CN114238542A

CN114238542A - Multi-level real-time fusion updating method for multi-source traffic GIS road network

Info

Publication number: CN114238542A
Application number: CN202111605329.7A
Authority: CN
Inventors: 魏存可
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-12-24
Filing date: 2021-12-24
Publication date: 2022-03-25

Abstract

The application aims at the application requirement of high-precision vector data fusion, provides a five-layer data fusion model based on the CityGML, and realizes a multi-level real-time fusion updating method covering a traffic GIS road network; firstly, a multi-level, object-oriented and lane-level fusion model based on the OGC standard is provided, the model is defined and described on the concept, logic and physical levels respectively, and a complete explanation is provided for the geometry, semantics and topology related to the model; secondly, designing an adaptive data fusion method to realize cluster analysis, linearization and topology reconstruction of the probe vehicle, vector road network matching based on cluster analysis of the vector road network and fusion based on position and semantic approximation information of POI data; thirdly, an LOD-s model management module is designed in an integrated mode; experimental results show that the tool meets the requirement of multivariate vector data fusion, and compared with manual self-adaptive fusion, the efficiency can be greatly improved on the premise of meeting multilevel and high precision.

Description

Multi-level real-time fusion updating method for multi-source traffic GIS road network

Technical Field

The application relates to a GIS road network real-time fusion updating method, in particular to a multi-source traffic GIS road network multi-level real-time fusion updating method, and belongs to the technical field of traffic road network multi-level fusion.

Background

With the rapid development of the urbanization process and the traffic industry, the road network is more and more complex, and various road traffic network information has the characteristics of bulkiness, diversity and complexity. Meanwhile, the requirements of various fields on the road network are increasingly refined, and industries at different levels show the requirements of different levels of detail on the road network. At present, more detailed expression and simulation of huge traffic vector data on a more microscopic level (sub-meter level) are already put into practice. The fine (sub-meter level) road network data has the characteristics of richer content, more diverse characteristics, higher updating speed, more microscopic expression level and more fine, and the lane level navigation application combining the finer lane level data is also developed rapidly. With the aim of solving the problems of traffic safety, efficiency, environment and the like in the field of transportation, the Internet of vehicles also comes, the important development branch is the fine position matching between vehicles and roads, the informatization of the roads related to the Internet of vehicles puts higher requirements on high-precision navigation and positioning, and the key basis of the application of technologies such as unmanned driving and unmanned vehicles lies in the high-precision guiding judgment of accurate lane-level (sub-meter-level) vector network data.

Meanwhile, with the explosive growth of basic geographic data, the original accumulation of the geographic data is gradually completed, the core of multi-source vector data is changed from data production to data updating, and the data sharing application updating becomes a concern hotspot. The situation increasingly becomes an important index of the data evaluation at present, and the acquisition based on large-scale and high-precision multi-source vector data is realized at present, so that an important method for ensuring the data situation is to fuse the multi-source data and complete the timely updating of the data by adopting the respective advantages of the multi-source data. However, as the data updating still lacks of an effective mechanism and technical method, the updating speed is slow, and the effect quality is poor, so that the problems of difficult guarantee of the data existential are urgently solved.

At the present stage, the data of the four major mainstream vector data interest points, DLG, navigation data and probe car data have different data characteristics. In the aspect of data content, the interest point data comprises all interest points of a ground object, including basic names, coordinates and associated attribute information of the ground object, the data digital line drawing comprises all vector data of the ground object, which can be abstracted into points, lines, surfaces and bodies, the data formats are consistent and easy to share, and the navigation data has various data formats at the present stage and is rich in content; the data of the detection vehicle comprises information such as data number, position, time, speed and the like returned at the moment. In the aspect of data updating, the POI data are updated by manual collection, updated by users and examined, the DLG adopts a method of linkage updating and dynamic updating, the accuracy is good, but the data is poor in the situation due to the inherent poor situation of the metadata, and the navigation data are updated in real time, so that the situation is good and the accuracy is high; in the aspect of a data updating method, DLG data is re-measured and warehoused by a professional, so that the data accuracy is high, the disadvantages of large consumption and long period are caused, navigation data can be updated in a centralized manner by means of the strength of volunteers, the updating speed is high, and meanwhile the accuracy needs to be further improved. The detection vehicle can accurately describe the position and time of the carrying platform every 1-10s through hardware measures such as a satellite navigation receiver carried on a taxi, and the like, but a sub-meter positioning precision error exists at present. To sum up: the multi-source traffic vector road network data has thousands of autumn and conflict points in respective advantages, so that the key for solving the problem is to abandon the defects of how to simultaneously have the advantages of the multi-source data.

Road network model present situation: the traditional road network model represented by the arc segment-node model is applied by a large amount of GIS software due to the simplicity and the easiness in application of the model, but along with the increase of data volume, the defects that the number of route segments is rapidly expanded, the attribute change of an arc segment is not considered, and the one-to-many relation cannot be processed are highlighted day by day, and aiming at the problems, the comprehensive development of the multidimensional LRS data model is difficult to implement due to the fact that the maintenance of a reference layer is needed, the flexibility is poor, and the later design is more and more complex and refined; the UNETRANS model lacks three-dimensional temporal object management, and does not consider actual lane topology; concepts such as 'virtual lanes', 'lane zones' and the like provided by a lane-oriented multi-level road network model do not accord with normal cognition of people, and a clear modeling standard for complex ground objects such as intersections and viaducts is lacked. In summary, the following steps: the existing road network models are numerous and have different advantages and disadvantages, but the fusion requirement of multilevel refinement (lane level) cannot be met in the aspect of meeting the multi-source data fusion.

The advantages of multi-source vector data are different, and on the basis of completing data integration, how to fuse the multi-source vector data is a key problem for solving multi-level fusion of the multi-source vector; the objects related to the road network are independent from each other, the objects are communicated with each other, and the integrated objects lack a uniform model standard; due to the fact that data audiences are various, the data precision requirements are different, and a proper fusion grade is established according to various requirements; in combination with the development trend of the vector network model and the user requirements, an object-oriented, multi-level and sub-meter-oriented vector network fusion model is urgently needed.

