CN105575120B - The floating car data parking behavior pattern cleaning method calculated towards road real-time speed - Google Patents
The floating car data parking behavior pattern cleaning method calculated towards road real-time speed Download PDFInfo
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- G08G—TRAFFIC CONTROL SYSTEMS
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- 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
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Abstract
A kind of floating car data parking behavior pattern cleaning method calculated towards road real-time speed, comprises the following steps：Step (1) reads map datum, and map datum includes section numbering, road point position information, map grid is obtained into " numbering in grid-section " mapping table；Step (2) reads the floating car data of setting time piece before current time according to " license plate number ", " creation time " ascending order；Step (3) calculates distance and the time difference of the double record of vehicle, then by distance and time difference calculating speed, and in section numbering where the speed to be added to the vehicle point position information of second of record according to " grid-section numbering " mapping table；Step (4) obtains the speed list of each vehicle on each section of the timeslice according to above step；Step (5) is recognized and rejected because what " pseudo- stopping behavior " was caused abends a little in speed list, and return speed list.The present invention can effectively recognize Floating Car parking behavior.
Description
Technical field
The present invention relates in data mining technical field, and in particular to a kind of floating calculated towards road real-time speed
Car data parking behavior pattern cleaning method.
Background technology
Road speeds are the premises of macroscopical, the microcosmic applications such as point duty, navigation, induced travel, congestion improvement, are important
Basic data.Therefore obtain that to be closer to real data most important.
Floating car data is calculated in Real-time Road passage rate and obtained as one of important component of traffic data
It is widely applied.The behavior generation it can be found that Floating Car abends during experimental study is carried out to existing floating car data
Data are applied to that during Real-time Road passage rate is calculated the accuracy of its speed can be had a strong impact on.Due to floating car data amount
Greatly, and abend behavior individual behaviour and it is irregular follow, artificial is difficult timely to find and reject.
The content of the invention
In order to overcome the shortcomings of can not effectively recognize parking behavior, the present invention provides a kind of effectively identification Floating Car and stopped
The floating car data parking behavior pattern cleaning method calculated towards road real-time speed that garage is.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of floating car data parking behavior pattern cleaning method calculated towards road real-time speed, the cleaning method
Comprise the following steps：
Step (1) reads map datum, and map datum includes section numbering (LDID), road point position information, by map net
Format and obtain " grid-section numbering " mapping table；
Step (2) reads the Floating Car number of setting time piece before current time according to " license plate number ", " creation time " ascending order
According to floating car data includes number-plate number CPHM, point position information GPS_FDC and creation time CJSJ；
Step (3) calculates distance and the time difference of the double record of vehicle, then calculates speed by distance and time difference
Degree, and the road where the speed to be added to the vehicle point position information of second of record according to " grid-section numbering " mapping table
In segment number；
Step (4) obtains the speed list of each vehicle on each section of the timeslice according to above step；
Step (5) is recognized and rejected because what " pseudo- stopping behavior " was caused abends a little in speed list, and returns to speed
List is spent, process is as follows：
5-1. searching loops in the data structure LDID_SpeedMap that step (4) is obtained obtain CPHM in LDIDkSpeed
Spend list speedListj, searching loop speedListjIn all speed viIf, vi=0 and vi+s≠ 0,0<s<=n-i,
S is the number of continuous halt, then the car speed record number δ=s+1 of " pseudo- stopping behavior " process；
5-2. calculates " pseudo- stopping behavior " car speed mutation threshold value SPSSC according to formula (1)；The multiplication sign left side as i ≠ 1
For the average speed of all speed records before first halt of the speed list, as i=1, with the section last moment
Average speed vtIt is used as average speed threshold value；
If 5-3. vi+s＞=SPSSC, then the speed record is the mutating speed that " pseudo- stopping behavior " is caused, from speed row
The speed record and mutating speed speed record of continuous halt are rejected in table；
