CN112462401B - Urban canyon rapid detection method and device based on floating vehicle track data - Google Patents
Urban canyon rapid detection method and device based on floating vehicle track data Download PDFInfo
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Abstract
The invention relates to the technical field of satellite navigation and positioning, and provides an urban canyon rapid detection method and device based on floating car track data, which comprises the following steps: selecting a sampling area to obtain a track data setP(ii) a Establishing a buffer area for the urban road in the sampling area, and generating a grid set corresponding to the urban roadR(ii) a Extracting a set of trajectory dataPTrack data of a certain vehicleP K (ii) a Extracting trajectory dataP K The abnormal points in (1) are set into the gridsRPerforming superposition analysis with the abnormal points and marking the grid setRA grid intersecting the outlier; from a set of trajectory dataPRemoving track dataP K (ii) a Determining a set of trajectory dataPWhether it is empty; statistical grid aggregationRThe number of times each grid is marked exceeds a thresholdHThe grid output of (a) is an urban canyon region. The track data used by the method is high in updating speed, the timeliness of the detection result is better, and the urban canyon region change in urban construction change can be reflected more truly.
Description
Technical Field
The invention relates to the technical field of satellite navigation and positioning, in particular to a method and a device for rapidly detecting urban canyons based on floating car track data.
Background
A Global Navigation Satellite System (GNSS) is a space-based wireless Navigation System that can provide information on three-dimensional coordinates, motion trajectories, etc. to people at any position on the earth's surface or in the near-earth space. The GNSS is widely applied to various fields of national defense and economic society, such as military affairs, communication, electric power, finance, transportation, basic mapping, disaster prevention and reduction and the like. With the continuous development of GNSS, the positioning accuracy is also improved. The performance of satellite positioning is closely related to the scene, including the number of visible satellites, geometric accuracy factors, signal interruption frequency, signal strength, multipath effect, deception source and other factors, which all affect the accuracy and usability of the satellite positioning result. In an open environment, the quality of a GNSS signal is good, in an urban environment, the GNSS signal is often shielded by high buildings, terrains, dense forests or reflected for multiple times to cause the situation that the positioning precision is poor or even the positioning cannot be performed, and the urban environment with the problems is called as an urban canyon. When the positioning terminal is located in an urban canyon, the satellite signal positioning performance is remarkably reduced, so that the related application is difficult to meet the actual production and living requirements. If the range of the urban canyon can be detected in advance, corresponding algorithm design and road traffic infrastructure transformation can be carried out, and the method is favorable for improving the positioning accuracy and the positioning effect in the urban canyon region.
In an urban complex environment, two main factors influencing the performance of the GNSS are occlusion and multipath effects, which are reflected by data loss or position drift at a certain positioning point on trajectory data. The traditional urban detection method mainly utilizes an urban three-dimensional model or a street view image, analyzes building height and position information, and combines a satellite altitude angle to calculate urban canyon distribution. However, the acquisition of the three-dimensional model data of the urban building has the defects of high cost, long time consumption and slow updating. The city is a constantly changing subject, and the position of the urban canyon is constantly changing along with the change of ground surface coverage data such as buildings, vegetation, water bodies and the like, so that the requirement of urban canyon change detection is difficult to meet by using urban three-dimensional data.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to solve the technical problem that the urban canyon change detection requirement is difficult to meet by using urban three-dimensional data in the prior art.
In order to achieve the purpose, the invention provides an urban canyon rapid detection method based on floating car track data, which comprises the following steps:
s1: selecting a sampling area, and carrying out track data acquisition on a plurality of vehicles in the sampling area to obtain a track data setP；
S2: establishing a buffer area for the urban road in the sampling area, and generating a grid set corresponding to the urban roadR；
S3: extracting the set of trajectory dataPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; proceeding to step S4;
s4: extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setRThe grid intersecting the abnormal point is entered to step S5;
s5: from the set of trajectory dataPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, go to step S6; if not, returning to the step S3;
s6: counting the grid setRThe number of times each grid is marked is made to exceed a threshold valueHThe grid output of (a) is an urban canyon region.