In summary, the multi-source road network fusion update in the prior art has obvious defects, and the main defects and design difficulties thereof include:

firstly, in the prior art, vector data represented by basic geographic data, navigation data such as gold, Baidu and the like does not really realize real-time fusion application and updating of data from a coarse level (point and line objects) to a fine level (actual lane), and the problems of untimely data mining, lack of data model level corresponding relation, incapability of meeting the requirement of accessing different data service providers on the basis of an OGC standard and the like exist; the method comprises the following steps that firstly, the existing data models are numerous and incompatible with each other, a data model for perfecting classification definition of the existing data models on a detail level is lacked, and a multi-level real-time fusion framework is lacked; secondly, the problems of inconsistency of data coordinate systems of different data sources with map projection, inconsistency of attribute classification grading and attribute coding, deviation of data visualization caused by different data formats and the like are solved, a road network multi-level real-time fusion and updating unified geographic framework is lacked, cluster analysis aiming at POI data, probe vehicle data, road networks and digital line maps cannot be realized, and static and dynamic updating fusion of vector data cannot be met; thirdly, a multi-source vector road network multi-level fusion updating tool is lacked;

secondly, obtaining based on large-scale and high-precision multi-source vector data is realized, but effective mechanisms and technical methods are still lacked in updating fused multi-source data, updating speed is low, effect quality is poor, data situation is difficult to guarantee, at the present stage, point-of-interest data, DLG (digital living group), navigation data and probe car data of four major main stream vector data have different data characteristics, POI (point of interest) data are manually acquired and updated, updated and examined by users and the like, DLG (digital living group) data are updated in a linkage manner and dynamically, accuracy is good, but inherent situation of metadata is poor; in the aspect of a data updating method, DLG data is re-measured and warehoused by a professional, so that the data accuracy is high, the disadvantages of high consumption and long period are caused, navigation data can be updated in a centralized manner by means of the force of volunteers, the updating speed is high, the accuracy needs to be further improved, a detection vehicle still has a sub-meter-level positioning accuracy error at present, multi-source traffic vector network data has thousands of autumn and conflict points in respective advantages, the prior art cannot have the advantages of the multi-source data at the same time, the defects of the multi-source data are abandoned, multiple sets of data cannot be fused and updated, the manual consumption is high, the advantages of different data sources cannot be fully utilized, and the data reuse rate is low;

thirdly, the road network model in the prior art does not consider the attribute change of the arc segment and has the defect that the one-to-many relationship cannot be processed, and the comprehensive development of the multidimensional LRS data model is difficult to implement due to the fact that the reference layer needs to be maintained and the flexibility is poor, and later-stage design tends to be more complex and more refined; the UNETRANS model lacks three-dimensional temporal object management, and does not consider actual lane topology; the lane-oriented multi-level road network model lacks a clear modeling standard for complex ground objects such as intersections and viaducts, and the existing road network model can not realize the fusion of multi-source traffic vector road network data in the aspect of meeting the multi-source data fusion because a data producer generates data structure barriers for data monopoly, the definition of road vector levels is lacked, the expression of complex ground objects is lacked, and the fusion requirement of multi-level refinement (lane level) can not be met;

fourthly, the prior art faces a plurality of difficulties of multi-source geographic data fusion, firstly, data integration is realized by data produced by different production departments according to needs, secondly, advantages and disadvantages exist among DLG, probe car data, road network and POI data, so that lane-level road network data fusion is difficult to realize, the fused data attribute is rich, the updating speed is high, the real-time performance is accurate, and a unified geographic framework is lacked for multi-source traffic vector road network data; the method can not be used for expressing the road network data fusion requirements in a computer, and a third road network fusion model for connecting various heterogeneous data is lacked; fusion algorithms such as matching of various accurate data road sections, construction of association relations between a road network and POI and the like are lacked, a unified model standard is lacked for a comprehensive object, and an object-oriented, multi-level and sub-meter-oriented vector network fusion model is lacked.

Disclosure of Invention

Aiming at the problems that the vector data does not really realize the real-time fusion application and update of data from a coarse level (point and line object) to a fine level (actual lane), the data is not mined in time, the corresponding relation of data model levels is lack, different data service providers can not be accessed on the basis of an OGC standard, and the like, the multi-source road network vector multi-level real-time fusion update method is provided; aiming at the problems of numerous data models and incompatibility of data, the multi-source vector road network multi-level real-time fusion model based on the CityGML standard is provided, the data model defined by classification is perfected on a detail level, and a multi-level real-time fusion framework is designed; secondly, aiming at the problems that the data coordinate systems of different data sources are inconsistent with the projection of a map, the attribute classification grading and the attribute coding are inconsistent, and the data visualization is deviated due to different data formats, a road network multi-hierarchy real-time fusion updating unified geographical frame is provided, the clustering analysis of POI data, probe vehicle data, a road network and a digital line map is realized, the high-precision matching and fusion inside and among multi-source data types are realized, and the static and dynamic updating fusion of vector data is met; and thirdly, a multi-source vector road network multi-level fusion updating tool is realized, a better multi-level road network fusion effect is achieved, and a new data fusion thought and method are provided for multi-level vector data application.

In order to achieve the technical effects, the technical scheme adopted by the application is as follows:

a multi-level real-time fusion updating method for a multi-source traffic GIS road network comprises the steps that firstly, a multi-level, object-oriented and lane-level road model is set based on an OGC standard; secondly, a five-layer data fusion model based on the CityGML is provided by adopting high-precision fusion of a vector network and POI data, and a multi-layer real-time fusion updating method covering the traffic GIS road network is realized;

firstly, a multi-level real-time fusion model of a multi-source vector road network: the method comprises the following steps of firstly, a geographic space cognition conceptual model which comprises a fusion model five-level hierarchical division and fusion model design framework, secondly, a geographic space data logic model and thirdly, a geographic space structure physical model;

providing a multi-source vector road network multi-level real-time fusion model based on a CityGML standard, perfecting classification definition of the multi-source vector road network multi-level real-time fusion model on a detail level, designing a multi-level real-time fusion framework, and realizing three-layer architecture design of a data model based on an abstract expression method of different elements covered by vector data and geometric, semantic and topological relations among the elements and among different levels of the elements;

secondly, a multi-source road network multi-level real-time fusion updating model algorithm: the method comprises the steps of firstly, consistency coordination fusion, including consistency processing of multi-source space vector data, unified combination of a space coordinate system and map projection, unified combination of classification grading and attribute coding, and unified conversion of multi-source road network data formats, secondly, real-time fusion and unification of multi-source road network multi-level models, including multi-level real-time unified model design, unified combination of a real-time fusion model and an algorithm, thirdly, vector road network data self-adaption matching fusion, including POI matching fusion based on combination of position and semantics, road network matching fusion based on a road network change mode, fourthly, probe vehicle matching and aggregation, including probe vehicle feature element calculation updated by multi-level fusion of a road network, probe vehicle and road network information matching, and probe vehicle hot spot path calculation based on cluster analysis;

the method provides a multi-level real-time fusion and update unified geographic framework of the road network, performs early-stage data integration related to multi-source data fusion and matching and fusion analysis of later-stage multi-source traffic vector road network data, achieves cluster analysis of POI data, probe car data, the road network and a digital line map, achieves high-precision matching and fusion inside and among multi-source data types, and meets the requirements of static and dynamic update and fusion of vector data.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized in that a fusion model is divided into five levels: the vector geographic data mainly relates to various data on a traffic network, is generally summarized into six basic elements of a traffic lane, an intersection, a tunnel, a viaduct, a rotary island and a square, and the following hierarchical division rules are formulated:

