CN105355042B - A kind of road network extraction method based on taxi GPS - Google Patents
A kind of road network extraction method based on taxi GPS Download PDFInfo
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- CN105355042B CN105355042B CN201510698684.1A CN201510698684A CN105355042B CN 105355042 B CN105355042 B CN 105355042B CN 201510698684 A CN201510698684 A CN 201510698684A CN 105355042 B CN105355042 B CN 105355042B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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Abstract
The invention discloses a kind of road network extraction method based on taxi GPS, including 7 steps：Step S1 is to extract a workaday GPS data from taxi storehouse, and step S2 is to limit identification urban border, and step S3 is data cleansing, step S4 is the division of observation scope, step S5 is the differentiation of intersection, and step S6 is the section identification based on intersection, and step S7 is output road attribute list.The road network information that the present invention is extracted updates, more accurately, and arithmetic speed is fast, reduces human input, reduces error.
Description
Technical field
The present invention relates to road network extraction method, particularly a kind of road network extraction method based on taxi GPS.
Background technology
Urban Transportation Development in China is swift and violent, and road construction is maked rapid progress.Domestic still shortage is available for traffic planning designer or traffic
The authoritative road network data that researcher is used for traffic analysis is issued, therefore, and the present situation road network of domestic communication planning or research is past
Substantial amounts of manpower and materials are spent to carry out on the spot toward the road network information of the previous exploration provided using planning department, or planning team
Investigate to update and correct road network information.The road network information that the former obtains is seriously delayed；The latter then will also devote a tremendous amount of time,
The data deficiency of acquisition is ageing.
The content of the invention
Goal of the invention：It is an object of the invention to provide a kind of inexpensive, efficient road network based on taxi GPS
Extracting method.
Road network extraction method of the present invention based on taxi GPS, including the steps：
S1：Extract a workaday GPS data from taxi storehouse：Database include 8 row, i.e., car number, trigger event,
Operation state, gps time, GPS longitudes, GPS latitudes, GPS velocity and GPS directions；
S2：Limit identification urban border：Extract municipal administration region border westernmost end, most the east, northernmost and southernmost
Node coordinate, node coordinate include longitude and latitude；The longitude coordinate for setting westernmost end is the longitude minimum value of urban border
LONmin, set most the east longitude coordinate be urban border longitude maximum LONmax, set the latitude coordinate to be northernmost
The latitude maximum LAT of urban bordermax, set southernmost end latitude coordinate be urban border latitude minimum value LATmin, four
Individual boundary value encloses the region to be formed as observation scope；Reject and be unsatisfactory in the GPS data from taxi storehouse obtained in step S1
The data row of formula (1)；
LONmin<GPS longitudes<LONmax, and LATmin<GPS latitudes<LATmax (1)
S3：Data cleansing：By the GPS data from taxi storehouse after step S2 processing successively according to gps time, car number two
Individual variable ascending order arrangement so that the data row of same vehicle connects together and in chronological sequence arranged in the database newly formed；
Non-rice habitats data row and malposition data row are deleted, effective GPS data from taxi storehouse is obtained；Set global iterative variable
Iteration is 1；Wherein, it is the data row for deleting meeting formula (2) to delete non-rice habitats data row：
Rule of judgment 1：Operation state=2 or 3, and GPS velocity=0 (2)
Delete position abnormal data row is to delete Dx,x+1Meet (x+1)th row data during formula (3)：
Rule of judgment 2：Dx,x+1>Vmax·(Tx+1-Tx) (3)
Dx,x+1As shown in formula (4)：
Wherein, Dx,x+1To delete between two data points of adjacent lines in the database obtained after non-rice habitats data row
Distance, VmaxFor the maximum speed limit of urban road, r is the radius of the earth, Tx、φx、λxFor delete non-rice habitats data row it
Xth row gps time, GPS latitudes and GPS longitudes in the database obtained afterwards, Tx+1、φx+1、λx+1To delete non-rice habitats number
According to (x+1)th row gps time, GPS latitudes and the GPS longitudes in the database obtained after row；
S4：The division of observation scope：The step S2 observation scopes determined are cut into the rectangle plot of m rows n row, wherein m
Obtained with n by formula (5) with formula (6), the latitude lat of cut-boundarySide iWith longitude lonSide jObtained respectively by formula (7) with formula (8)：
M=INT ((LATmax-LATmin)/0.0027) (5)
N=INT ((LONmax-LONmin)/0.003528) (6)
latSide i=LATmin+(i-0.5*iteration)*(LATmax-LATmin)/m, (i=2,3,4 ..., m) (7)