Preferably, in step S2, the grid setREach grid in (a) is specifically represented as:
wherein the content of the first and second substances,ja number representing a grid;CNT j representing the number of outliers in the grid;xl j represents the longitude of the lower left corner of the grid;yl j representing the latitude of the lower left corner of the grid;xR j represents the longitude of the upper right corner of the grid;yR j representing the latitude of the upper right corner of the grid; h denotes the number of the mesh set.
Preferably, in step S3, the trajectory dataP K The concrete expression is as follows:
wherein the content of the first and second substances,Tthe maximum sampling times of the track;Knumbering the vehicles;P i is the first in the trackiTracing points;t i is shown asiSampling time of each trace point;v i indicating that the vehicle ist i The speed of travel at that moment;x i to representt i Longitude of the time track point;y i to representt i The latitude of the time trace point.
Preferably, in step S4, the step of extracting the data missing point in the outlier specifically includes:
s401: from the trajectory dataP K Middle pass sampling timet n Two adjacent track points are taken out in sequenceP n AndP n+1 ，nis from 1 toT-A positive integer of 1, or a mixture thereof,nis 1; if the track pointP n And track pointP n+1 The sampling time interval is greater than the preset timet m If the data missing point exists, the data missing point and the track point existP n And track pointP n+1 To (c) to (d);
s402: by means of tracing pointsP n And track pointP n+1 Obtaining the coordinates of the data missing point by the coordinate calculation of (2) so thatnThe value of (a) is added to 1, and the formula is specifically as follows:
wherein (A), (B), (C), (D), (C), (B), (C)x,y) Coordinates of the data missing point; (x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a);
representing points of trackP n And track pointP n+1 The time interval between two adjacent points;t m is a preset time;
s403: repeating step S401 and step S402T-1 time, calculating said trajectory dataP K All of the data missing points in (1).
Preferably, in step S4, the step of extracting the spatial localization drift point in the outlier specifically includes:
s411: from the trajectory dataP K Middle pass sampling timet n Sequentially taking out three adjacent track pointsP n 、P n+1 AndP n+2 ，nis from 1 toT-A positive integer of 2 is a positive integer of,nis 1;
s412: judging the track pointP n 、P n+1 AndP n+2 the distance between and the average distanceSThe magnitude relationship of (1);
if it isP n AndP n+1 is less than or equal toSAnd is andP n+1 andP n+2 is greater thanSThen point of trackP n+2 For spatially locating drift points and track pointsP n+2 The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is less than or equal to S, the track points areP n For spatially locating drift points and track pointsP n The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is greater than S, the track points areP n+1 For spatially locating drift points and track pointsP n+1 The coordinates of (a) are:
wherein (A), (B), (C), (D), (C), (B), (C)x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a); (x n+2 ,y n+2 ) Is a track pointP n+2 The coordinates of (a);
s413: repeating the steps S411 and S412 for T-2 times, and calculating the track dataP K All of the spatially located drift points.
Preferably, step S6 is specifically:
when the grid is assembledRWhen a grid in (2) is marked once, the gridCNT j Adding 1 to the value of (c); when the grid is inCNT j Value greater than thresholdHThe grid is then output as an urban canyon region.
An urban canyon rapid detection device based on floating car track data comprises the following modules:
a track data acquisition module for selecting a sampling area, acquiring track data of a plurality of vehicles in the sampling area to obtain a track data setP；
A grid set generating module for establishing a buffer area for the urban road in the sampling area and generating a grid set corresponding to the urban roadR；
A track data extraction module for extracting the track data setPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; entering a superposition analysis module;
a superposition analysis module for extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setREntering a grid intersected with the abnormal point into an updating track data set module;
an update track data set module for generating a track data set from the track data setPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, entering an urban canyon region judgment module; if not, returning to the track data extraction module;
the urban canyon region judgment module is used for counting the grid setRAnd outputting the grids with the marked times exceeding a threshold value as the urban canyon region.
The invention has the following beneficial effects:
1. the spatial distribution of the urban canyon region is detected by using the abnormal points existing in the large-scale track data, and the track data is simple to obtain and easy to realize;
2. track sampling time abnormity and space position abnormity are fully considered in the abnormal point extraction process, and the extraction result is more reliable and accurate;
3. according to the method, only the processing and analysis of the trajectory data need to be considered, the calculation of complex factors such as satellite height, angle, position, time period and the like related to urban canyon detection based on urban three-dimensional modeling data is avoided, and the calculation speed is higher;
4. the track data used by the method is high in updating speed, the timeliness of the detection result is better, and the urban canyon region change in urban construction change can be reflected more truly.