LOD-0: adopting points or lines to carry out basic description on positions of various characteristics;

LOD-1: describing the shape of various characteristics by adopting a surface;

LOD-2: adopting the surface with interface to describe the connectivity of each characteristic;

LOD-3: describing connectivity and lane shapes of various characteristics by using a lane level surface;

LOD-4: describing connectivity of various characteristics and shapes of lane levels by using a lane level surface;

under the definitions of the model module and the hierarchical division, a multi-level concept hierarchical division diagram is obtained through synthesis.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized by fusing a model design framework: the multi-level road network model performs grouping and sub-packaging according to logical properties, and is characterized in that:

(1) the method is characterized in that: objects in the network feature pack comprise abundant multi-scale geometric features, and are related and constructed through internal coding representation to form multi-scale geometric expressions;

(2) the second characteristic: events include static facilities and random dynamic time, and appear as multi-scale aspects of points;

(3) the characteristics are three: the mobile object comprises a probe car object related to traffic behaviors in the vector road network;

(4) the model design is based on query, path planning, position reference, event, network characteristics, mobile object, domain knowledge and relation module;

(5) the domain knowledge comprises the steps of carrying out the regulated classification on the vehicle type and the road function of the probe vehicle data and giving a specific mark;

(6) the query includes the function of mutual query between elements and inside elements.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized by comprising the following steps of (1) designing a geospatial data logic model: describing each level of the multi-level logic model in geometry, semantics and topology:

(1) LOD-0 abstracts the vector network at the macroscopic level, describes the simplest geometric form of the road by points or single lines, is positioned at the center of an object, and describes the road network layout from the macroscopic level by adopting an LOD-O geometric object;

(2) LOD-1 does not have any semantic meaning to describe the shape of the divided objects in the vector network;

(3) LOD-2 divides the six kinds of objects divided in the vector network into object outlets according to the connection nodes among the objects, and gives a semantic relation by increasing the nodes among the objects;

(4) LOD-3 abstracts the lane level of a vector network system, describes the geometric form of vector network data by double lines, the double line position of the geometric form is positioned at the center position of the lane, and the geometric form is still expressed by a single line for roads without a special central isolation zone;

(5) LOD-4 abstracts a vector network system in the most detail, describes lane-level abstract ground objects, describes the geometric form of a road in a multi-line mode, and each line segment has directionality and has different traffic semantic features;

designing a physical model of a geographic space structure: dividing objects related to a vector road network into six types of road network elements and one type of POI elements according to main use frequency, segmenting the road network and describing a topology construction method of each hierarchical network, and setting the following data expression rules:

(1) rule one is as follows: for the LOD0 network, the positioning selection among the objects is at the object intersection, the road section positioning selection is at the street logic intersection, and the objects are communicated in two directions without semantic attribute constraint;

(2) rule two: the viaduct and roundabout buildings are expressed by a point element on the level of LOD 0;

(3) rule three: in order to ensure semantic association between multiple layers in the multi-layer road network model, road section nodes are additionally arranged.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized by comprising the following steps of: the method comprises the following steps of unifying space coordinates and map projection, unifying classification grading and attribute coding and unifying data format conversion;

unified union of spatial coordinate system and map projection: a WGS84 space coordinate system and a Gaussian Krigger projection coordinate system are adopted in a unified mode, a 1985 national high-level standard is adopted in an elevation coordinate system, a code conversion mode is combined with ArgGIS and ENVI image processing software, mass space coordinate system conversion and projection coordinate conversion are unified through codes, and a single image is converted through the image processing software.

A multi-level real-time fusion updating method of a multi-source traffic GIS road network further comprises the following steps of unified combination of classification and attribute coding: adopting different classification grading and attribute coding modes for the vector road network and POI data, wherein the classification grading of the vector road network is according to a six-class five-layer classification system in a road network model;

the method comprises the following steps of combining and unifying classification and classification of the multi-source road network and attribute coding: data sources of classification and attribute coding are classified according to needs and are uniformly divided into two categories: the road network data and POI data are coded according to the following rules:

1) classifying and grading road networks and encoding attributes: based on the design of a multilevel road vector model, classifying and grading a road network according to six elements, grading according to a five-level model, and coding the attribute according to a physical model coding method of a geographic space structure;

2) POI data classification and attribute coding: and classifying according to a four-level classification system.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized in that a multi-level real-time unified model is designed, wherein real-world geographic entities and mutual relations are abstracted, a plurality of geographic space recognizing windows with geographic areas as boundaries are constructed, each data area comprises a plurality of data blocks, each data block comprises a plurality of geographic element layers, each element layer is relatively independent in data structure and organization, and the operation of data updating, inquiring, analyzing and displaying takes the element layers as basic units;

the multi-level unified model of the multi-source road network organizes data by adopting a hierarchical structure, simultaneously adopts a topological structure form, and is operated based on the whole target of a surface object, so that the integrated management of attribute and geometric data is realized, and the integration requirement of multi-source data is met;

the multisource road network multi-level unified model takes geographic element objects as the most basic element units, each geographic element object is taken as the most basic element unit, attribute data and spatial data of the geographic element objects are integrally stored, and the whole process of object-oriented data storage and GIS data operation is adopted;

the real-time fusion model and the algorithm are united and unified: fusion of different algorithms is realized at different levels:

extraction of LOD-O level: POI data are fused at an LOD-O level by adopting the relation between position and semantic approximation degree in model design, fusion vector network data of the POI data are realized at an LOD-0 level, and the node and the geometric center position of the POI data are obtained according to DLG data with the highest precision level, so that the fusion of the 0 level is realized;

the LOD-1 level acquires the shape characteristics of the LOD-1 level according to the attribute table to realize information extraction on the 1 level;

the LOD-2 level obtains the connectivity of the surface domain through the intersection of objectification elements;

according to the LOD-3 level, directional aggregation and topological reconstruction are carried out on the probe vehicle data, so that linearization of the probe vehicle data is realized, and probability matching is carried out on the probe vehicle data and the navigation data, so that LOD-3 level fusion is realized;

the LOD-4 level realizes the linearization of the probe vehicle data and the probability matching with the navigation data to realize the LOD-4 level fusion through the directional road network aggregation and the topological reconstruction.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized in that POI matching fusion based on combination of position and semantics: the accuracy of the POI fusion set is improved by combining the information based on the spatial position and the attribute information;

road network matching fusion based on road network change patterns: the method comprises three types:

the first type, in the same area, data with better occurrence contains more spatial elements than data with lower occurrence, and belongs to 0: 1, matching;

the second category, the same area, the data with better occurrence includes less spatial elements than the data with lower occurrence because of spatial variation, and belongs to 1: 0 matching;

in the third category: in the same area, the data with better occurrence replaces the space elements with poorer occurrence, and the conditions are complex and various;

aiming at a vector data change mode, the method meets the requirements of real-time and dynamic vector data fusion, and map change of a time point t when the time point t of a road network is matched at a certain time point t comprises two modes, wherein one mode is that road network data A at the time t is matched with t_iThe second is to update map matching at a certain time by using POl data transmitted in real time.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized by comprising the following steps of: the method comprises the steps that the data of a probe car are subjected to satellite navigation positioning technology, driving longitude and latitude and time information of the probe car are recorded regularly at intervals according to the sampling rate of the probe car, meanwhile, potential instantaneous speed longitude and latitude information is processed through a probe car clustering analysis mode and a model algorithm, text information of the probe car is converted into space data format information, the potential instantaneous speed longitude and latitude information is associated with road network data in time and space, road network information change is dynamically detected in real time, the communication relation between lanes is excavated, and geometric, semantic and topological information of the urban complex road network is extracted;