5-4. completes " pseudo- stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
Further, the cleaning method also comprises the following steps：
Step (6) is recognized and rejected because what " really stopping behavior " caused abends a little in speed list, and returns to speed
List is spent, process is as follows：
Searching loop obtains CPHM in LDID to 6-1. from new LDID_SpeedMap after step (5)kSpeed row
Table speedListj, searching loop speedListjIn all speed viIf, vi=0 and vi+s≠ 0,0<s<=n-i, s are
The number of continuous halt；If s>=3 are thought that the car occurs in that " stopping behavior " skips to 6-2 and judge whether to belong to and " stopped at crossing
Only behavior "；
6-2. obtains the v of the stopping behavioriCorresponding LDID and GPS_FDCi, intersectionMap is traveled through, if
Then illustrate that the LDID is crossing section in the presence of the LDID in intersectionMap, enter 6-3 if being not present；From
The corresponding crossing points (GPS_QD, GPS_ZD) of the LDID are obtained in intersectionMap, GPS_ is calculated by formula (2)
FDCiWith the distance at crossing and taking its minimum value L, if L<L_min, then think that " the stopping behavior " belongs to " crossing stopping behavior "
Without rejecting, otherwise judge whether to belong to " collective stops behavior " into 6-3；
L=min (abs (GPS_FDCi-GPS_QD),abs(GPS_FDCi-GPS_ZD)) (2)
6-3. obtains the v of the stopping behavioriCorresponding CJSJiAnd GPS_FDCi, other vehicles under the searching loop LDID
Speed list, if there is the GPS_FDC that " stopping behavior " obtains its correspondence halti'And CJSJi', according to formula (3), formula
(4) Rule of judgment is calculated,
abs(GPS_FDCi-GPS_FDCi') ＜ Δs s_min, i ≠ i'(3)
abs(CJSJi-CJSJi') ＜ Δs t_min, i ≠ i'(4)
Think that the stopping behavior belonging to " collective stops behavior " no if other vehicles under the LDID all meet above Rule of judgment
Rejected, the otherwise stopping behavior belonging to " really stopping behavior ", and reject from speed list the speed note of continuous halt
Record；
6-4. completes " really stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
Further, the cleaning method also comprises the following steps：
Step (7) calculates the average speed of the corresponding all speed records of LDID after above step and is stored in data
In the real-time speed table of storehouse, renewal time piece repeat step (2) to step (6).
Further, in the step (3), velocity computation process is as follows：
3-1. obtains all records of each vehicle under the timeslice according to step (2), is designated as { CPHMk}_{GPS_
FDCi,CJSJi, k ∈ K, i ∈ n, n are the number of all records, and K is the sum of the timeslice vehicle；
3-2. calculates car speed v according to formula (5) or (6)i, obtain car speed list { CPHMk}_{vi,GPS_
FDCi,CJSJi}；
The absolute distance of the GPS point of double record is used, as the operating range of vehicle,
1) formula (5) is being used not in the case of section
2) formula (6) is being used in the case of section
GPS_JDiFor the GPS point of double record, when across section, the point position information of the intersection point in two sections.
In the step (7), the speed record of all car speed lists under LDID is obtained simultaneously according to LDID_SpeedMap
The corresponding average speeds of LDID are calculated according to formula (7), and is stored in database Real-time Road speedometer and is used for other application；
K is the sum of the timeslice vehicle, nkFor the speed record number of kth vehicle.
The present invention technical concept be：According to real-time floating car data, calculate floating vehicle travelling speed and be added to
In corresponding section, obtain the speed list of the vehicle of each in section and recognize abend behavior and rejecting abnormalities speed.
The Floating Car refers to taxi and bus in urban public transport.Floating Car meeting in vehicle travel process
(generally 30 seconds) upload a data at regular intervals, and data include：Floating Car license plate number, Floating Car positional information
(GPS), data creation time etc.；" stopping behavior " includes " normally stopping behavior " and " abend behavior ".It is " normal to stop row
For " include：
" crossing stopping behavior "：Vehicle is parked in crossing and (is waiting red light to belong to normal in traffic process in most cases
Phenomenon)
" collective stops behavior "：The behavior for referring to all Floating Cars in the same place stopping of same time (is running into congestion
In the case of the normal phenomenon fallen within traffic process occurs).
" abend behavior " includes：
" really stopping behavior "：(individual vehicle is parked in a point for a long time, belongs to individual in traffic process for vehicle abnormality stopping
Other behavior is anomaly)
" pseudo- stopping behavior "：Data upload the abnormal stopping caused, show as the point that speed is 0 continuously occur, and at this
Velocity jump after point.(as shown in Figure 1).