lonSide j=LONmin+(j-0.5*iteration)*(LONmax-LONmin)/n, (j=2,3,4 ..., n) (8)
S5：The differentiation of intersection：Intersection identification is carried out to the rectangle plot obtained in step S4, will also be fallen at one
All latitude and longitude coordinates composition subdata base in effective GPS data from taxi storehouse in plot, to all subdata bases, according to warp
Degree and the span of latitude, are evenly dividing as 10 × 10 detection units, then judge each detection according to intersection Rule of judgment
With the presence or absence of intersection and the position of intersection in unit, the position record of the intersection of all identifications is detected in intersection
In list, assignment finally is carried out with differentiating to global iterative variable i teration；Wherein, intersection Rule of judgment is：Detection is single
The data row included in member is more than 40 rows, and the number of unduplicated " GPS orientation " value that these data rows are included is more than
40；
S6：Section identification based on intersection：The intersection detection list that step S5 is obtained is converted into intersection to be detected
Mouth point is to table, and each intersection point to be detected by originating intersection with terminating intersection to being constituted；It is to be detected to each group to intersect
Mouthful point pair, judge starting intersection and terminate intersection between whether there is road, if in the presence of, will starting intersection with terminate
The coordinate record of intersection deletes repetition erroneous judgement section in the detection list of section；
S7：Export road attribute list：The section that the intersection detection list and step S6 that output step S5 is obtained are obtained
Detect list.
Further, the position of the intersection in the step S5 includes intersection longitude and intersection latitude, intersection warp
Spend for the average value of the longitude of all data rows in detection unit, intersection latitude is the latitude of all data rows in detection unit
Average value.
Further, it is to global iterative to carry out assignment with differentiation to global iterative variable i teration in the step S5
Variable i teration adds 1 and assignment again, if judging that global iterative variable i teration is not equal to 3, returns to step S4,
If global iterative variable i teration is equal to 3, into step S6.
Further, the step S6 includes following sub-step：
S6.1：Intersection detection list is converted into intersection point to be detected to table：Detection row in intersection are calculated according to formula (4)
The distance between any intersection and remaining intersection in table, filter out intersection point of the distance less than 2km and composition are treated
Intersection point is detected to table, the point includes 5 variables to table, that is, originates intersection longitude lonstart, starting intersection latitude
latstart, terminate intersection longitude lonend, terminate intersection latitude latend, two intersections are apart from Ds-e；Wherein, according to formula (9)
To calculate, " s " represents starting intersection, and " e ", which is represented, terminates intersection：
S6.2：To each group of intersection point pair to be detected, judge to whether there is between starting intersection and termination intersection
Road：First, Road Detection region is limited, the region is respectively offset from starting intersection with terminating intersection coordinate line to both sides
20m two straight lines and respectively through originating intersection, the vertical line for this two straight lines for terminating intersection is enclosed and formed；Then will
Detection zone is divided into along intersection line directionIndividual rectangular sub blocks, if less than 20 Continuous Rectangular
Any effective GPS data from taxi storehouse latitude and longitude coordinates point is not contained in block, then shows there is road between two intersections, by this
5 variables of intersection point pair recorded in Road Detection list；
S6.3：Delete and repeat erroneous judgement section：Terminated in the Road Detection list obtained according to formula (10) calculation procedure S6.2
The angle, θ that intersection is formed with starting intersection line, wherein atan2 is that quadrant is included to the arctan function considered；
By Road Detection list successively according to angle, θ and starting intersection longitude lonstartSequence so that the number of identical starting intersection
Connect together and sorted according to angle value size according to row；The angle, θ of the adjacent data row of identical starting intersection is contrasted, if two
Person's difference is less than 40 °, then deletes relatively large distance Ds-eData row；Then by the angle, θ in all data rows between 270 ° with
Value between 360 ° replaces with θ -360 °, again by Road Detection list successively according to angle, θ and starting intersection longitude
lonstartSequence, contrasts the angle, θ of the adjacent data row of identical starting intersection, if difference is less than 40 °, delete compared with
