CN108920481A - A kind of road network method for reconstructing and system based on mobile phone location data - Google Patents
A kind of road network method for reconstructing and system based on mobile phone location data Download PDFInfo
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Abstract
一种基于手机定位数据的道路网重建方法及系统，包括：获取原始数据集，原始数据集包括训练数据集和预测数据集；原始数据集为根据手机定位数据得到轨迹点；对训练数据集和预测数据集分别利用时间和密度两个维度进行特征构建与组合，并随之生成训练集和预测集；使用生成的训练集，基于机器学习中的算法得到采样点分类模型；将学习到的分类模型应用于预测集，将轨迹点划分为道路点和非道路点；基于约束Delaunay三角网对已识别出的道路点进行道路构建；按照拓扑关系，修改道路中心线，从而完成道路网的重建。本发明提出了一种高质量的精细化道路网生产和更新的新方式，将为无缝导航技术尤其是精细级道路网方面提供技术支撑，扩大路径导航服务的覆盖范围。
A road network reconstruction method and system based on mobile phone positioning data, comprising: obtaining an original data set, the original data set includes a training data set and a prediction data set; the original data set is to obtain trajectory points according to the mobile phone positioning data; the training data set and The prediction data set uses the two dimensions of time and density to construct and combine features, and then generates a training set and a prediction set; using the generated training set, the sampling point classification model is obtained based on the algorithm in machine learning; the learned classification The model is applied to the prediction set, and the trajectory points are divided into road points and non-road points; road construction is carried out on the identified road points based on the constrained Delaunay triangulation; according to the topological relationship, the road centerline is modified to complete the reconstruction of the road network. The invention proposes a new way of producing and updating high-quality refined road networks, which will provide technical support for seamless navigation technology, especially fine-level road networks, and expand the coverage of route navigation services.
Description
Technical field
The present invention relates to space-time trajectory data digging technology, especially a kind of road network based on mobile phone location data is rebuild
Method and system belong to GIS-Geographic Information System and intelligent transportation research field.
Background technique
Road is the important infrastructure in city, during rapid urban, how to be replenished in time and more new town
Road data is a problem urgently to be resolved to keep its accuracy, timeliness and degree of perfection.Residential communities are advised in city
Mould constantly expands, and internal passageway net is increasingly fine and complicated, but existing cell-level road information fine degree is not enough even
Missing causes outdoor thin portion navigation in residential block to there is " blind area ", causes very big obstacle to the development of outdoor seamless navigation technology.
At the same time, road network structure information is the core content of electronic map basic data, and even more electronic map realizes navigation feature, intelligence
The data basis of energy traffic system, therefore, accurately and in time more new town fine-grade road network is an important problem (city
City's fining road network refers to the cell-level branch that Intra-cell is respectively closed in city).
The convenience obtained with crowd-sourced track data and its real-time updated are greatly improved, academia and work
Industry can be readily available a large amount of track datas comprising space time information, so that the road network based on space-time trajectory data mentions
Method is taken to be possibly realized.Currently, there are mainly two types of Automatic Road Extraction methods：One is mention from vehicle GPS track data
Take road information；Another kind is to extract road network information based on high-resolution remote sensing image.But existing road network extracting method master
It concentrates in the research of each major trunk roads in city, and the fine road network of cell-level is usually ignored in city.Wherein, remote sensing image because
Remote for image-forming range, coverage area is big, while portraying the scarce capacity of different spaces scale fine information, based on such data
Processing method is only capable of preferably solving the information extraction of general major trunk roads, can not finally solve fining road network building and ask
Topic；In addition, the acquisition of GPS track data is limited inside the building range such as general school, cell to a certain extent, and pedestrian
Mobile trajectory data randomness is strong, and sample frequency is low, comprising various types of ground objects such as road, building and squares, so that being based on
Mobile trajectory data extracts road network, and there are numerous difficulties.
