through matching algorithms. The sharing mechanism can actively adjust the sharing content and timeliness according to the priority and needs of different objects. For example, the traffic management department may need real-time network status, while the navigation application may need detailed traffic information in a specific area.

[0067] Through the technical solutions of the present application, the problem of dynamic decision lag and resource scheduling error caused by the difficulty of real-time quantitative prediction of traffic event propagation due to the differences in space-time reference and structure of multi-source heterogeneous traffic data is effectively solved.

[0068] In some embodiments of the present application, based on the road geometry distribution and intersection connection relationship of the urban road network, a traffic network topology is established, and the nodes of the traffic network topology correspond to each intersection, and the connection lines between the nodes represent the passable paths connecting these nodes based on the road geometry distribution.

[0069] Among them, the start and end positions of the passable path are defined as the nodes closest in distance in the passable path, and the traffic data sources are set at each node.

[0070] The geometric distribution and intersection connection information of all roads are obtained through city planning maps and actual surveying and mapping data, which should detail the length, width, curvature of each road and the connection relationship between roads. The intersection, as a node of the traffic network topology, its definition not only includes the geographical position, but also includes the road type connected thereto and the traffic signal lamp setting; based on these detailed geographical and geometric data, a traffic network topology structure including multiple nodes and connection lines is constructed, in which each node corresponds to an actual intersection, the selection of these intersections is based on the density and importance of traffic flow data, therefore, the main and secondary intersections are selected to ensure the connectivity of the network and the effective coverage of data, the connection lines are drawn according to the road geometric distribution, and the setting of the connection lines considers the length and passability of the road, so as to truly reflect the actual traffic situation; the passability of the path is defined as whether the process from one node to another node is limited by the road and traffic facilities, the start and end positions need to be determined in this process to design the shortest or optimized path flow, the start position of the path is set at the nearest geographical position from the node, so as to facilitate efficient monitoring of the change of traffic flow, in addition, traffic data sources such as detectors and sensors are set on each node to ensure the real-time and accuracy of data, these devices are responsible for collecting information such as toll gate vehicle flow, speed and congestion; the data sources on each node are adjusted and optimized according to the position and connected road type, so as to ensure that different types of data can work together to build a dynamic traffic network structure, at the same time, for different traffic events such as congestion or accident, the influence of the change of these data on the traffic flow is considered in the path planning, so as to facilitate rapid analysis and processing.

[0071] In some embodiments of the present application, as shown in Figure 2 the event change characteristics of the traffic event are analyzed, including:

[0072] Locking the core node in the traffic network topology based on the traffic spatio-temporal data of the traffic event;

[0073] Obtaining the traffic change amount of each passable path associated with the core node and expanding the associated influence node layer by layer to generate a spatial influence domain;

[0074] Real-time monitoring of the traffic change amount in the spatial influence domain to divide the evolution stage of the traffic event;

[0075] Obtaining the event change characteristics of the spatial influence domain based on the evolution stage, wherein the event change characteristics are updated in real time with the traffic change amount.

[0076] When a traffic event occurs, the entire network is scanned based on real-time traffic spatio-temporal data, and the core nodes in the traffic network topology are locked. The core nodes are usually important intersections in the event area or its adjacent area, and these nodes are defined as positions where traffic flow changes are concentrated. The selection of the core nodes is based on factors including sudden changes in traffic flow and sharp rises in congestion index. Once the core nodes are identified, it is crucial to obtain the traffic changes of the passable paths associated with these nodes. Traffic changes refer to the dynamic changes of traffic on each path under the influence of the event. The data sources can be field sensors, camera feedback, and historical event data models. When the traffic changes are too large, it may indicate that the influence range of the event is expanding. By expanding these nodes affected by changes layer by layer, a dynamic spatial influence domain can be constructed. The spatial influence domain is a region containing all nodes and related paths affected by the event, and it reflects the dynamic influence range of the event in real time. To monitor the spatial influence domain, it is necessary to continuously track the traffic changes of each node and path, and divide the evolution stages of the traffic event based on these data. Each stage of the evolution stage has different characteristics. Based on the data of these evolution stages, the event change characteristics of the nodes and paths in the spatial influence domain are updated in real time. These characteristics include traffic flow change speed and direction change, aiming to provide clear event image prediction. Dynamic updating of this process ensures the real-time nature of traffic information, realizes timely adjustment of traffic management and public transportation facilities, and further optimizes traffic resource allocation.