Greatly apart from Ds-eData row；
θ=atan2 (latend-latstart,lonend-lonstart) (10)。
Beneficial effect：Compared with prior art, the present invention has following beneficial effect：
1) extract road network information update, it is more accurate：The present situation city CAD road networks that tradition is provided by planning department are extracted
The method of road information, relies heavily on the renewal time of planning department road network；Chinese most cities planning department
Road network updates often synchronous with urban planning, carries out once within several years, and the road network information extracted certainly will be caused delayed；And present invention side
Method can be extracted according to recent GPS data from taxi, as long as the road for having taxi to pass through can be identified, be greatly improved
The ageing and accuracy of road network information；
2) arithmetic speed is fast：Traditional road network extraction method, on the basis of delayed present situation city CAD, with reference to big
The manpower of amount is made an on-the-spot survey on the spot could obtain newest road network information, and whole process takes time and effort；Fact-finding process can continue several weeks
Even for more time；And the inventive method utilizes recent GPS data from taxi, it can extract detailed newest within several hours
Road network information, greatly save man-hour, improve the efficiency of traffic Correlative plan；
3) the automatic computing of computer, reduces human input, reduces error：The inventive method can be completely by computer
Computing is obtained, and has both saved the input of manpower survey, the human error for preventing manpower to participate in again and producing, and is also greatlyd save
The cost that data update.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
The schematic diagram that Fig. 2 divides for the observation scope of the present invention；
Fig. 3 is the schematic diagram in the Road Detection region of the present invention.
Embodiment
Technical scheme is further introduced with reference to embodiment.
A kind of road network extraction method based on taxi GPS that the present invention is provided, as shown in figure 1, including following
Step：
S1：Extract a workaday GPS data from taxi storehouse：Database include 8 row, i.e., car number, trigger event,
Operation state, gps time, GPS longitudes, GPS latitudes, GPS velocity and GPS directions, the extraction call format and implication of variable are such as
Shown in formula (1)：
S2：Limit identification urban border：Extract municipal administration region border westernmost end, most the east, northernmost and southernmost
Node coordinate, node coordinate include longitude and latitude；The longitude coordinate for setting westernmost end is the longitude minimum value of urban border
LONmin, set most the east longitude coordinate be urban border longitude maximum LONmax, set the latitude coordinate to be northernmost
The latitude maximum LAT of urban bordermax, set southernmost end latitude coordinate be urban border latitude minimum value LATmin, four
Individual boundary value encloses the region to be formed as observation scope；Reject and be unsatisfactory in the GPS data from taxi storehouse obtained in step S1
The data row of formula (2)；
LONmin<GPS longitudes<LONmax, and LATmin<GPS latitudes<LATmax (2)
S3：Data cleansing：By the GPS data from taxi storehouse after step S2 processing successively according to gps time, car number two
Individual variable ascending order arrangement so that the data row of same vehicle connects together and in chronological sequence arranged in the database newly formed；
Non-rice habitats data row and malposition data row are deleted, effective GPS data from taxi storehouse is obtained；Set global iterative variable
Iteration is 1；Wherein, it is the data row for deleting meeting formula (3) to delete non-rice habitats data row：
Rule of judgment 1：Operation state=2 or 3, and
GPS velocity=0 (3)
Delete position abnormal data row is to delete Dx,x+1Meet (x+1)th row data during formula (4)：
Rule of judgment 2：
Dx,x+1>Vmax·(Tx+1-Tx) (4)
Dx,x+1As shown in formula (5)：
Wherein, Dx,x+1To delete between two data points of adjacent lines in the database obtained after non-rice habitats data row
Distance, VmaxFor the maximum speed limit of urban road, r is the radius of the earth, Tx、φx、λxFor delete non-rice habitats data row it
Xth row gps time, GPS latitudes and GPS longitudes in the database obtained afterwards, Tx+1、φx+1、λx+1To delete non-rice habitats number
According to (x+1)th row gps time, GPS latitudes and the GPS longitudes in the database obtained after row；
S4：The division of observation scope：As shown in Fig. 2 the step S2 observation scopes determined to be cut into the rectangle of m rows n row
Plot, wherein m are obtained with n by formula (6) with formula (7), the latitude lat of cut-boundarySide iWith longitude lonSide jRespectively by formula (8) and formula
(9) obtain：
M=INT ((LATmax-LATmin)/0.0027) (6)
N=INT ((LONmax-LONmin)/0.003528) (7)
latSide i=LATmin+(i-0.5*iteration)*(LATmax-LATmin)/m, (i=2,3,4 ..., m) (8)
lonSide j=LONmin+(j-0.5*iteration)*(LONmax-LONmin)/n, (j=2,3,4 ..., n) (9)