Summary of the invention
The technical problems to be solved by the invention are for road fine degree in existing cell-level road network not enough and part
The problem of missing, provides a kind of fining Road network extraction method, can be convenient the building work for accurately completing fine-grade road network
Make.This method will be converted into two classes of waypoint Yu non-rice habitats point the problem of carrying out road network reconstruction based on extensive pedestrian's data
Classification problem carries out the building of feature set using the various combination of two dimensions of time and density first, generates training set, then
Learn sampled point disaggregated model using random forests algorithm, application class model divides data set later, obtains road
Point set finally borrows constraint Delaunay triangulation network to having identified that road point set carries out road building and mention with road axis
It takes, to complete the building for refining road network in closed area.It includes the following steps：
Step 1) obtains raw data set, and raw data set includes training dataset and predictive data set；Wherein original number
It is that tracing point is obtained according to mobile phone location data according to collection；
Step 2) is utilized respectively two dimensions of time and density to training dataset and predictive data set and carries out feature construction
With combine, and generate training set and forecast set therewith；
Step 3) learns to obtain sampled point disaggregated model based on the algorithm in machine learning using the training set generated；
The disaggregated model learnt is applied to forecast set by step 4), and tracing point is divided into waypoint and non-rice habitats point；
Step 5) clicks through the building of trade road to the road identified based on constraint Delaunay triangulation network；
Step 6) modifies road axis, to complete the reconstruction of road network according to topological relation.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, training in the step 1
Data set is obtained according to following manner：The distribution pattern for obtaining multiple closing Intra-cell tracing points, selects tracing point distribution
The different multiple nonoverlapping small-scale regions of mode form training dataset.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, density in the step 2
Characteristic value includes：Binding time dimension carries out Delaunay Triangulation, the institute that each tracing point is connected to training dataset
There is the average length of adjacent triangle edges.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, density in the step 2
Characteristic value includes：Binding time dimension constructs Voronoi diagram, Voronoi diagram corresponding to each tracing point to training dataset
The inverse of middle area of a polygon.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, the time in the step 2
Specific step is as follows with the feature construction of two latitudes of density and combination：
By analyzing the behavior pattern of people, based on the space-time Density Distribution difference structure for closing small internal road and non-rice habitats
Feature space is built, that is, passes through track points amount, the adjoining triangle edges length based on constraint Delaunay triangulation network in buffer area
It is poor with the aggregation of three kinds of geometrical characteristic joint time dimension expression tracing point spatial and temporal distributions of area of polygon in Voronoi diagram
It is different；The identification of road waypoint is carried out based on the feature set building classifier that above-mentioned three kinds different geometry dimension control conditions are established.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, learn in the step 3
Algorithm includes random forest, support vector machines.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, for training dataset
In track point data generated based on road net data incomplete in OSM according to certain radius using corresponding OSM data
Line buffer area falls in the tracing point in buffer area labeled as 1, falls in the tracing point except buffer area labeled as 0, is based on this, will
The tracing point that training data is concentrated is divided into two class of waypoint and non-rice habitats point, label when as subsequent training machine learning model
Data.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, the step 5 is specifically wrapped
It includes：
The road point set building constraint Delaunay triangulation network formed using the road point extracted in step 4, root
According to adjacent triangle number, the triangle in the triangulation network is divided into 3 classes, respectively correspond the entrance of road, the trunk of road and
Intersection is respectively handled it, road axis is extracted, the specific implementation process is as follows described：
Building constraint Delaunay triangulation network, the triangle in the triangulation network is divided into the road point set identified
3 classes：Only having adjacent triangle on one side is A class, and it is B class that, which there is adjacent triangle on both sides, and there is adjacent triangle on three sides
For C class, A class triangle connects the vertex of the midpoint on unique proximity side corresponding thereto, and B class triangle connects in two neighbouring sides
Point, C class triangle connect the midpoint of center of gravity and three sides, in search connection procedure, first search for, terminate since C class triangle
In A class triangle or C class triangle, network a line is obtained, until having handled C class triangle；It uses and is opened from A class triangle again
Begin to search for, terminates at A class triangle, obtain all sides, to form road axis network.
Further, in the road network method for reconstructing of the invention based on mobile phone location data, the step 6 is specifically wrapped
It includes：
Optimization and perfect road center line, carry out the road axis near intersection processing is straightened, to complete to seal
Closed region refines the reconstruction of road network, and specific processing mode is as described below：
For the arc road network near intersection, since crosspoint, straight line is cropped with certain length, then compare sanction
The angle of rear remaining line is cut, when the angle of two lines meets a certain range, two lines of connection are into a line, when being crossroad,
Node of the intersection point as net is acquired, road network is ultimately formed, to complete the reconstruction of closed area road network.
The present invention is to solve its technical problem, additionally provides a kind of road network reconstructing system based on mobile phone location data,
Road network reconstruction is carried out using the road network method for reconstructing based on mobile phone location data of any of the above-described.