Drawings
FIG. 1 is a flow chart of a method for rapid urban canyon detection based on floating car trajectory data;
FIG. 2 is a diagram of an urban canyon region in Wuhan City;
FIG. 3 is a schematic diagram of an urban canyon rapid detection device based on floating car trajectory data;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
With the increase of urban traffic track data in recent years, particularly the increase of GPS positioning devices installed on taxies and buses, floating car track data are acquired in a large scale, the data comprise geographic coordinates, speed, direction, time stamps and other information, the driving process of a vehicle can be completely recorded at a high frequency by virtue of a standard data structure, and a large amount of researches are carried out on the aspects of crowd flow analysis, trip hot spot area extraction, urban space analysis, urban traffic condition prediction and the like by adopting the urban floating car track data; the floating car track data inevitably has abnormal points, part of the abnormal points are generated due to random errors of the positioning terminal, more of the abnormal points are due to systematic influences existing in the driving process of the car, and most of the abnormal points are caused by the phenomena of signal shielding, reflection, refraction and the like in the driving process, and the phenomena are the concentrated reflection of the influence of urban canyon areas on GNSS signals; therefore, when the abnormal points in the trajectory data reach a certain scale, the distribution of the urban canyon region can be extracted completely by performing spatial analysis and processing on the abnormal points in the trajectory data.
Due to the fact that the track data are easy to obtain and fast to update, by exploring the relation between track positioning abnormal points and GNSS positioning abnormal areas, urban canyon areas around roads can be obtained fast in a large range, effective judgment means is provided for accurate updating of road infrastructures, construction and perfection of urban road infrastructures are facilitated, and effective data support is provided for intelligent transportation, unmanned driving, GNSS chip testing and the like;
referring to fig. 1, in view of the above analysis, the present invention provides a method for rapidly detecting an urban canyon based on floating car trajectory data, which obtains abnormal points that cannot be matched to a road network in the trajectory data by matching coordinates of trajectory points in the trajectory data with the road network, and performs spatial clustering on the abnormal points, thereby obtaining an urban canyon region along the road network, and specifically includes the following steps:
s1: selecting a sampling area, and carrying out track data acquisition on a plurality of vehicles in the sampling area to obtain a track data setP；
S2: establishing a buffer area for the urban road in the sampling area, and generating a grid set corresponding to the urban roadR；
S3: extracting the set of trajectory dataPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; proceeding to step S4;
s4: extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setRThe grid intersecting the abnormal point is entered to step S5; the superposition analysis specifically comprises the following steps: comparing the coordinates of the abnormal points with the grid set R one by one, and when the coordinates of the abnormal points are positioned in the grid setRThe mesh in (1) is marked when inside or at the boundary of the mesh.
S5: from the set of trajectory dataPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, go to step S6; if not, returning to the step S3;
s6: counting the grid setRThe number of times each grid is marked is made to exceed a threshold valueHThe grid output of (a) is an urban canyon region.
In this embodiment, wuhan city is used as a sampling region, a grid set of a road buffer area is established on the basis of wuhan city road network data, 9447362 taxi track data in the wuhan city are collected, and an urban canyon region is extracted by extracting abnormal points in the track data set and performing superposition analysis and statistics with a road grid.
Further, in step S2, the grid setREach grid in (a) is specifically represented as:
wherein the content of the first and second substances,ja number representing a grid;CNT j representing the number of outliers in the grid;xl j represents the longitude of the lower left corner of the grid;yl j representing the latitude of the lower left corner of the grid;xR j represents the longitude of the upper right corner of the grid;yR j representing the latitude of the upper right corner of the grid;ha number representing a set of grids.
In the embodiment, urban roads in Wuhan City are downloaded from OpenStreetMap, contain road grade information, and are specifically divided into four levels of urban expressways, main roads, secondary roads and branches; the road data covers the whole Wuhan city; establishing a buffer area for urban roads in Wuhan city, considering the positioning accuracy of GNSS, setting the distance of the buffer area as 50 meters, dividing the buffer area into grids with the width of 25 meters, and endowing each grid with an attribute fieldCNTThis field stores the long shaping variable.