1. calculating the characteristic elements of the probe vehicle updated by multi-level fusion of the road network: the probe vehicle data definition and calculation related to road network multi-level fusion updating comprises the following steps:

1) traffic volume ps in the measurement direction:

p_s＝(X_d+Y_e)/(r_d+r_e) Formula 1

Wherein:

p_s: traffic volume in the direction to be measured at LOD-3 level, unit: vehicle/min;

X_d: on the LOD-3 level, the number of incoming vehicles traveling toward the probe vehicle (in the test direction), unit: vehicle/min;

Y_e: on the LOD-3 level, when a certain vehicle to be detected runs in an undetermined direction, subtracting the number of the vehicles overtaking the detection vehicle from the number of the vehicles overtaking the detection vehicle;

r_d: the unit of the running time of a certain vehicle to be tested on the LOD-3 level and the running time of the vehicle in the traffic flow direction and the reverse running is as follows: vehicle/min;

r_s: on the LOD-3 level, the running time, unit, when a certain vehicle to be measured runs along the direction of the flow to be measured: min;

2) mean time of flight T_s：

R_s＝r_s-Y_s/p_sFormula 2

Wherein: r_sThe average travel time of the road section is measured;

3) average vehicle speed U_s：

U_s＝(J/R_s) X 60 formula 3

Wherein: u shape_sThe unit Km/h is the average speed of the observed road section, and J is the length of the observed road section and the unit Km;

when the formula 1 to the formula 3 are used for calculation, X_d，Y_e，r_d，R_sThe arithmetic mean value is taken to calculate, the cleaning processing is carried out on the data set, the abnormality of the vehicle positioning information is judged by solving the standard deviation of the first-order difference of the positioning information, the direction abnormal value is cleaned by judging the moving range of the vehicle positioning information through the variance of the longitude and latitude, and the error statistics of the data process caused by error data is avoided by eliminating gross error data.

A multi-level real-time fusion updating method for a multi-source traffic GIS road network is further characterized in that a detection vehicle is matched with road network information: correcting the positioning error of the probe vehicle data according to the priori knowledge of the high-precision lane-level road network data, correcting the position information of the probe vehicle data to the correct road network, and establishing association between the probe vehicle text data information and the road network data information through a map matching algorithm;

and (3) calculating a hot spot path of the probe vehicle based on cluster analysis: on the undirected basis, by increasing the equidirectional condition basis, finding track data of the detection vehicles connected with equidirectional density, and by mining the distribution rule of the data of the detection vehicles, generating directed edges according to aggregation classes, completing LOD-4 level data extraction, and obtaining a topological structure diagram of a complex road network;

the method comprises the steps of further analyzing the relevance between a spatial distribution pattern of track data of the probe car and traffic geographic characteristics by analyzing the similarity and aggregation characteristics of the spatial data, finally mining the spatial distribution pattern and the traffic geographic relevance of the data of the probe car by cluster analysis, and achieving the road network cluster analysis effect of the data of the probe car by adopting the approximate cluster analysis of directed data, thereby realizing the dynamic and timely fusion of the data of the road network and the probe car.

Compared with the prior art, the innovation points and advantages of the application are as follows:

first, the application needs for high-precision vector data fusion, and aims at the problems existing in the existing methods: there is no multi-level, object-oriented, lane-level road model that can be offered to different map providers based on the OGC standard; the high-precision fusion of vector network and POI data can not be realized, a five-layer data fusion model based on the CityGML is provided, and a multi-level real-time fusion updating method covering a traffic GIS road network is realized; firstly, a multi-level, object-oriented and lane-level fusion model based on the OGC standard is provided, the model is defined and described on the concept, logic and physical levels respectively, and a complete explanation is provided for the geometry, semantics and topology related to the model; secondly, designing a data fusion method suitable for the system to realize cluster analysis, linearization and topology reconstruction of the probe vehicle, vector road network matching based on the cluster analysis of the vector road network and fusion based on position and semantic approximation information of POI data; thirdly, an LOD-s model management module is designed in an integrated mode, and six modules including coordinate system conversion, classification and grading, format consistency processing, probe vehicle data cluster analysis and topology reconstruction, probability registration of road network data and LODs model generation management are designed according to functions of the LOD-s model management module; finally, the data of the Gade and heaven and earth maps are taken as experimental data, and the experimental result is combined with the data analysis of the probe vehicle, so that the tool meets the requirement of multivariate vector data fusion, and compared with manual self-adaptive fusion, the efficiency can be greatly improved on the premise of meeting multilevel and high precision;

secondly, the application and the updating of the real-time fusion of data from a coarse level (point and line objects) to a fine level (actual lane) are not really realized in the current vector data, and the multi-source road network vector multi-level real-time fusion updating method is provided by the application aiming at the problems that the data is not mined in time, the corresponding relation of data model levels is lack, different data service providers cannot be accessed on the basis of the OGC standard, and the like; aiming at the problems of numerous data models and incompatibility of data, the multi-source vector road network multi-level real-time fusion model based on the CityGML standard is provided, the data model defined by classification is perfected on a detail level, and a multi-level real-time fusion framework is designed; secondly, aiming at the problems that the data coordinate systems of different data sources are inconsistent with the projection of a map, the attribute classification grading and the attribute coding are inconsistent, and the data visualization is deviated due to different data formats, a road network multi-hierarchy real-time fusion updating unified geographical frame is provided, the clustering analysis of POI data, probe vehicle data, a road network and a digital line map is realized, the high-precision matching and fusion inside and among multi-source data types are realized, and the static and dynamic updating fusion of vector data is met; thirdly, a multi-level fusion updating tool of the multi-source vector road network is realized, the multi-level fusion updating tool is suitable for fusion processing of multi-source vector data, a good multi-level road network fusion effect is achieved, and a new data fusion thought and method are provided for multi-level vector data application;

thirdly, the method is oriented to the data fusion and application requirements of the multi-source traffic vector road network, is based on the current situations of diversification of the existing vector data, data structure barriers generated by data manufacturers for data monopoly and the like, and aims at the problems of different advantages of the existing vector geographic data, lack of road vector level definition, complex surface feature expression loss and the like, comprehensively considers the development current situation of multi-source vector multi-level fusion, a multi-source vector data model and an algorithm, researches the design and implementation of a multi-source vector multi-level fusion tool, realizes the fusion of the multi-source traffic vector road network data, and achieves the purpose of enhancing the situation and the precision of the multi-source fusion vector geographic data; on one hand, the integration of data is realized aiming at data produced by different production departments according to needs, and on the second hand, the lane-level road network data fusion is realized aiming at the advantages and disadvantages of DLG, probe vehicle data, road network and POI data, so that the fused data has the characteristics of rich attributes, high updating speed, real-time accuracy and the like. The built fusion system can fuse and update a plurality of sets of data, has little labor consumption, can fully adopt the advantages of different data sources, and improves the reuse rate of the data.