[0077] In some embodiments of the present application, the evolution stages of the traffic event are divided, including:

[0078] When the traffic changes of the core nodes and the associated affected nodes in the spatial influence domain meet the condition that the increment rate exceeds the first critical rate for three consecutive monitoring periods, it is defined as the outbreak stage.

[0079] When the traffic changes meet the condition that the decrement rate exceeds the second critical rate for three consecutive monitoring periods, and the boundary of the spatial influence domain shrinks to the preset minimum boundary, it is defined as the dissipation stage.

[0080] When the conditions of the outbreak stage or the dissipation stage are met, the traffic changes are converted to a fluctuation amplitude that remains within the preset tolerance band and maintains at least five consecutive monitoring periods, which is defined as the stable stage.

[0081] The first critical rate and the second critical rate are set based on the traffic changes of each node in the same period of history.

[0082] The core nodes and their associated impact nodes in the traffic network need to be continuously monitored, and the selection and layout of these nodes depend on dynamic modeling and data analysis of traffic flow. The monitoring period of the incremental rate is a pre-set time slice that is adapted to the update frequency of the traffic data source. When the traffic change amount of the core nodes and associated impact nodes within the spatial influence domain exceeds the pre-set first critical rate for three consecutive monitoring periods, this phenomenon is defined as the outbreak stage. The first critical rate is a parameter set based on historical data and usually depends on the historical average or 90% of similar events under similar conditions to ensure the accuracy of detection. In the outbreak stage, traffic flow suddenly and sharply increases, which may be triggered by traffic accidents or emergencies. The management system should promptly issue a warning and take intervention measures. When the traffic change amount decreases at a rate exceeding the second critical rate for three consecutive monitoring periods, and the boundary of the spatial influence domain shrinks to less than the set minimum boundary, the event is classified into the dissipation stage. The second critical rate is also determined based on historical data and is usually measured by analyzing the decreasing characteristics in similar scenarios. In this stage, efforts should be made to restore normal traffic flow, optimize routes, and improve vehicle traffic efficiency. The condition for entering the stable stage is that after meeting the conditions of the outbreak or dissipation stage, the fluctuation amplitude of the traffic change amount should be observed. If the fluctuation state remains within the pre-set tolerance band and remains for at least five consecutive monitoring periods, the current state can be defined as the stable stage. The tolerance band is set based on the variability index of historical data to ensure flexibility in responding to minor fluctuations and to avoid unnecessary responses.

[0083] In some embodiments of the present application, as shown in Figure 3 The real-time changes of the dynamic traffic network within the sharing range are matched and shared, including:

[0084] Obtain the demand information of the target sharing object, including the sharing range, the sharing content type, and the sharing frequency;

[0085] Based on the demand information, obtain the real-time change data corresponding to the sharing range in the dynamic traffic network, wherein the real-time change data includes real-time change traffic spatio-temporal data and event change characteristics;

[0086] According to the sharing content type, format process the real-time change data to generate standardized sharing data;

[0087] According to the sharing frequency, push the standardized sharing data to the target sharing object.