From data itself the characteristics of distinguish " really stopping behavior ", " pseudo- stopping behavior "." really stopping behavior " includes " really stopping
Only _ individual behavior " and " true stopping _ group behavior ".This programme identification is " true stopping _ individual behavior ", of this stopping
Body behavior is disturbing factor when calculating road speeds, refuses to be included in sample data when calculating road real-time speed.For this
" pseudo- stopping behavior " that patent is illustrated, is primarily referred to as those because of equipment fault, transmission network failure or database Write fault
The data sample of stopping behavior being appeared as Deng caused by.Although these stopping behaviors showing as stopping on data mode,
Data produce bigger with possibility caused by transmission link mistake, in other words, it is believed that it is " pseudo- stopping behavior ".Will be this
" pseudo- stopping behavior ", which is also excluded, to be calculated outside the sample data of road real-time speed.
Beneficial effects of the present invention are mainly manifested in：In big data quantity and irregular governed floating car data quickly simultaneously
And two kinds of stopping behaviors accurate in identification data and reject, while the real-time of data is ensured, significantly increase
The accuracy of real-time speed.
Brief description of the drawings
Fig. 1 is that " pseudo- stopping behavior " illustrates figure.
Fig. 2 is that the operating range of the vehicle in the case of section calculates schematic diagram.
Fig. 3 is the flow chart of the floating car data parking behavior pattern cleaning method calculated towards road real-time speed.
Fig. 4 is LDID_SpeedMap data structure diagrams.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.
1~Fig. 4 of reference picture, a kind of floating car data parking behavior pattern cleaning side calculated towards road real-time speed
Method, defines related symbol as follows：
vi：Car speed, unit km/h, i ∈ n, n are speed record of certain car on same LDID under single timeslice
Number.
vavg：Road average-speed, unit km/h.
δ：The car speed record number of " pseudo- stopping behavior " process (continuously for 0 number of times plus 1 speed dash forward by speed
Become).
SPSSC(Stop Point Speed Sudden Change):" pseudo- stopping behavior " car speed is mutated threshold value.
Δt_min：The speed record time difference threshold value (setting value 30 seconds) of same section different vehicle.
Δs_min：The speed record location interval threshold value (setting 50 meters) of same section different vehicle
L_min：Judge whether vehicle is parked in the distance threshold (setting 50 meters) at crossing.
The floating car data parking behavior pattern cleaning method comprises the following steps：
Step (1) map datums are pre-processed：
1-1. reads map datum, and map datum includes section numbering (LDID), starting point point position information (GPS_QD), terminal
Point position information (GPS_ZD), midpoint point position information (GPS_MD), " net is obtained by map grid (100-300 meters of side length of element)
Lattice-section numbering " mapping table (as shown in table 1).
Table 1
1-2. is because the connectivity in section, and terminal is connected (being same point) with starting point between section 1 and section 2, unites
The beginning and end in all sections in map datum is counted, " point-section numbering " mapping table (as shown in table 2) is obtained.
Table 2
The i.e. LDID numbers of point that 1-3. finds fork in the road or crossroad according to " numbering in point-section " mapping table are more than
Point equal to 3, forms new " point-section numbering " mapping table and is referred to as " crossing point mapping table ", " crossing point mapping table " is carried out
Upset forms " section numbering-point " mapping table (as shown in table 3, note：Recorded in Value be the section belong to fork in the road or
The point position information of person crossroad, the Value if the beginning and end in certain section belongs to fork in the road or crossroad point
Middle the starting point point position information for recording the section and terminal point position information, if only wherein some point is fork in the road or cross
Crossing point, then only record the point position information of the point) it is referred to as " crossing section mapping table " note intersectionMap.
Table 3
Step (2) reads current time toward previous timeslice (5min) according to " number-plate number ", " creation time " ascending order
Floating car data, floating car data include the number-plate number (CPHM), point position information (GPS_FDC), creation time (CJSJ).
Step (3) calculates road speeds：
3-1. obtains all records of each vehicle under the timeslice according to step (2), is designated as { CPHMk}_{GPS_
FDCi,CJSJi, k ∈ Ki ∈ n, n are the number of all records, and K is the sum of the timeslice vehicle.