Fourthly, the innovation points of the application are also embodied on three levels: one is aiming at the multi-source traffic vector road network data, unifying the geographic framework; solving the expression of the road network data fusion oriented requirements in a computer, providing a road network fusion model, and connecting a third road network fusion model of various heterogeneous data; thirdly, fusion algorithms such as accurate road section matching of various data, construction of association relations between road networks and POIs and the like are adopted; the situation is improved, and the road network data is updated through the real-time updated probe vehicle data; the precision is improved, and the precision of the data of the probe car is improved in real time through high-precision road network and POI data; the fusion purpose of dynamically improving the situation and the space precision is achieved by superposing the data of the multi-source road network data on the probe vehicle, a multi-level road network model is designed, multi-source traffic vector road network data with multiple levels, high precision and good situation are obtained, and the method has good practical value;

drawings

FIG. 1 is a schematic diagram of three expression levels of a multi-source vector road network multi-level real-time fusion model.

FIG. 2 is a classification diagram of six basic elements of a multi-source vector road network multi-level real-time fusion model.

FIG. 3 is a multi-level conceptual division diagram of a multi-source vector road network real-time fusion model.

FIG. 4 is a flow diagram of a multi-source spatial vector data consistency process.

FIG. 5 is a flow chart of code design of classification rules of multi-source traffic GIS data.

Fig. 6 is a schematic diagram of a multi-source POI data classification hierarchy and attribute coding scheme.

FIG. 7 is a schematic diagram of integration and fusion of multi-source road network data formats.

Fig. 8 is a flow chart of a POI matching fusion scheme based on location and semantic integration.

FIG. 9 is a diagram of a probe vehicle data directional clustering effect and topology.

FIG. 10 is a DEM and DOM data graph before and after the application clips the splice.

Fig. 11 is a schematic diagram of the superposition effect of the probe vehicle and the road network data.

FIG. 12 is a diagram of the effect of multi-source data on lane level data fusion update.

Detailed description of the invention

The technical scheme of the multi-source traffic GIS road network multi-level real-time fusion updating method provided by the present application is further described below with reference to the accompanying drawings, so that those skilled in the art can better understand the present application and can implement the present application.

At present, vector data represented by basic geographic data, navigation data such as high-grade navigation data and Baidu navigation data do not really realize real-time fusion application and updating of data from a coarse level (point and line objects) to a fine level (actual lane). Aiming at the problems that data are not mined in time, the corresponding relation of data model levels is lack, different data service providers cannot be accessed on the basis of the OGC standard, and the like, the multi-source road network vector multi-level real-time fusion updating method is provided. The method mainly comprises the following steps:

(1) aiming at the problems of numerous data models and incompatibility of data, a multi-source vector road network multi-level real-time fusion model based on the CityGML standard is provided, the data model defined by classification is perfected on a detail level, a multi-level real-time fusion framework is designed, and three-level architecture design of the data model is realized based on an abstract expression method of different elements covered by vector data and geometric, semantic and topological relations between the elements and between different levels of the elements.

(2) Aiming at the problems of inconsistency of data coordinate systems of different data sources with map projection, inconsistency of attribute classification grading and attribute coding, deviation of data visualization caused by different data formats and the like, a road network multi-hierarchy real-time fusion and updating unified geographic framework is provided, the difference of multi-source geospatial road network data fusion in the aspects of geometry, semantics and topology is analyzed, early-stage data integration related to multi-source data fusion matching and fusion analysis of later-stage multi-source traffic vector road network data are carried out, clustering analysis aiming at POI data, probe vehicle data, road networks and digital line maps is realized, high-precision matching and fusion inside and among multi-source data types are achieved, and static and dynamic updating fusion of vector data is met.

(3) The multi-level fusion updating tool for the multi-source vector road network is designed, the effectiveness, the fusion precision and the algorithm efficiency of the model are verified, and the experimental result shows that the fusion model is suitable for the fusion processing of multi-source vector data, achieves a good multi-level road network fusion effect, and provides a new data fusion thought and method for multi-level vector data application.

The multi-level real-time fusion updating method of the multi-source traffic GIS road network comprises the following specific steps:

step one, multi-source data preprocessing: preprocessing the consistency of the geometry, the attribute and the semantics of the multi-source traffic vector road network data;

step two, model loading: and loading and warehousing the multi-source vector data according to a multi-level concept according to the concept of a multi-level real-time fusion model of the multi-source vector road network.

Step three, matching the multi-source spatial data according to a road network and POI points respectively according to a classical algorithm probability relaxation method, and performing static road network updating and fusion on the multi-source POI data by adopting a road network data cleaning and fusion detection vehicle data and a matching fusion method based on space and attribute combination by adopting spatial clustering analysis;

and step four, spatializing the data of the probe car. The vector data is unified in format, vector consistency processing is carried out, and loading and warehousing are carried out;

and fifthly, based on correction and fusion of the high-precision vector data lane-level probe vehicle, superimposing the probe vehicle data to the road network data, building a probe vehicle road network through real-time superimposition and cluster analysis of the probe vehicle and the static road network, analyzing the topological relation of the probe vehicle road network, and finally realizing dynamic real-time fusion of the probe vehicle data and the road network information.

Multi-level real-time fusion model of multi-source vector road network

The vector fusion oriented multi-source vector road network multi-level real-time fusion model is provided with a five-level fusion level model, and multi-level association of the fusion model on module division, geometry, semantics and topology is designed in detail from a geographic space cognitive concept model, a geographic space data logic model and a geographic space structure physical model respectively.

The method comprises the steps of abstracting and describing geographic entities, representations and relations of the geographic entities based on a geographic space cognitive concept model, a geographic space data logic model and a geographic space structure physical model, and realizing three expression levels of the road network from geographic implementation to a computer, wherein the three expression levels are abstracted from top to bottom to concrete, and are shown in figure 1.