Further, in step S3, the trajectory dataP K The concrete expression is as follows:
wherein the content of the first and second substances,Tthe maximum sampling times of the track;Knumbering the vehicles;P i is the first in the trackiTracing points;t i is shown asiSampling time of each trace point;v i indicating that the vehicle ist i The speed of travel at that moment;x i to representt i Longitude of the time track point;y i to representt i The latitude of the time trace point.
In the embodiment, the track data is derived from the vehicle history record of the management system of the passenger taxi in Wuhan city, and the track data comprises information such as a vehicle number, longitude and latitude coordinates, data uploading time, whether the vehicle carries passengers or not, a power supply state, a GPS (global positioning system) positioning state and the like; for track data setPThe vehicles are grouped according to the vehicle numbers, and after the vehicles are sequenced according to the data uploading time, different vehicles can be sequencedkThe track data of (1) is recorded as track data increasing according to timeP K 。
Further, in step S4, the step of extracting the data missing point in the outlier specifically includes:
s401: from the trajectory dataP K Middle pass sampling timet n Two adjacent track points are taken out in sequenceP n AndP n+1 ，nis from 1 toT-A positive integer of 1, or a mixture thereof,nis 1; if the track pointP n And track pointP n+1 The sampling time interval is greater than the preset timet m If the data missing point exists, the data missing point and the track point existP n And track pointP n+1 To (c) to (d);
in this embodiment, the sampling time average of the vehicle trajectory data in wuhan cityThe interval is 60 seconds, so that the preset time for judging whether the trace point has the data missing point or not is sett m Set to 60 seconds;
s402: by means of tracing pointsP n And track pointP n+1 Obtaining the coordinates of the data missing point by the coordinate calculation of (2) so thatnThe value of (a) is added to 1, and the formula is specifically as follows:
wherein (A), (B), (C), (D), (C), (B), (C)x,y) Coordinates of the data missing point; (x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a);
representing points of trackP n And track pointP n+1 The time interval between two adjacent points;t m is a preset time;
s403: repeating step S401 and step S402T-1 time, calculating said trajectory dataP K All of the data missing points in (1).
Further, when judging whether the track points exist in the space positioning drift points, considering that the distance of a running road network between two adjacent track points on one track should be close to the average distance of running on the road where the track points are located in the same sampling intervalSThus, when the distance between two tracing points is greater thanSIn time, the two trace points can be considered to have abnormal drift points; average distanceSThe calculation formula of (2) is as follows:
wherein the content of the first and second substances,
representing a road preset average speed;t m is a preset time.
In this embodiment, different speed thresholds are set for different road grades, and the urban expressway is used
Set to 80km/h, of the trunk
Set to 60km/h, secondary trunk
Set at 50km/h, branched
Set to 40 km/h;
in step S4, the step of extracting the spatial localization drift point in the outlier specifically includes:
s411: from the trajectory dataP K Middle pass sampling timet n Sequentially taking out three adjacent track pointsP n 、P n+1 AndP n+2 ，nis from 1 toT-A positive integer of 2 is a positive integer of,nis 1;
s412: judging the track pointP n 、P n+1 AndP n+2 the distance between and the average distanceSThe magnitude relationship of (1);
if it isP n AndP n+1 is less than or equal toSAnd is andP n+1 andP n+2 is greater thanSThen point of trackP n+2 For spatially locating drift points and track pointsP n+2 The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is less than or equal to S, the track points areP n For spatially locating drift points and track pointsP n The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is greater than S, the track points areP n+1 For spatially locating drift points and track pointsP n+1 The coordinates of (a) are:
wherein (A), (B), (C), (D), (C), (B), (C)x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a); (x n+2 ,y n+2 ) Is a track pointP n+2 The coordinates of (a);
s413: repeating the steps S411 and S412 for T-2 times, and calculating the track dataP K All of the spatially located drift points.