Implement the road network method for reconstructing and system of the invention based on mobile phone location data, the present invention is according to the row of people
Mining analysis is carried out to track data according to space-time density feature for mode, using included in a large amount of sparse mobile phone location data
In semantic information, propose it is a kind of constructed using mobile phone location data fining road network new method, biography can be improved
Unite road network update efficiency and reduce maintenance cost, substantially reduce road network extraction time, can be make up cell-level road
The not perfect and missing problem of information provides a kind of new mode.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing：
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the schematic diagram that the present invention calculates each area of a polygon in Voronoi diagram；
Fig. 3 is the schematic diagram for the average length that the present invention calculates the adjacent triangle edges of tracing point in Delaunay triangulation network.
Specific embodiment
For a clearer understanding of the technical characteristics, objects and effects of the present invention, now control attached drawing is described in detail
A specific embodiment of the invention.
The present invention relates to a kind of road network method for reconstructing based on mobile phone location data, carries out at fining road network
Reason.The present invention is analyzed by the distribution pattern to multiple closing Intra-cell tracing points first, then selects different community
The small range regions of interior multiple representative distribution patterns constructs training dataset, and remainder incorporates prediction data into
Collection for the track point data that training data is concentrated, carries out manual sort, is divided into waypoint and non-rice habitats point two at the same time
Class, the label value as training data；Using two dimensions of time and density carry out feature constructions with combine, wherein time dimension
It can be by being divided into 7 different periods (6 for one day:00-8:00,08:00-12:00,12:00-14:00,14:00-17:
00,17:00-20:00,20:00-24:00,24:00-06:00) it realizes, in terms of density, can be constructed from three angles
Feature：Points amount in track in buffer area, the area of each polygon based on Voronoi diagram and based on constraint Delaunay tri-
The average length of the adjacent triangle edges of each tracing point is netted at angle, combines all of above feature and corresponding with each tracing point
Label value forms training set；Using the training set of generation, based on the random forests algorithm study sampled point classification in sorting algorithm
Model；Later, the disaggregated model acquired is applied in predictive data set, for the road waypoint in the point data of identification prediction track
With non-rice habitats point, road point set is obtained；Finally in road point set, using constraint Delaunay triangulation network, road is extracted
Center line completes the building of fining road network in closing cell.
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing and specific implementation
Example, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only to explain this hair
It is bright, it is not intended to limit the present invention.
As shown in Figure 1, Fig. 1 is the side provided by the invention for carrying out fining road network based on mobile phone location data and rebuilding
Method flow chart, the described method comprises the following steps：
Step 1, raw data set is obtained, raw data set includes training dataset and predictive data set；Wherein original number
It is that tracing point is obtained according to mobile phone location data according to collection.Preferably, concentrate selection tracing point distribution pattern different from initial data
Multiple nonoverlapping small-scale regions form training datasets, remaining track point data is divided into predictive data set；Meanwhile it is right
In the track point data that training data is concentrated, needs to carry out manual sort, be divided into two class of waypoint and non-rice habitats point, as subsequent
Label data when training machine learning model.
Embodiment is implemented as follows：
For raw data set, tracing point distribution situation therein is analyzed first, it is multiple from different area of space selections
Different distribution patterns, such as：Center Assembled distribution, ribbon distribution, random discrete distribution etc., by a variety of distribution patterns
Track point data is merged into as training dataset, remaining track point data is divided into predictive data set；Meanwhile for training data
Corresponding OSM data can be used in the track point data of concentration, based on incomplete road net data in OSM according to certain half
Diameter generates line buffer area, falls in the tracing point in buffer area labeled as 1, falls in the tracing point except buffer area labeled as 0, is based on
This, is divided into two class of waypoint and non-rice habitats point for the tracing point that training data is concentrated, when as subsequent training machine learning model
Label data.
Wherein, the radius of line buffer area can be determined according to the mean radius of cell-level branch in city, Intra-cell road
The radius on road is generally within 8 meters.
Step 2, two dimensions of time and density are utilized respectively to training dataset and predictive data set to complete feature set
Building with combine, and generate training set and forecast set therewith.Wherein time dimension can be by being divided into 7 differences for one day
Period can carry out construction feature from three angles in terms of density：Points amount in track in buffer area, based on Voronoi diagram
The area of each polygon and the average length that triangle edges are abutted based on each tracing point of constraint Delaunay triangulation network, group
All of above feature and label value corresponding with each tracing point are closed, training set is formed.
Specific building process is as follows：
According to the trip of people rule, on time dimension, 7 periods was divided by one day, are 06 respectively:00-08:
00,8:00-12:00,12:00-14:00,14:00-17:00,17:00-20:00,20:00-24:00,24:00-06:00；?