The method adopts UML language description to establish a multi-source vector multi-level fusion model as a traffic GIS vector fusion logic model, and adopts Oracle design language PLSql to explain a data structure, wherein the multi-source traffic vector road network data modeling is divided into five steps: requirement modeling, entity relation definition, correct expression of geographic elements, matching of database elements, and physical design.

Design of geographic space cognitive concept model

1. Five-level hierarchical division of fusion model

The vector geographic data mainly relates to various data on a traffic network, and is generally summarized into six basic elements of a traffic lane, an intersection, a tunnel, a viaduct, a rotary island and a square, as shown in fig. 2.

The hierarchy of the fusion model is based on a CityGML hierarchical division rule, the requirement of multi-hierarchy application is met, different hierarchies are defined in a supplementary mode, and the following hierarchical division rule is formulated:

LOD-1: describing the shape of various characteristics by adopting a surface;

under the definition of the model module and the hierarchical division, a multi-level concept hierarchical division diagram is obtained by integration, as shown in fig. 3.

2. Fusion model design framework

The multi-level road network model performs grouping and sub-packaging according to logical properties, and is characterized in that:

(II) design of geospatial data logic model

Describing each level of the multi-level logic model in geometry, semantics and topology:

and expressing the corresponding relation between the element entities in the data model in the road network multi-level real-time fusion model and the elements in Oracle.

(III) design of physical model of geospatial structure

Dividing objects related to a vector road network into six types of road network elements and one type of POI elements according to main use frequency, segmenting the road network and describing a topology construction method of each hierarchical network, and setting the following data expression rules:

Two-source and multi-source road network multi-level real-time fusion updating model algorithm

The multi-source vector multi-level fusion comprises three aspects of fusion of space entity geometric features, fusion of attribute information and topological relation reconstruction, and mainly relates to the following three aspects:

(1) consistency coordination and fusion: the method comprises the unification of space small system and map projection, the unification of classification grading and attribute coding, the unification of data format conversion and the unification of data model;

(2) matching and fusing POI data and vector linear data: matching is realized on the geometric, semantic and topological relations, and spatial matching of data is realized by taking certain source data as a reference;

(3) the probe vehicle data clustering analysis algorithm comprises the following steps: and the cluster analysis process of probe vehicle data from scattered points to the road network with the constraint points is realized.

Consistency coordination fusion

1. Multi-source spatial vector data consistency handling

The multi-source spatial vector data consistency processing flow is shown in fig. 4. The consistency processing is divided into unification of space coordinates and map projection, unification of classification and attribute coding, and unification of conversion of data formats.

2. Unified union of spatial coordinate system and map projection

The multi-source data is easy to generate a shift problem during superposition due to the fact that adopted coordinate systems are inconsistent. The offsets produced by the coordinate system are divided into three categories: one is a deviation due to inconsistency of the spatial coordinate system, one is a deviation due to the projection coordinate system, and the other is a deviation due to inconsistency of the spatial coordinate and the projection coordinate.

(1) The geospatial coordinate system is a coordinate system established by the central point (the center of gravity/the geocentric) of the ellipsoid of the earth, and comprises a WGS84 coordinate system, a Xian 80 coordinate system and a national 2000 coordinate system, and the geospatial coordinate system is unified for ensuring consistency.

(2) The projection coordinate system is a coordinate system which converts space coordinates into plane coordinates according to a certain mathematical projection method, and comprises mercator projection, Gauss-Kruger projection and UTM projection, wherein the latter two are generally variants of the former, a map is displayed on the plane coordinate system to ensure the map correctness, and the projection coordinate system is unified on the premise of the unified space coordinate system.

The method adopts a WGS84 space coordinate system and a Gaussian Kruger projection coordinate system, a 1985 national high-level standard is adopted in an elevation coordinate system, a code conversion mode is combined with ArgGIS and ENVI image processing software, large-batch space coordinate system conversion and projection coordinate conversion are unified by codes, and a single image is converted by the image processing software.

3. Unified association of classification hierarchy and attribute coding

On the basis of meeting the OGC standard, analyzing the existing classification and classification system, comparing the difference of the classification and classification system, and constructing a vector data fusion classification and classification system and an attribute code. Data classification grading is an important way and content of spatial data organization in GIS. The classification and classification of the multi-source traffic vector road network data is the basis of the multi-source traffic vector road network data coding, the coding is the final embodiment of classification and classification, and the specific real world entity is uniquely stored in a computer through classification and attribute coding, so that the organization and management of a database are facilitated.

The specific flow is shown in fig. 5 by combining the above data classification and classification rules and criteria. Aiming at multi-source vector multi-level fusion, different classification grading and attribute coding modes are adopted for vector road networks and POI data, wherein the classification grading of the vector road networks is according to six classes of five-layer classification systems in a road network model.

(1) Joint unification of classification and attribute coding of multi-source road network

The data sources of classification and attribute coding are uniformly divided into two categories according to needs: the road network data and POI data are coded according to the following rules:

1. classifying and grading road networks and encoding attributes: based on the design of a multilevel road vector model, the road network is classified according to six elements in classification and classification is carried out according to a five-level model, and the attribute coding of the road network is carried out according to a physical model coding method of a geographic space structure.

POI data classification hierarchy and attribute coding: the classification was performed according to a four-level classification system, and the number of each class is shown in FIG. 6 below.

4. Conversion unification of multi-source road network data formats

Based on the requirement of existing data fusion, for the probe vehicle data with large data volume, AE secondary development is adopted, a development language is c #, data formats are uniformly converted into a shapefile file in batches, meanwhile, for a single file, application software FME which receives conversion of multiple formats is adopted to convert the data into the shapefile, and uniform integration of multi-source data is achieved.

The analysis of the fused data is combined, the conversion between data formats is realized by adopting a form of combining FME software and codes, and the integration of multi-source data is realized, as shown in FIG. 7.

(II) real-time fusion unification of multi-source road network multi-level models

1. Multi-level real-time unified model design

The multi-source road network multi-level unified model abstracts real-world geographic entities and mutual relations, and constructs a plurality of geographic space recognizing windows taking a geographic area as a boundary, wherein the data area comprises a plurality of data blocks, each data block comprises a plurality of geographic element layers, each element layer is relatively independent in data structure and organization, the element layers are used as basic units for data updating, query, analysis and display operation, and the geographic element layers comprise a plurality of geographic elements which are basic representations of the geographic entities and representations.

The multi-level unified model of the multi-source road network organizes data by adopting a hierarchical structure, simultaneously adopts a topological structure form, and is operated based on the whole target of a surface object, so that the integrated management of attribute and geometric data is realized, and the integration requirement of multi-source data is met.

The multi-source road network multi-level unified model takes geographic element objects as the most basic element units, each geographic element object is taken as the most basic element unit, attribute data and spatial data of the geographic element objects are integrally stored, and the whole process of object-oriented data storage and GIS data operation is adopted, so that the data operation is simple, and the addition and deletion of the spatial objects and the expansion of various new data types are more flexible.