3-2. calculates car speed v according to formula (8) or (9)i, obtain car speed list { CPHMk}_{vi,GPS_
FDCi,CJSJi}。
The absolute distance of the GPS point of double record is used, the operating range of vehicle is used as.
1) formula (8) is being used not in the case of section
2) formula (9) is being used in the case of section
GPS_JDiFor the GPS point of double record, when across section, the point position information of the intersection point in two sections.
GPS_FDCs of step (4) in speed listiMatching " grid-section numbering " mapping table, obtains GPS_
FDCiAll LDID of affiliated grid, calculate GPS_FDCiDistance is calculated with each LDID GPS_MD, the LDID for taking distance minimum
It is added to as the section of the Point matching, and by the speed list in LDID, obtains data structure LDID_SpeedMap (such as Fig. 3
It is shown).Step (5) is recognized and rejected " pseudo- stopping behavior "：
5-1. searching loops in data structure LDID_SpeedMap obtain CPHM in LDIDkSpeed list
speedListj, searching loop speedListjIn all speed viIf, vi=0 (i.e. halt) and vi+s≠0(0<s<=
N-i, s are the number of continuous halt), then the car speed of " pseudo- stopping behavior " process records number δ=s+1.
5-2. calculates " pseudo- stopping behavior " car speed mutation threshold value SPSSC according to formula (10).As i ≠ 1, multiplication sign is left
Side be first halt of the speed list before all speed records average speed, as i=1, with the section for the moment
The average speed v at quartertIt is used as average speed threshold value.
If 5-3. vi+s＞=SPSSC, then the speed record is the mutating speed that " pseudo- stopping behavior " is caused, from speed row
The speed record and mutating speed speed record of continuous halt are rejected in table.
5-4. completes " pseudo- stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
Citing：
The speed list of certain section car is obtained in data structure LDID_SpeedMap, in chronological order for
(35.52,27.89,0,0,131.07,32.12,9.1,33.17), unit is km/h.Now we can be found that and continuously stopped
Stop number is 2 then " pseudo- stopping behavior " SPSSC (velocity jump threshold value)=(35.52+27.89)/2* (2+1)=31.7*3
=95.1；95.1<131.07 are thought that the behavior is " pseudo- stopping behavior ".
Step (6) is recognized and rejected " really stopping behavior "
Citing：" really stopping behavior ", it is as shown in the table：Occur in that and stop in only one car of same timeslice in same section
Garage is.I.e. the behavior is " really stopping behavior ".
| Zhejiang CT1**0 | Speed | Zhejiang CT3**3 | Speed | Zhejiang CT3**7 | Speed | Zhejiang CT5**6 | Speed |
| 8:55:45 | 12.29 | 8:55:24 | 0 | 8:55:35 | 34.4 | 8:55:49 | 55.5 |
| 8:56:03 | 42.26 | 8:55:36 | 0 | 8:56:07 | 24.33 | 8:56:15 | 28.56 |
| 8:56:25 | 27.62 | 8:55:57 | 4.93 | 8:56:31 | 85.7 | 8:56:51 | 0 |
| 8:56:44 | 8.29 | 8:56:18 | 11.14 | 8:57:02 | 25.41 | 8:57:14 | 36.2 |
| 8:56:44 | 8.29 | 8:56:37 | 19.19 | 8:57:32 | 75.01 | 8:57:50 | 32.5 |
| 8:57:04 | 47.48 | 8:56:55 | 8 | 8:58:03 | 21.49 | 8:58:14 | 47.82 |
| 8:57:25 | 50.36 | 8:56:55 | 8 | 8:58:32 | 0 | 8:58:50 | 22.25 |
| 8:57:43 | 50.5 | 8:57:17 | 0 | 8:58:56 | 11.25 | 8:59:15 | 28.98 |
| 8:58:04 | 21 | 8:57:36 | 18.24 | 8:59:23 | 54.17 | 8:59:48 | 18 |
| 8:58:24 | 27 | 8:57:55 | 0 | 8:59:23 | 54.46 | ||
| 8:58:45 | 25.07 | 8:58:17 | 0 | ||||
| 8:59:04 | 22.03 | 8:58:37 | 2.7 | ||||
| 8:59:25 | 4.29 | 8:58:56 | 0 | ||||
| 8:59:45 | 19.35 | 8:59:18 | 12.27 |
Searching loop obtains CPHM in LDID to 6-1. from new LDID_SpeedMap after step (5)kSpeed row
Table speedListj, searching loop speedListjIn all speed viIf, vi=0 (i.e. halt) and vi+s≠0(0<s<
=n-i, s are the number of continuous halt)；If s>=3 are thought that the car occurs in that " stopping behavior " skips to 6-2 and judge whether category
In " crossing stopping behavior ".