In terms of density, buffer area radius takes 3 different values according to road conditions respectively：8m, 12m, 16m, by 3 buffer area radiuses and 7
A period carries out feature two-by-two and combines, and constructs 21 features, for indicating some tracing point in special time period, radius is
Density value in 8/12/16 meter, quantitative formula are as follows：
Wherein, { 6,8,12 } Radius=, Point={ P1,P2,....,Pn, Time=(6,8), (8,12), (12,
14), (14,17), (17,20), (20,24), (0,6) },It indicates in period TaUnder, tracing point PiIt is R in radiusbModel
The number of interior included tracing point is enclosed, the combination of different time sections and different radii feature is as shown in the table：
Later according to 7 different periods, training dataset is divided into 7 parts, construct respectively corresponding Voronoi diagram and
Delaunay triangulation network is constrained, for the Voronoi diagram that building obtains, the area of each polygon in figure is calculated, calculates therewith
The inverse of area, if Fig. 2 is a part of a Voronoi diagram, the present invention needs to calculate polygon R corresponding to tracing point P
Area, and the inverse and time dimension of area are combined, as 7 features of expression track vertex neighborhood density, are calculated public
Formula is as follows：
In above-mentioned formula,Indicate point PiThe area of corresponding polygon；Then for constraint Delaunay triangle
Net calculates the average length of the adjacent triangle edges of each tracing point, if Fig. 3 is a part of a Delaunay triangulation network, this
Invention needs to calculate the average length of all adjacent edges of tracing point P, and by it in conjunction with time dimension, adjacent as expression tracing point
Other 7 features of domain density, calculation formula are as follows：
In above-mentioned formula, Neighbor_P indicates point PiThe set of all consecutive points, L indicate the distance between consecutive points, such as
A in Fig. 3, b, c, d, e, f, g, h；Finally, by above-described 35 features and corresponding label described in step 1
Value merges, and generates training set.
Step 3, using the training set of generation, based in Python machine learning packet scikit-learn
RandomForestClassifier module, on the basis of partial parameters in adjustment algorithm, sampling that learning data is concentrated
Point disaggregated model.
Step 4, the sampled point disaggregated model learnt based on training set is applied in predictive data set, it is pre- for identifying
The road waypoint and non-rice habitats point in the point data of track are surveyed, road point set therein is extracted.
Step 5, using the road point set building constraint Delaunay triangulation network extracted in step 4, according to adjoining
Triangle in the triangulation network is divided into 3 classes by triangle number, is respectively corresponded the entrance of road, the trunk of road and road and is handed over
Prong is respectively handled it, road axis is extracted, the specific implementation process is as follows described：
Building constraint Delaunay triangulation network, the triangle in the triangulation network is divided into the road point set identified
3 classes：Only having adjacent triangle on one side is A class, and it is B class that, which there is adjacent triangle on both sides, and there is adjacent triangle on three sides
For C class, A class triangle connects the vertex of the midpoint on unique proximity side corresponding thereto, and B class triangle connects in two neighbouring sides
Point, C class triangle connect the midpoint of center of gravity and three sides, in search connection procedure, first search for, terminate since C class triangle
In A class triangle or C class triangle, network a line is obtained, until having handled C class triangle；It uses and is opened from A class triangle again
Begin to search for, terminates at A class triangle, obtain all sides, to form road axis network.
Step 6, optimization and perfect road center line, carry out the road axis near intersection processing is straightened, thus
The reconstruction of closed area fining road network is completed, specific processing mode is as described below：
For the arc road network near intersection, since crosspoint, straight line is cropped with certain length, then compare sanction
The angle of rear remaining line is cut, when the angle of two lines meets a certain range, two lines of connection are into a line, when being crossroad,
Node of the intersection point as net is acquired, road network is ultimately formed, to complete the reconstruction of closed area fining road network.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (10)
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| CN109684384A (en) * | 2018-12-29 | 2019-04-26 | 太原科技大学 | A kind of track data space-time density Analysis System and its analysis method |
| CN109934865A (en) * | 2019-03-12 | 2019-06-25 | 重庆市勘测院 | Extraction method of road network centerline |
| CN110008872A (en) * | 2019-03-25 | 2019-07-12 | 浙江大学 | A road network extraction method combining vehicle trajectories and remote sensing images |
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| CN113052084A (en) * | 2021-03-26 | 2021-06-29 | 中国地质大学（武汉） | Community-level vector road network extraction method based on mobile phone positioning data |
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