Referring to fig. 2, further, step S6 specifically includes:
when the grid is assembledRWhen a grid in (2) is marked once, the gridCNT j Adding 1 to the value of (c); when the grid is inCNT j Value greater than thresholdHThen, outputting the grid as an urban canyon region; in this embodiment, the threshold value is setHIs set to 50, the urban canyon region in wuhan city is finally obtained.
Referring to fig. 3, an urban canyon rapid detection device based on floating car track data includes the following modules:
a track data acquisition module 10, configured to select a sampling area, acquire track data of multiple vehicles in the sampling area, and obtain a track data setP；
A grid set generating module 20, configured to establish a buffer area for the urban road in the sampling area, and generate a grid set corresponding to the urban roadR；
A trajectory data extraction module 30 for extracting the trajectory data setPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; entering a superposition analysis module;
an overlay analysis module 40 for extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setREntering a grid intersected with the abnormal point into an updating track data set module;
an update track data set module 50 for generating a track data set from the track data setPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, entering an urban canyon region judgment module; if not, returning to the track data extraction module;
an urban canyon region determination module 60 for counting the grid setRThe number of times each grid is marked is made to exceed a threshold valueHThe grid output of (a) is an urban canyon region.
Other embodiments or specific implementation manners of the target area detection apparatus according to the present invention may refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third and the like do not denote any order, but rather the words first, second and the like may be interpreted as indicating any order.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (5)
1. A floating vehicle track data-based urban canyon rapid detection method is characterized by comprising the following steps:
s1: selecting a sampling area, and carrying out track data acquisition on a plurality of vehicles in the sampling area to obtain a track data setP；
S2: establishing a buffer area for the urban road in the sampling area, and generating a grid set corresponding to the urban roadR；
S3: extracting the set of trajectory dataPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; proceeding to step S4;
the trackData ofP K The concrete expression is as follows:
wherein the content of the first and second substances,Tthe maximum sampling times of the track;Knumbering the vehicles;p i is the first in the trackiTracing points;t i is shown asiSampling time of each trace point;v i indicating that the vehicle ist i The speed of travel at that moment;x i to representt i Longitude of the time track point;y i to representt i Latitude of the moment track point;
s4: extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setRThe grid intersecting the abnormal point is entered to step S5;
the step of extracting the space positioning drift point in the abnormal points specifically comprises the following steps:
s411: from the trajectory dataP K Middle pass sampling timet n Sequentially taking out three adjacent track pointsP n 、P n+1 AndP n+2 ，nis from 1 toT-A positive integer of 2 is a positive integer of,nis 1;
s412: judging the track pointP n 、P n+1 AndP n+2 the distance between and the average distanceSThe magnitude relationship of (1);
if it isP n AndP n+1 is less than or equal toSAnd is andP n+1 andP n+2 is greater thanSThen point of trackP n+2 For positioning in spaceDrift points, track pointsP n+2 The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is less than or equal to S, the track points areP n For spatially locating drift points and track pointsP n The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is greater than S, the track points areP n+1 For spatially locating drift points and track pointsP n+1 The coordinates of (a) are:
wherein (A), (B), (C), (D), (C), (B), (C)x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a); (x n+2 ,y n+2 ) Is a track pointP n+2 The coordinates of (a);
s413: repeating the steps S411 and S412 for T-2 times, and calculating the track dataP K All of the spatially located drift points;
s5: from the set of trajectory dataPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, go to step S6; if not, returning to the step S3;
s6: counting the grid setRThe number of times each grid is marked is made to exceed a threshold valueHThe grid output of (a) is an urban canyon region.
2. The floating vehicle trajectory data-based urban canyon rapid detection method according to claim 1, wherein in step S2, said grid assemblyREach grid in (a) is specifically represented as:
wherein the content of the first and second substances,ja number representing a grid;CNT j representing the number of outliers in the grid;xl j represents the longitude of the lower left corner of the grid;yl j representing the latitude of the lower left corner of the grid;xR j represents the longitude of the upper right corner of the grid;yR j representing the latitude of the upper right corner of the grid;ha number representing a set of grids.