6-2. obtains the v of the stopping behavioriCorresponding LDID and GPS_FDCi.IntersectionMap is traveled through, if
Then illustrate that the LDID is crossing section in the presence of the LDID in intersectionMap, enter 6-3 if being not present.From
The corresponding crossing points (GPS_QD, GPS_ZD) of the LDID are obtained in intersectionMap, GPS_ is calculated by formula (11)
FDCiWith the distance at crossing and taking its minimum value L, if L<L_min, then think that " the stopping behavior " belongs to " crossing stopping behavior "
Without rejecting, otherwise judge whether to belong to " collective stops behavior " into 6-3；
L=min (abs (GPS_FDCi-GPS_QD),abs(GPS_FDCi-GPS_ZD)) (11)
6-3. obtains the v of the stopping behavioriCorresponding CJSJiAnd GPS_FDCi, other vehicles under the searching loop LDID
Speed list, if there is the GPS_FDC that " stopping behavior " obtains its correspondence halti'And CJSJi', according to formula (12), formula
(13) Rule of judgment is calculated,
abs(GPS_FDCi-GPS_FDCi') ＜ Δs s_min, i ≠ i'(12)
abs(CJSJi-CJSJi') ＜ Δs t_min, i ≠ i'(13)
Think that the stopping behavior belonging to " collective stops behavior " no if other vehicles under the LDID all meet above Rule of judgment
Rejected, the otherwise stopping behavior belonging to " really stopping behavior ", and reject from speed list the speed note of continuous halt
Record.
6-4. completes " really stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
Step (7) has been handled after all data of the timeslice, is obtained under LDID and owned according to LDID_SpeedMap
The speed record of car speed list simultaneously calculates the corresponding average speeds of LDID, and be stored in the real-time road of database according to formula (14)
Used in the speedometer of road for other application；
K is the sum of the timeslice vehicle, nkFor the speed record number of kth vehicle.
Renewal time piece, repeat step (2) to step (6).
Claims (5)
1. a kind of floating car data parking behavior pattern cleaning method calculated towards road real-time speed, it is characterised in that：Institute
Cleaning method is stated to comprise the following steps：
Step (1) reads map datum, and map datum includes section numbering LDID, road point position information, map grid is obtained
To " grid-section numbering " mapping table；
Step (2) reads the floating car data of setting time piece before current time according to " license plate number ", " creation time " ascending order,
Floating car data includes number-plate number CPHM, point position information GPS_FDC and creation time CJSJ；
Step (3) calculates distance and the time difference of the double record of vehicle, then by distance and time difference calculating speed,
And the section where the speed to be added to the vehicle point position information of second of record according to " grid-section numbering " mapping table
In numbering；
Step (4) obtains the speed list of each vehicle on each section of the timeslice according to above step；
Step (5) is recognized and rejected because what " pseudo- stopping behavior " was caused abends a little in speed list, and return speed is arranged
Table, process is as follows：
5-1. searching loops in the data structure LDID_SpeedMap that step (4) is obtained obtain CPHM in LDIDkSpeed row
Table speedListj, k ∈ K, K are the sum of the timeslice vehicle, searching loop speedListjIn all speed viIf,
vi=0 and vi+s≠ 0,0<s<=n-i, s are the number of continuous halt, and n is the number of all records, then " pseudo- stopping behavior "
Car speed record number δ=s+1 of process；
5-2. calculates " pseudo- stopping behavior " car speed mutation threshold value SPSSC according to formula (1)；As i ≠ 1, the multiplication sign left side is should
The average speed of all speed records before first halt of speed list, as i=1, with putting down for the section last moment
Equal speed vtIt is used as average speed threshold value；
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If 5-3. vi+s＞=SPSSC, then the speed record is the mutating speed that " pseudo- stopping behavior " is caused, from speed list
Reject the speed record and mutating speed speed record of continuous halt；
5-4. completes " pseudo- stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
2. a kind of floating car data parking behavior pattern cleaning side calculated towards road real-time speed as claimed in claim 1