S5：The differentiation of intersection：Intersection identification is carried out to the rectangle plot obtained in step S4, will also be fallen at one
All latitude and longitude coordinates composition subdata base in effective GPS data from taxi storehouse in plot, to all subdata bases, according to warp
Degree and the span of latitude, are evenly dividing as 10 × 10 detection units, then judge each detection according to intersection Rule of judgment
With the presence or absence of intersection and the position of intersection in unit, the position record of the intersection of all identifications is detected in intersection
In list, assignment finally is carried out with differentiating to global iterative variable i teration；Wherein, intersection Rule of judgment is：Detection is single
The data row included in member is more than 40 rows, and the number of unduplicated " GPS orientation " value that these data rows are included is more than
40；The position of intersection includes intersection longitude and intersection latitude, and intersection longitude is all data rows in detection unit
The average value of longitude, intersection latitude is the average value of the latitude of all data rows in detection unit；To global iterative variable
It is plus 1 to global iterative variable i teration and assignment again that iteration, which carries out assignment and differentiation, if the judgement overall situation changes
It is not equal to 3 for variable i teration, then returns to step S4, if global iterative variable i teration is equal to 3, into step
S6；
S6：Section identification based on intersection：The intersection detection list that step S5 is obtained is converted into intersection to be detected
Mouth point is to table, and each intersection point to be detected by originating intersection with terminating intersection to being constituted；It is to be detected to each group to intersect
Mouthful point pair, judge starting intersection and terminate intersection between whether there is road, if in the presence of, will starting intersection with terminate
The coordinate record of intersection deletes repetition erroneous judgement section in the detection list of section；
Step S6 includes following sub-step：
S6.1：Intersection detection list is converted into intersection point to be detected to table：Detection row in intersection are calculated according to formula (5)
The distance between any intersection and remaining intersection in table, filter out intersection point of the distance less than 2km and composition are treated
Intersection point is detected to table, the point includes 5 variables to table, that is, originates intersection longitude lonstart, starting intersection latitude
latstart, terminate intersection longitude lonend, terminate intersection latitude latend, two intersections are apart from Ds-e；Wherein, according to formula
(10) calculate, " s " represents starting intersection, " e ", which is represented, terminates intersection：
S6.2：To each group of intersection point pair to be detected, judge to whether there is between starting intersection and termination intersection
Road：First, limit Road Detection region, as shown in figure 3, the region from starting intersection with terminate intersection coordinate line to
Both sides respectively offset 20m two straight lines and enclosed respectively through the vertical line of starting intersection, this two straight lines for terminating intersection
Form；Then detection zone is divided into along intersection line directionIndividual rectangular sub blocks, if less than 20
Any effective GPS data from taxi storehouse latitude and longitude coordinates point is not contained in individual Continuous Rectangular sub-block, then is shown between two intersections
There is road, 5 variables of the intersection point pair recorded in Road Detection list；
S6.3：Delete and repeat erroneous judgement section：Terminated in the Road Detection list obtained according to formula (11) calculation procedure S6.2
The angle, θ that intersection is formed with starting intersection line, wherein atan2 is that quadrant is included to the arctan function considered；
By Road Detection list successively according to angle, θ and starting intersection longitude lonstartSequence so that the number of identical starting intersection
Connect together and sorted according to angle value size according to row；The angle, θ of the adjacent data row of identical starting intersection is contrasted, if two
Person's difference is less than 40 °, then deletes relatively large distance Ds-eData row；Then by the angle, θ in all data rows between 270 ° with
Value between 360 ° replaces with θ -360 °, again by Road Detection list successively according to angle, θ and starting intersection longitude
lonstartSequence, contrasts the angle, θ of the adjacent data row of identical starting intersection, if difference is less than 40 °, delete compared with
Greatly apart from Ds-eData row；
θ=atan2 (latend-latstart,lonend-lonstart) (11)。
S7：Export road attribute list：The section that the intersection detection list and step S6 that output step S5 is obtained are obtained
Detect list.