[0088] Collecting the demand information of the target sharing object, the information including sharing range, sharing content type and sharing frequency, the sharing range defining the geographical space limit, such as a certain city or traffic artery, the sharing content type indicating the data elements that the object is concerned about, such as traffic data, accident information, and the sharing frequency specifying the periodicity of information update, such as real-time, hourly or daily update, the process of collecting demand information should have a flexible interactive interface, allowing users to customize their needs without being bound to preset options; after obtaining the demand information, the system extracts the corresponding real-time change data in the dynamic traffic network based on the sharing range, these data not only contain the spatio-temporal variation of urban traffic, but also include the characteristics of event changes, such as newly occurring traffic accidents or public transportation shutdown, this process relies on an efficient data filtering and matching mechanism, which can quickly locate relevant data from large-scale data sets, enabling users to obtain accurate information; then, the obtained real-time change data is processed in format to generate standardized sharing data, format processing involves data cleaning, sorting and structure reorganization to ensure data readability and compatibility. The technical requirements of the target object should be considered during the data format processing process, such as the supported format of the terminal device or the expected access interface of the integrated system, the generated standardized data should be output in a universal data format, such as JSON or XML, to facilitate object parsing and use; finally, according to the sharing frequency, the standardized sharing data is pushed to the target sharing object. The pushing methods include but are not limited to API interface, real-time data pushing service or periodic report, the specific method should be selected according to user demand and system technical conditions, the sharing process needs to ensure data security and transmission stability, using encryption transmission technology and redundancy mechanism to prevent data leakage or transmission interruption.

[0089] In some embodiments of the present application, the real-time change data corresponding to the sharing range in the dynamic traffic network is obtained based on the demand information, including:

[0090] Based on the sharing range, filtering the target nodes and associated connection lines in the dynamic traffic network whose spatial positions are located within the sharing range;

[0091] According to the sharing frequency, obtaining the traffic spatio-temporal data of the target nodes within the corresponding time range, and synchronously obtaining the event change characteristics of the associated connection lines within the same time range;

[0092] Integrating the traffic spatio-temporal data and the event change characteristics into real-time change data.

[0093] Based on the demand information of the target shared object, the target nodes and associated connection lines in the dynamic traffic network whose spatial positions are located within the sharing range are screened, and this process relies on a module with geographic information system (GIS) function. By using the specific geographic boundaries of the sharing range, such as the boundary lines of urban areas or designated commuting corridors, the relevant nodes and connection lines are identified and extracted from the complex traffic network topology. These nodes are the basic components of the network, such as major traffic intersections, while the connection lines represent the actual road connections, ensuring the spatial matching and rationality of the data. Then, according to the setting of the sharing frequency, the traffic spatio-temporal data of the target nodes within the corresponding time range are obtained. These data include real-time traffic flow, vehicle speed and position changes at the nodes, ensuring sufficient capture of temporal dynamics. At the same time, the event change characteristics of the associated connection lines within the same time range are synchronously obtained. These characteristics may include traffic events such as accidents, temporary road closures, etc. The monitoring of the connection lines can reflect the broader traffic conditions. In order to effectively integrate the data, the collected traffic spatio-temporal data and event change characteristics are merged into a single real-time change data. The integration process involves data cleaning and standardization. Before merging the data, the format needs to be unified and redundant information needs to be eliminated. After data standardization, the data of each node and the event characteristics of the associated connection lines are interactively presented in a comprehensive data set. This combination of data sets not only improves the data usage efficiency and response speed, but also ensures the integrity and accuracy of the information, providing a solid foundation for subsequent sharing and analysis.

[0094] In some embodiments of the present application, the standardized shared data is pushed to the target shared object according to the sharing frequency, including:

[0095] determining whether the sharing frequency is higher than a preset threshold value, if it is higher than the preset threshold value, the standardized shared data is pushed to the target shared object in real time according to the shortest time interval corresponding to the sharing frequency;

[0096] if it is lower than or equal to the preset threshold value, the standardized shared data of the continuous multiple monitoring periods is aggregated to generate aggregated data, and then pushed according to the sharing frequency;

[0097] the current evolution stage label of the associated event change characteristics is pushed at the time of pushing;

[0098] when the event change characteristics enter the outbreak stage or the dissipation stage, the real-time change data incremental push is triggered immediately regardless of the sharing frequency.