Method, it is characterised in that：The cleaning method also comprises the following steps：
Step (6) is recognized and rejected because what " really stopping behavior " caused abends a little in speed list, and return speed is arranged
Table, process is as follows：
Searching loop obtains CPHM in LDID to 6-1. from new LDID_SpeedMap after step (5)kSpeed list
speedListj, searching loop speedListjIn all speed viIf, vi=0 and vi+s≠ 0,0<s<=n-i, s are company
The number of continuous halt；If s>=3 are thought that the car occurs in that " stopping behavior " skips to 6-2 and judge whether to belong to " crossing stopping
Behavior "；
6-2. obtains the v of the stopping behavioriCorresponding LDID and GPS_FDCi, intersectionMap is traveled through, if
Then illustrate that the LDID is crossing section in the presence of the LDID in intersectionMap, enter 6-3 if being not present；From
The corresponding crossing points (GPS_QD, GPS_ZD) of the LDID are obtained in intersectionMap, GPS_ is calculated by formula (2)
FDCiWith the distance at crossing and taking its minimum value L, if L<L_min, then think that " the stopping behavior " belongs to " crossing stopping behavior "
Without rejecting, otherwise judge whether to belong to " collective stops behavior " into 6-3；
L=min (abs (GPS_FDCi-GPS_QD),abs(GPS_FDCi-GPS_ZD)) (2)
6-3. obtains the v of the stopping behavioriCorresponding CJSJiAnd GPS_FDCi, the speed of other vehicles under the searching loop LDID
List, if there is the GPS_FDC that " stopping behavior " obtains its correspondence halti'And CJSJi', counted according to formula (3), formula (4)
Calculate Rule of judgment,
abs(GPS_FDCi-GPS_FDCi') ＜ Δs s_min, i ≠ i'(3)
abs(CJSJi-CJSJi') ＜ Δs t_min, i ≠ i'(4)
Think that the stopping behavior belonging to " collective stops behavior " no if other vehicles under the LDID all meet above Rule of judgment
Rejected, the otherwise stopping behavior belonging to " really stopping behavior ", and reject from speed list the speed note of continuous halt
Record；
6-4. completes " really stopping behavior " identification and returns to a new LDID_SpeedMap after rejecting.
3. a kind of floating car data parking behavior pattern cleaning side calculated towards road real-time speed as claimed in claim 2
Method, it is characterised in that：The cleaning method also comprises the following steps：
Step (7) calculates the average speed of the corresponding all speed records of LDID after above step and is stored in database reality
When speedometer in, renewal time piece repeat step (2) to step (6).
4. a kind of floating car data parking behavior mould calculated towards road real-time speed as described in one of claims 1 to 3
Formula cleaning method, it is characterised in that：In the step (3), velocity computation process is as follows：
3-1. obtains all records of each vehicle under the timeslice according to step (2), is designated as { CPHMk}_{GPS_FDCi,
CJSJi, k ∈ K, i ∈ n, n are the number of all records, and K is the sum of the timeslice vehicle；
3-2. calculates car speed v according to formula (5) or (6)i, obtain car speed list { CPHMk}_{vi,GPS_FDCi,
CJSJi}；
The absolute distance of the GPS point of double record is used, as the operating range of vehicle,
1) formula (5) is being used not in the case of section
<mrow>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>FDC</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>FDC</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>CJSJ</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>CJSJ</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
2) formula (6) is being used in the case of section
<mrow>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>a</mi>
<mi>b</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>FDC</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>JD</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>a</mi>
<mi>b</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>JD</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>G</mi>
<mi>P</mi>
<mi>S</mi>
<mo>_</mo>
<msub>
<mi>FDC</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>CJSJ</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>CJSJ</mi>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
GPS_JDiFor the GPS point of double record, when across section, the point position information of the intersection point in two sections.