Claims (4)
1. a kind of road network extraction method based on taxi GPS, it is characterised in that：Including the steps：
S1：Extract a workaday GPS data from taxi storehouse：Database includes 8 row, i.e. car number, trigger event, operation
State, gps time, GPS longitudes, GPS latitudes, GPS velocity and GPS directions；
S2：Limit identification urban border：Extract municipal administration region border westernmost end, most the east, section northernmost with southernmost end
Point coordinates, node coordinate includes longitude and latitude；The longitude coordinate for setting westernmost end is the longitude minimum value of urban border
LONmin, set most the east longitude coordinate be urban border longitude maximum LONmax, set the latitude coordinate to be northernmost
The latitude maximum LAT of urban bordermax, set southernmost end latitude coordinate be urban border latitude minimum value LATmin, four
Individual boundary value encloses the region to be formed as observation scope；Reject and be unsatisfactory in the GPS data from taxi storehouse obtained in step S1
The data row of formula (1)；
LONmin<GPS longitudes<LONmax, and LATmin<GPS latitudes<LATmax (1)
S3：Data cleansing：By the GPS data from taxi storehouse after step S2 processing successively according to gps time, two changes of car number
Measure ascending order arrangement so that the data row of same vehicle connects together and in chronological sequence arranged in the database newly formed；Delete
Non-rice habitats data row and malposition data row, obtain effective GPS data from taxi storehouse；Set global iterative variable
Iteration is 1；Wherein, it is the data row for deleting meeting formula (2) to delete non-rice habitats data row：
Rule of judgment 1：Operation state=2 or 3, and GPS velocity=0 (2)
In formula (2), " operation state=2 " represent " parking ", and " operation state=3 " represent " stoppage in transit "；Delete position abnormal data
Row is to delete Dx,x+1Meet (x+1)th row data during formula (3)：
Rule of judgment 2：Dx,x+1＞ Vmax·(Tx+1-Tx) (3)
Dx,x+1As shown in formula (4)：
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Wherein, Dx,x+1For delete between two data points of adjacent lines in the database obtained after non-rice habitats data row away from
From VmaxFor the maximum speed limit of urban road, r is the radius of the earth, Tx、φx、λxTo delete after non-rice habitats data row
To database in xth row gps time, GPS latitudes and GPS longitudes, Tx+1、φx+1、λx+1To delete non-rice habitats data row
(x+1)th row gps time, GPS latitudes and GPS longitudes in the database obtained afterwards；
S4：The division of observation scope：The step S2 observation scopes determined are cut into the rectangle plot of m rows n row, wherein m and n by
Formula (5) is obtained with formula (6), the latitude lat of cut-boundarySide iWith longitude lonSide jObtained respectively by formula (7) with formula (8)：
M=INT ((LATmax-LATmin)/0.0027) (5)
N=INT ((LONmax-LONmin)/0.003528) (6)
latSide i=LATmin+(i-0.5*iteration)*(LATmax-LATmin)/m, i=2,3,4 ..., m (7)
lonSide j=LONmin+(j-0.5*iteration)*(LONmax-LONmin)/n, j=2,3,4 ..., n (8)
S5：The differentiation of intersection：Intersection identification is carried out to the rectangle plot obtained in step S4, will also be fallen in a plot
In effective GPS data from taxi storehouse all latitude and longitude coordinates composition subdata base, to all subdata bases, according to longitude with
The span of latitude, is evenly dividing as 10 × 10 detection units, then judges each detection unit according to intersection Rule of judgment
In with the presence or absence of intersection and intersection position, the position record of the intersection of all identifications is detected into list in intersection
In, assignment finally is carried out with differentiating to global iterative variable i teration；Wherein, intersection Rule of judgment is：In detection unit
Comprising data row more than 40 rows, and the number of unduplicated " GPS orientation " value that these data rows are included is more than 40；
S6：Section identification based on intersection：The intersection detection list that step S5 is obtained is converted into intersection point to be detected
To table, each intersection point to be detected by originating intersection with terminating intersection to being constituted；To each group of intersection point to be detected
It is right, judge starting intersection and terminate intersection between whether there is road, if in the presence of, will starting intersection with termination intersect
The coordinate record of mouth deletes repetition erroneous judgement section in the detection list of section；
S7：Export road attribute list：The section detection that the intersection detection list and step S6 that output step S5 is obtained are obtained
List.