[0099] determining whether the sharing frequency of the target shared object is higher than a preset threshold, the preset threshold being a standard set according to the processing capacity of the system and the real-time requirement of the data, if the sharing frequency is higher than the threshold, the data request is considered to be real-time pushing, at this time, the standardized shared data is pushed to the target object within the shortest time interval specified by the sharing frequency, ensuring the timeliness of the information, for example, for a real-time traffic monitoring system, data update within a few seconds can adapt to the requirement of rapid change of traffic flow; if the sharing frequency is lower than or equal to the preset threshold, considering the stability of the data and the amount of information, the system will perform data aggregation processing in this case, the aggregation processing involves merging the standardized data of continuous multiple monitoring periods to generate comprehensive aggregated data, this data processing method optimizes the information width and is suitable for non-real-time reporting such as daily or weekly traffic conditions, the aggregated data is pushed according to the sharing frequency to reduce the occupation of system resources by frequent transmission. In the pushing process, the evolution stage label of the associated current event change characteristics is associated, in this way, the event context is provided for the receiving object, for example, in the case of traffic management, the evolution stage label accompanying the data can quickly guide the manager to identify the congestion outbreak or dissipation trend, so as to optimize the scheduling scheme; when the event change characteristics enter the outbreak stage or the dissipation stage, the regular sharing frequency is ignored immediately, and the incremental pushing of real-time change data is triggered immediately, which is an emergency response mechanism, ensuring that all parties can obtain the latest dynamic information in the shortest time when a key traffic event occurs, so as to promote timely decision-making and response measures, the incremental pushing emphasizes the rapid transmission of data rich in change rate, impact range and other key details, so as to facilitate emergency management and timely adjustment.

[0100] In some embodiments of the present application, the basic traffic data includes:

[0101] Static attribute data, the static attribute data includes the geometric topology of the road network, the speed limit area setting, and the position of traffic signs and markings;

[0102] Dynamic behavior data, the dynamic behavior data represents the real-time motion state and interaction behavior of the road traffic participants;

[0103] Environmental perception data, the environmental perception data includes monitoring data of the external environment by sensors;

[0104] User interaction data, the user interaction data includes event information actively reported by road traffic participants, traffic condition feedback, navigation query records, and route preference settings.

[0105] The basic traffic data can be divided into four main categories, each of which provides support with its unique data characteristics and uses. Static attribute data is the foundation of the traffic network, including the geometric topology of the road network, speed limit areas, traffic sign and marking positions, etc. These static data are collected through geographic information systems (GIS), map services and field marking data collection, and are used to build a complete and detailed urban traffic network framework. These data are the basis of the traffic information system and remain unchanged in the update of real-time data, providing basic information for geographic positioning and path calculation. Dynamic behavior data relates to the real-time motion state and interactive behavior of traffic participants. These data are usually obtained from GPS, smart phone applications, vehicle-mounted devices, etc., and have real-time and dynamic characteristics, reflecting the speed, position and flow line of vehicles in real time. Such data are the basis for real-time analysis of traffic flow trends and identification of abnormal behavior, and are of key importance for the detection and response of traffic incidents. Environmental perception data include sensor monitoring data of the external environment, such as weather conditions, visibility, road surface slipperiness, etc. These data are obtained from fixed environmental monitoring stations or mobile sensing devices in the city, such as air quality sensors, temperature and humidity collectors on the road, etc. Environmental data are crucial in judging traffic safety conditions and analyzing incident causes, and can help develop effective early warning and safety prompts. User interaction data includes event information actively reported by traffic participants, traffic condition feedback, navigation query records and route preference settings. These data are usually collected through users' mobile devices. Users can report traffic conditions, request navigation or set their personal preferences through the application. User interaction data directly reflects the needs and experiences of participants, allowing the system to optimize navigation suggestions and path planning.

[0106] The application also provides a traffic information fusion and sharing system based on multi-source heterogeneous data, comprising:

[0107] A data acquisition module acquires basic traffic data of current road traffic participants based on multiple traffic data sources;

[0108] A road network topology module constructs a traffic network topology according to the target city road network, and maps the basic traffic data to the traffic network topology in a unified space-time coordinate system to obtain traffic space-time data;

[0109] A feature analysis module locks the traffic space-time data from the current time and analyzes the event change characteristics of the traffic incident when a traffic incident occurs;

[0110] A dynamic correlation module constructs a dynamic traffic network by taking the traffic space-time data as the nodes and node attributes of the traffic network topology, and taking the event change characteristics as the characteristics of the connecting lines between different nodes;

[0111] The matching sharing module matches real-time changes of the dynamic traffic network in the sharing range according to the demand of the target sharing object.