3. The floating vehicle trajectory data-based urban canyon rapid detection method according to claim 1, wherein in step S4, the step of extracting the data missing point in the outlier specifically comprises:
s401: from the trajectory dataP K Middle pass sampling timet n Two adjacent track points are taken out in sequenceP n AndP n+1 ，nis from 1 toT-A positive integer of 1, or a mixture thereof,nis 1; if the track pointP n And track pointP n+1 The sampling time interval is greater than the preset timet m If the data missing point exists, the data missing point and the track point existP n And track pointP n+1 To (c) to (d);
s402: by means of tracing pointsP n And track pointP n+1 Obtaining the coordinates of the data missing point by the coordinate calculation of (2) so thatnThe value of (a) is added to 1, and the formula is specifically as follows:
wherein (A), (B), (C), (D), (C), (B), (C)x,y) Coordinates of the data missing point; (x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Is a track pointP n+1 The coordinates of (a);
representing points of trackP n And track pointP n+1 The time interval between two adjacent points;t m is a preset time;
s403: repeating step S401 and step S402T-1 time, calculating said trajectory dataP K All of the data missing points in (1).
4. The floating vehicle trajectory data-based urban canyon rapid detection method according to claim 2, wherein step S6 specifically comprises:
when the grid is assembledRWhen a grid in (2) is marked once, the gridCNT j Adding 1 to the value of (c); when the grid is inCNT j Value greater than thresholdHThe grid is then output as an urban canyon region.
5. The utility model provides a quick detection device of urban canyon based on floating car orbit data which characterized in that includes following module:
a track data acquisition module for selecting a sampling region and comparing the selected sampling region with a corresponding placeAcquiring track data of a plurality of vehicles in the sampling area to obtain a track data setP；
A grid set generating module for establishing a buffer area for the urban road in the sampling area and generating a grid set corresponding to the urban roadR；
A track data extraction module for extracting the track data setPTrack data of a certain vehicleP K ，KIs a positive integer and has an initial value of 1; entering a superposition analysis module;
the trajectory dataP K The concrete expression is as follows:
wherein the content of the first and second substances,Tthe maximum sampling times of the track;Knumbering the vehicles;p i is the first in the trackiTracing points;t i is shown asiSampling time of each trace point;v i indicating that the vehicle ist i The speed of travel at that moment;x i to representt i Longitude of the time track point;y i to representt i Latitude of the moment track point;
a superposition analysis module for extracting the trajectory dataP K The outlier of (1), the outlier comprising: data missing points and space positioning drift points; aggregating the gridsRPerforming superposition analysis with the abnormal points and marking the grid setREntering a grid intersected with the abnormal point into an updating track data set module;
the step of extracting the space positioning drift point in the abnormal points specifically comprises the following steps:
s411: from the trajectory dataP K Middle pass sampling timet n Sequentially taking out three adjacent track pointsP n 、P n+1 AndP n+2 ，nis from 1 toT-A positive integer of 2 is a positive integer of,nis 1;
s412: judging the track pointP n 、P n+1 AndP n+2 the distance between and the average distanceSThe magnitude relationship of (1);
if it isP n AndP n+1 is less than or equal toSAnd is andP n+1 andP n+2 is greater thanSThen point of trackP n+2 For spatially locating drift points and track pointsP n+2 The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is less than or equal to S, the track points areP n For spatially locating drift points and track pointsP n The coordinates of (a) are:
if it isP n AndP n+1 is greater thanSAnd is andP n+1 andP n+2 if the distance between the two points is greater than S, the track points areP n+1 For spatially locating drift points and track pointsP n+1 The coordinates of (a) are:
wherein (A), (B), (C), (D), (C), (B), (C)x n ,y n ) Is a track pointP n The coordinates of (a); (x n+1 ,y n+1 ) Tracing pointP n+1 The coordinates of (a); (x n+2 ,y n+2 ) Is a track pointP n+2 The coordinates of (a);
s413: repeating the steps S411 and S412 for T-2 times, and calculating the track dataP K All of the spatially located drift points;
an update track data set module for generating a track data set from the track data setPRemoving the trajectory dataP K To makeKAdding 1 to the value of (c); determining the trajectory data setPWhether it is empty; if yes, entering an urban canyon region judgment module; if not, returning to the track data extraction module;
the urban canyon region judgment module is used for counting the grid setRAnd outputting the grids with the marked times exceeding a threshold value as the urban canyon region.
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