2. the road network extraction method according to claim 1 based on taxi GPS, it is characterised in that：The step
The position of intersection in S5 includes intersection longitude and intersection latitude, and intersection longitude is all data rows in detection unit
Longitude average value, intersection latitude be detection unit in all data rows latitude average value.
3. the road network extraction method according to claim 1 based on taxi GPS, it is characterised in that：The step
It is plus 1 to global iterative variable i teration and assign again with differentiation to carry out assignment to global iterative variable i teration in S5
Value, if judging that global iterative variable i teration is not equal to 3, returns to step S4, if global iterative variable
Iteration is equal to 3, then into step S6.
4. according to the road network extraction method according to claim 1 based on taxi GPS, it is characterised in that：It is described
Step S6 includes following sub-step：
S6.1：Intersection detection list is converted into intersection point to be detected to table：Calculated according to formula (4) in the detection list of intersection
The distance between any intersection and remaining intersection, filter out intersection point of the distance less than 2km to be detected to composition
Intersection point includes 5 variables to table, the point to table, that is, originates intersection longitude lonstart, starting intersection latitude
latstart, terminate intersection longitude lonend, terminate intersection latitude latend, two intersections are apart from Ds-e；Wherein, according to formula (9)
To calculate, " s " represents starting intersection, and " e ", which is represented, terminates intersection：
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</msub>
</mrow>
<mn>2</mn>
</mfrac>
<mo>)</mo>
</mrow>
</mrow>
</msqrt>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
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</mrow>
</mrow>
S6.2：To each group of intersection point pair to be detected, judge to whether there is road between starting intersection and termination intersection：
First, Road Detection region is limited, the region respectively offsets 20m's to both sides from starting intersection with terminating intersection coordinate line
Two straight lines and respectively through originating intersection, the vertical line for this two straight lines for terminating intersection is enclosed and formed；Then will detection
Region is divided into along intersection line directionIndividual rectangular sub blocks, if in less than 20 Continuous Rectangular sub-blocks
Any effective GPS data from taxi storehouse latitude and longitude coordinates point is not contained, then shows there is road between two intersections, this is intersected
Mouthful point to 5 variables recorded in Road Detection list；
S6.3：Delete and repeat erroneous judgement section：Terminate and intersect in the Road Detection list obtained according to formula (10) calculation procedure S6.2
The angle, θ that mouth is formed with starting intersection line, wherein atan2 is that quadrant is included to the arctan function considered；By road
Road detection list is successively according to angle, θ and starting intersection longitude lonstartSequence so that the data row of identical starting intersection
Connect together and sorted according to angle value size；The angle, θ of the adjacent data row of identical starting intersection is contrasted, if the two is poor
Little Yu not be 40 °, then delete relatively large distance Ds-eData row；Then by the angle, θ in all data rows between 270 ° with 360 ° it
Between value replace with θ -360 °, again by Road Detection list successively according to angle, θ with starting intersection longitude lonstartSequence,
The angle, θ of the adjacent data row of identical starting intersection is contrasted, if difference is less than 40 °, relatively large distance D is deleteds-e's
Data row；
θ=atan2 (latend-latstart,lonend-lonstart) (10)。
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