[0112] The above adjustment system in the application can effectively realize a traffic information fusion and sharing method based on multi-source heterogeneous data, and the technical effects are as described above.

[0113] In some embodiments of the application, the matching sharing module comprises:

[0114] The demand acquisition unit acquires demand information of the target sharing object, and the demand information comprises a sharing range, a sharing content type, and a sharing frequency;

[0115] The range corresponding unit acquires real-time change data corresponding to the sharing range in the dynamic traffic network based on the demand information, wherein the real-time change data comprises real-time change traffic space-time data and event change characteristics;

[0116] The standard processing unit performs format processing on the real-time change data according to the sharing content type, and generates standardized sharing data;

[0117] The data pushing unit pushes the standardized sharing data to the target sharing object according to the sharing frequency.

[0118] Similarly, the above optimization scheme of the system can also correspondingly realize the optimization effects of the method provided by the application, which will not be described here again.

[0119] Although the present application has been described in connection with specific features and embodiments thereof, it will be evident to those of ordinary skill in the art that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the description and drawings are to be regarded as illustrative in nature and are not to be regarded as limiting the scope of the application. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for fusion and sharing of traffic information based on multi-source heterogeneous data, characterized in that, include: Basic traffic data of current road traffic participants is obtained from multiple traffic data sources; Based on the road network of the target city, a traffic network topology is constructed, and the basic traffic data is mapped to the traffic network topology under a unified spatiotemporal coordinate system to obtain traffic spatiotemporal data. When a traffic incident occurs, the traffic spatiotemporal data from the current moment is locked and the event change characteristics of the traffic incident are analyzed; Using the traffic spatiotemporal data as nodes and node attributes of the traffic network topology, and the event change characteristics as the characteristics of the connecting lines between different nodes, a dynamic traffic network is constructed. Based on the needs of the target sharing object, the real-time changes of the dynamic transportation network within the sharing scope are matched and shared.

2. The method for fusion and sharing of traffic information based on multi-source heterogeneous data according to claim 1, characterized in that, Based on the road geometry distribution and intersection connection relationships of the urban road network, the traffic network topology is established. The nodes of the traffic network topology are represented as various intersections, and the connecting lines between the nodes represent the passable paths connecting these nodes based on the road geometry distribution. The start and end positions of the passable path are defined as the nodes that are closest to each other in the passable path, and the traffic data source is set at each node.

3. The traffic information fusion and sharing method based on multi-source heterogeneous data according to claim 2, characterized in that, The analysis of the event change characteristics of the traffic incident includes: Based on the spatiotemporal data of the traffic event, the core nodes in the traffic network topology are identified; Obtain the passage change of each passable path associated with the core node and expand the associated affected nodes layer by layer to generate a spatial influence domain; Real-time monitoring of traffic changes within the spatial influence domain to classify the evolution stages of traffic events; The event change characteristics of the spatial influence domain are obtained based on the evolution stage, wherein the event change characteristics are updated in real time with the traffic change.

4. The traffic information fusion and sharing method based on multi-source heterogeneous data according to claim 3, characterized in that, The evolutionary stages of the aforementioned traffic incident are divided as follows: When the passage change of the core node and the associated affected node within the spatial influence domain meets the condition that the incremental rate exceeds the first critical rate for three consecutive monitoring cycles, it is defined as the outbreak stage; When the change in traffic volume satisfies the condition that the reduction rate exceeds the second critical rate for three consecutive monitoring cycles, and the shrinkage of the spatial influence domain boundary reaches the preset minimum boundary, it is defined as the dissipation stage. When the conditions for the outbreak stage or the dissipation stage are met, the change in the passage volume is defined as a stable stage, in which the fluctuation amplitude is continuously within the preset tolerance zone and the fluctuation state is maintained for at least five consecutive monitoring cycles. The first critical rate and the second critical rate are set based on the traffic changes of each node during the same historical period.