2. Joint unification of real-time fusion model and algorithm

The effectiveness and the precision of the data model are improved through a multi-source vector multi-level fusion algorithm, and according to different levels, fusion of different algorithms is achieved at different levels.

Extraction of LOD-O level: and (3) fusing the POI data at an LOD-O level by adopting the relationship between the position and the semantic approximation degree in model design, realizing fused vector network data of the POI data at an LOD-0 level, and acquiring the node and the geometric center position of the POI data according to DLG data with the highest precision level to realize the fusion of the 0 level.

(III) adaptive matching fusion of vector road network data

1. POI matching fusion based on combination of position and semantics

The method and the device improve the accuracy of the POI fusion set by combining the spatial position and the attribute information, and the POI matching fusion scheme based on the combination of the position and the semantics is shown in figure 8.

2. Road network matching fusion based on road network change mode

The differences in spatial expression due to changes in geographic environment include three categories:

aiming at a vector data change mode, the method meets the requirements of real-time and dynamic vector data fusion, and map change of a time point t when the time point t of a road network is matched at a certain time point t comprises two modes, wherein one mode is that road network data A at the time t is matched with t_iMap matching of road network data B, and updating the matching with POl data transmitted in real timeMap matching at a certain time.

(IV) matching and aggregating probe cars

The method is characterized in that the data of the probe car adopts a satellite navigation positioning technology, driving longitude and latitude and time information of the probe car are recorded regularly at intervals of a period of time (several seconds) according to the sampling rate of the probe car, meanwhile, the potential instantaneous speed longitude and latitude information is processed by an analysis mode such as probe car clustering and a model algorithm, text information of the probe car is converted into space data format information, so that the text information of the probe car is associated with road network data in time and space, the change of the road network information is dynamically detected in real time, and the communication relation between lanes is excavated, so that the geometrical, semantic and topological information of the urban complex road network is extracted.

1. Calculation of feature elements of probe vehicle for multi-level fusion and update of road network

The probe vehicle data definition and calculation related to road network multi-level fusion updating comprises the following steps:

1) traffic volume ps in the measurement direction:

p_s＝(X_d+Y_e)/(r_d+r_e) Formula 1

Wherein:

Y_e: on the LOD-3 level, when a certain vehicle to be detected runs in an undetermined direction, subtracting the number of the vehicles overtaking the detection vehicle from the number of the vehicles overtaking the detection vehicle (the traffic volume in the direction opposite to the detection direction of the detection vehicle);

r_s: on the LOD-3 level, the running time, unit, when a certain vehicle to be measured runs along the direction of the flow to be measured: and (5) min.

2) Mean time of flight T_s：

R_s＝r_s-Y_s/p_sFormula 2

Wherein: r_sThe average travel time of the road section is measured;

3) average vehicle speed U_s：

U_s＝(J/R_s) X 60 formula 3

when the formula 1 to the formula 3 are used for calculation, X_d，Y_e，r_d，R_sThe arithmetic mean value is taken for calculation, in order to obtain a better correction fusion effect and avoid the influence of noise data on the fusion process, the data set is cleaned, the abnormity of the vehicle positioning information is judged by solving the standard deviation of the first-order difference of the positioning information, the direction abnormal value is cleaned by judging the moving range of the vehicle positioning information through the variance of longitude and latitude, and the error statistics of the data process caused by error data is avoided by eliminating gross error data.

2. Information matching between probe vehicle and road network

The data of the detection vehicle has a sub-meter-level positioning error, and meanwhile, in an urban environment, the satellite navigation positioning precision error is larger due to the influence of large-scale and high-density urban buildings, so that the data of the detection vehicle cannot directly fall in a road range. Meanwhile, urban road networks are very dense and complex, map matching of road network data and probe vehicle data is indispensable for positioning probe vehicle data on correct roads, and because probe vehicle text information only reflects vehicle position (longitude and latitude coordinates) and time information and cannot be directly associated with the road network data, positioning errors of the probe vehicle data need to be corrected according to prior knowledge of high-precision lane-level road network data, the probe vehicle data position information is corrected to correct road networks, and the probe vehicle text data information and the road network data information are associated through a map matching algorithm.

3. Probe vehicle hot spot path calculation based on cluster analysis

On the undirected basis, the method comprises the steps of searching track data of detection vehicles connected with equidirectional density by increasing the equidirectional condition basis, mining the distribution rule of the data of the detection vehicles, generating directed edges according to aggregation classes, completing LOD-4 level data extraction, obtaining a topological structure diagram of a complex road network, and realizing directed clustering as shown in figure 9, wherein (a) is a directed clustering effect diagram, and (b) is a topological structure diagram generated in a self-adaptive manner.

The method has the advantages that the relevance between the spatial distribution pattern of the track data of the detection vehicle and the traffic geographic characteristics is further analyzed by analyzing the similarity and the aggregation characteristics of the spatial data of the detection vehicle, the spatial distribution pattern and the traffic geographic relevance of the data of the detection vehicle are finally mined by cluster analysis, the approximate cluster analysis of the directed data is adopted, the high-precision correction is not needed according to the priori knowledge of the lane-level road network data, the road network cluster analysis effect of the data of the detection vehicle is achieved, and the dynamic and timely fusion of the road network and the data of the detection vehicle is realized.

Third, experiment and analysis

(one) construction of three-dimensional scenes

The construction of the three-dimensional scene comprises the steps of carrying out three-dimensional reconstruction on a Beijing area data background by adopting a DEM (digital elevation model) and a DOM (document object model), loading road network data to the three-dimensional background and finally loading probe vehicle data. The three-dimensional scene is constructed for verifying the elevation accuracy of road network and probe vehicle data.

1. Three-dimensional background modeling

The elevation data file of the three-dimensional scene adopts a DEM _1 file, the DEM _1 file is converted into a riff format through ENVI, the texture map file adopts a DEM.riff file of the same area, a terrain tool based on an MFC is written by using OSG, the DEM is read, and a 3D scene model is generated, as shown in FIG. 10, the road surface fluctuation is not large.

2. Probe vehicle data loading

Firstly, selecting probe vehicle data covered in the area range, sequentially importing the probe vehicle data (the probe vehicle data converted into shp data) meeting the conditions into a database according to a time sequence, and overlaying the drawn three-dimensional scene graph into road network data, so that the ground features of the change described by the two types of data due to time difference can be conveniently visualized, such as the ground features shown in the graph 11, and the preparation is made for verifying and analyzing the effectiveness, the accuracy and the efficiency of the fusion model.