5. a kind of floating car data parking behavior pattern cleaning side calculated towards road real-time speed as claimed in claim 3
Method, it is characterised in that：In the step (7), the speed of all car speed lists under LDID is obtained according to LDID_SpeedMap
Record and the corresponding average speeds of LDID are simultaneously calculated according to formula (7), and be stored in database Real-time Road speedometer should for other
With using；
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>a</mi>
<mi>v</mi>
<mi>g</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>K</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>n</mi>
<mi>k</mi>
</msub>
</munderover>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</mrow>
<mrow>
<mi>K</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>n</mi>
<mi>k</mi>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
K is the sum of the timeslice vehicle, nkFor the speed record number of kth vehicle.
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| CN106548444B (en) * | 2016-11-03 | 2020-08-25 | 杭州电子科技大学 | Floating vehicle passenger behavior mode cleaning method for road real-time speed estimation |
| CN106875680A (en) * | 2017-03-21 | 2017-06-20 | 杭州电子科技大学 | Crossing average latency computational methods based on big data analysis |
| CN108922193B (en) * | 2018-08-03 | 2019-06-04 | 北京航空航天大学 | A kind of intersection signal phase estimate method based on Floating Car track data |
| CN110766937B (en) * | 2019-05-22 | 2020-10-20 | 滴图(北京)科技有限公司 | Parking spot identification method and device, electronic equipment and readable storage medium |
| CN114822016B (en) * | 2022-03-11 | 2023-07-25 | 上海工程技术大学 | Road section uniform speed estimation system based on GPS data flow |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001188986A (en) * | 1999-12-30 | 2001-07-10 | Hiroshi Tanaka | Method for measuring vehicle travel speed and traffic volume on road by aerial photography |
| JP4809321B2 (en) * | 2007-12-07 | 2011-11-09 | 住友電気工業株式会社 | Traffic information calculation device, traffic information calculation program, and traffic information calculation method |
| CN102982678A (en) * | 2012-11-28 | 2013-03-20 | 青岛海信网络科技股份有限公司 | Traffic data information service system and method for realizing traffic data information service |
| CN103927873A (en) * | 2014-04-28 | 2014-07-16 | 中国航天系统工程有限公司 | Matching method for probe car and road section and method for obtaining real-time traffic status in parallel |
| CN103971516A (en) * | 2014-05-13 | 2014-08-06 | 紫光捷通科技股份有限公司 | Traffic data preprocessing method and road condition detection method |
| CN104282151A (en) * | 2014-09-30 | 2015-01-14 | 北京交通发展研究中心 | Real-time floating vehicle path matching method based on high-frequency satellite positioning data |
| CN105139645A (en) * | 2015-07-23 | 2015-12-09 | 合肥革绿信息科技有限公司 | Urban regional road network operation index assessment method based on floating car technology |
-
2015
- 2015-12-31 CN CN201511031346.9A patent/CN105575120B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001188986A (en) * | 1999-12-30 | 2001-07-10 | Hiroshi Tanaka | Method for measuring vehicle travel speed and traffic volume on road by aerial photography |
| JP4809321B2 (en) * | 2007-12-07 | 2011-11-09 | 住友電気工業株式会社 | Traffic information calculation device, traffic information calculation program, and traffic information calculation method |
| CN102982678A (en) * | 2012-11-28 | 2013-03-20 | 青岛海信网络科技股份有限公司 | Traffic data information service system and method for realizing traffic data information service |
| CN103927873A (en) * | 2014-04-28 | 2014-07-16 | 中国航天系统工程有限公司 | Matching method for probe car and road section and method for obtaining real-time traffic status in parallel |
| CN103971516A (en) * | 2014-05-13 | 2014-08-06 | 紫光捷通科技股份有限公司 | Traffic data preprocessing method and road condition detection method |
| CN104282151A (en) * | 2014-09-30 | 2015-01-14 | 北京交通发展研究中心 | Real-time floating vehicle path matching method based on high-frequency satellite positioning data |
| CN105139645A (en) * | 2015-07-23 | 2015-12-09 | 合肥革绿信息科技有限公司 | Urban regional road network operation index assessment method based on floating car technology |
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