5. The method for fusion and sharing of traffic information based on multi-source heterogeneous data according to claim 1, characterized in that, Matching and sharing real-time changes of the dynamic transportation network within the shared scope includes: Obtain the demand information of the target sharing object, including the sharing scope, the type of shared content, and the sharing frequency; Based on the demand information, real-time change data corresponding to the shared range in the dynamic traffic network is obtained, wherein the real-time change data includes the real-time change of the traffic spatiotemporal data and the event change characteristics; Based on the type of shared content, the real-time changing data is formatted to generate standardized shared data; The standardized shared data is pushed to the target sharing object according to the sharing frequency.

6. The traffic information fusion and sharing method based on multi-source heterogeneous data according to claim 5, characterized in that, Based on the demand information, real-time change data corresponding to the shared area in the dynamic transportation network is obtained, including: Based on the shared range, target nodes and associated connecting lines in the dynamic traffic network whose spatial locations are within the shared range are selected. Based on the shared frequency, the traffic spatiotemporal data of the target node within the corresponding time range is obtained, and the event change characteristics of the associated connecting line within the same time range are obtained synchronously. The traffic spatiotemporal data and the event change characteristics are integrated into the real-time change data.

7. The traffic information fusion and sharing method based on multi-source heterogeneous data according to claim 5, characterized in that, Pushing the standardized shared data to the target sharing object according to the sharing frequency includes: Determine whether the sharing frequency is higher than a preset threshold. If it is higher than the preset threshold, push the standardized sharing data to the target sharing object in real time according to the shortest time interval corresponding to the sharing frequency. If the data is lower than or equal to the preset threshold, the standardized shared data for multiple consecutive monitoring periods will be aggregated, and the aggregated data will be pushed according to the sharing frequency. When pushing a notification, associate the current evolution stage tag of the event change characteristics; When the event change characteristics enter the burst phase or dissipation phase, the incremental push of the real-time change data is immediately triggered regardless of the sharing frequency.

8. The method for fusion and sharing of traffic information based on multi-source heterogeneous data according to claim 1, characterized in that, The basic traffic data includes: Static attribute data, including the geometric topology of the road network, speed limit zone settings, and the location of traffic signs and markings; Dynamic behavior data, which characterizes the real-time movement state and interactive behavior of the road traffic participants; Environmental sensing data, which includes monitoring data of the external environment by sensors; User interaction data includes event information proactively reported by road traffic participants, traffic condition feedback, navigation query records, and route preference settings.

9. A traffic information fusion and sharing system based on multi-source heterogeneous data, characterized in that, The system includes: The data acquisition module obtains basic traffic data of current road traffic participants based on multiple traffic data sources; The road network topology module constructs a traffic network topology based on the target city's road network, and maps basic traffic data to the traffic network topology under a unified spatiotemporal coordinate system to obtain traffic spatiotemporal data. The feature analysis module, when a traffic incident occurs, locks down the traffic spatiotemporal data from the current moment and analyzes the event change characteristics of the traffic incident; The dynamic association module uses traffic spatiotemporal data as nodes and node attributes in the traffic network topology, and event change characteristics as the characteristics of the connecting lines between different nodes to construct a dynamic traffic network. The matching and sharing module matches and shares real-time changes in the dynamic traffic network within the sharing range according to the needs of the target sharing object.

10. The traffic information fusion and sharing system based on multi-source heterogeneous data according to claim 9, characterized in that, The matching and sharing module includes: The requirement acquisition unit acquires the requirement information of the target sharing object, including the sharing scope, the type of shared content, and the sharing frequency. The scope-corresponding unit acquires real-time change data corresponding to the shared scope in the dynamic transportation network based on demand information. The real-time change data includes real-time change traffic spatiotemporal data and event change characteristics. The standard processing unit processes the format of real-time changing data according to the type of shared content, generating standardized shared data. The data push unit pushes standardized shared data to the target sharing object according to the sharing frequency.

Images