3. And loading road network data.

The road network data is roughly divided into national basic geographic information data and road network data according to the classification, wherein the basic geographic data only studies DLG data and vector data produced by vector data production departments of all levels, the road network data comprises wide data which comprise data of multiple sources such as a sky map, a high-grade map and a Baidu map, heterogeneous data problems caused by different production requirements of the data are processed through a data consistency processing flow, the multi-source road network data is contained under the same geographic framework, and fig. 11 is a superposition effect graph of probe vehicle data and the road network data under the unified geographic framework, so that the situation that the road network data loading and the probe vehicle data do not have superposition gross errors can be seen under the unified geographic framework.

4. Simulation verification update

Extracting a certain characteristic vector (density, direction and the like) of a road section according to the obtained probe vehicle data, constructing traffic vector linear characteristics and a structural relationship, and then verifying the correctness and the precision level of the data fusion effect by fusing and associating the vector and comparing the vector with the actual road network condition of each node and DOM data. After the experimental data are preprocessed, all the data are superposed together, and the simulation and fusion results are verified by carrying out various statistical analyses on the data of the detection vehicles on the road network. In the POI points, 760 objects in an OSM map and a Baidu map are selected respectively, and according to rules, each POI data uniquely represents a geographic entity without repeated problems; in road network data, an LOD-0 level is obtained by map digitalization, geometric connectivity between objects is guaranteed, an LOD-1 level is expanded to a planar structure from a dotted structure by attributes, a planar structure with an outlet is obtained in the data flow direction of a probe vehicle by an LOD-2 level, updating is detected by calculating the relative driving number of the probe vehicle data by an LOD-3 level, and LOD-4 is detected by the data (density) of the probe vehicle data in the same direction.

Multi-source POI data fusion results: and 4 groups of POI data sets are selected to participate in the test, the POI sets have different contact degrees, a positive example is defined that 2 interest point sets have matching items, and a negative example is defined that no matching item exists. After the POI data are processed by the POI data fusion algorithm and are visually checked, the two groups of data have better fusion effect after being processed by the point fusion algorithm, and only individual data cannot be correctly matched due to larger coordinate error between maps.

Road network data fusion result: the experimental verification analyzes the effect of data fusion updating when multi-source data is loaded into a multi-level data fusion model. As shown in fig. 12, the dotted line portion is 2021 year old data, the dotted data is 2020 year probe car data and linear data connected by the dotted data and vectorized, and the dotted line is a high and vectorized homonymous point connection line. And fusing probe vehicle data based on the characteristics of the central line of the multi-source road network data, stacking the multi-source vector data, mining information, and sequentially loading the data into a database according to the design of a fusion model to further obtain information data of the multi-level road network. As can be seen from the figure, after the data of the probe vehicles are corrected through the data of the lane road network, the data of the probe vehicles can be subjected to lane data fusion in real time according to the topology of the data of the probe vehicles, and the requirement of multi-level visualization is met.

(II) effectiveness

Through the experimental analysis, POI data, probe vehicle data and road network data are loaded in the multi-level road network model designed by the application, and the fusion of multi-source vector data is realized. Experiments show that the multi-source vector multi-level fusion achieves a good fusion effect, so that the effectiveness of the road network model and the multi-source traffic vector road network data fusion method is verified.

(III) accuracy

The multisource traffic vector road network data fusion tool is mainly characterized in that sub-meter-level road data are included in a fusion range, requirements of different levels can be met, and the multisource traffic vector road network data fusion tool is obviously superior to other data model fusion tools in the aspect of precision.

The multisource traffic vector road network data fusion tool based on the CityGML standard and taking a multi-level multisource traffic vector road network data model with the vector data fusion as the target as the core basically achieves the requirements of effectiveness and efficiency while ensuring the use of lane-level data.

Claims

1. A multi-level real-time fusion updating method for a multi-source traffic GIS road network is characterized in that a multi-level object-oriented lane-level road model is set based on an OGC standard; secondly, a five-layer data fusion model based on the CityGML is provided by adopting high-precision fusion of a vector network and POI data, and a multi-layer real-time fusion updating method covering the traffic GIS road network is realized;

2. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that a fusion model is divided into five levels: the vector geographic data mainly relates to various data on a traffic network, is generally summarized into six basic elements of a traffic lane, an intersection, a tunnel, a viaduct, a rotary island and a square, and the following hierarchical division rules are formulated:

LOD-1: describing the shape of various characteristics by adopting a surface;

3. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that a fusion model design framework: the multi-level road network model performs grouping and sub-packaging according to logical properties, and is characterized in that:

4. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that a geospatial data logic model is designed as follows: describing each level of the multi-level logic model in geometry, semantics and topology:

5. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that multi-source space vector data consistency processing: the method comprises the following steps of unifying space coordinates and map projection, unifying classification grading and attribute coding and unifying data format conversion;

6. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that the unified combination of classification and attribute coding: adopting different classification grading and attribute coding modes for the vector road network and POI data, wherein the classification grading of the vector road network is according to a six-class five-layer classification system in a road network model;

7. The multi-source traffic GIS road network multi-level real-time fusion updating method of claim 1 is characterized in that a multi-level real-time unified model is designed, wherein real-world geographic entities and mutual relations are abstracted, a plurality of geographic space recognizing windows with geographic areas as boundaries are constructed, each data area comprises a plurality of data blocks, each data block comprises a plurality of geographic element layers, each element layer is relatively independent in data structure and organization, and the operation of data updating, querying, analyzing and displaying takes the element layers as basic units;

8. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that POI matching fusion based on combination of position and semantics: the accuracy of the POI fusion set is improved by combining the information based on the spatial position and the attribute information;

9. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 1, characterized in that the matching and aggregation of detection vehicles: the method comprises the steps that the data of a probe car are subjected to satellite navigation positioning technology, driving longitude and latitude and time information of the probe car are recorded regularly at intervals according to the sampling rate of the probe car, meanwhile, potential instantaneous speed longitude and latitude information is processed through a probe car clustering analysis mode and a model algorithm, text information of the probe car is converted into space data format information, the potential instantaneous speed longitude and latitude information is associated with road network data in time and space, road network information change is dynamically detected in real time, the communication relation between lanes is excavated, and geometric, semantic and topological information of the urban complex road network is extracted;

1) traffic volume ps in the measurement direction:

p_s＝(X_d+Y_e)/(r_d+r_e) Formula 1

Wherein:

2) mean time of flight T_s：

R_s＝r_s-Y_s/p_sFormula 2

Wherein: r_sThe average travel time of the road section is measured;

3) average vehicle speed U_s：

U_s＝(J/R_s) X 60 formula 3

10. The multi-source traffic GIS road network multi-level real-time fusion updating method according to claim 9, characterized in that a probe vehicle is matched with road network information: correcting the positioning error of the probe vehicle data according to the priori knowledge of the high-precision lane-level road network data, correcting the position information of the probe vehicle data to the correct road network, and establishing association between the probe vehicle text data information and the road network data information through a map matching algorithm;