CN108253974A - Floating Car location data automatic adaptation cushion route matching system and method - Google Patents
Floating Car location data automatic adaptation cushion route matching system and method Download PDFInfo
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- CN108253974A CN108253974A CN201711473687.0A CN201711473687A CN108253974A CN 108253974 A CN108253974 A CN 108253974A CN 201711473687 A CN201711473687 A CN 201711473687A CN 108253974 A CN108253974 A CN 108253974A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
Abstract
The present invention discloses Floating Car location data automatic adaptation cushion route matching system, including：Location data acquiring unit；Electronic map；Traffic network database；Timesharing variable weight heavy-route link library, according to history Floating Car motion track and current current anchor point degree of rarefication dynamic setting link field range, road network is cut according to link field range, and goes out the path that every section reaches another indirectly connected road network in link field according to timesharing category of roads weighted calculation；And route matching unit.Route matching unit includes：Historical location data collection acquiring unit extracts the matched section road net data of multiple buffering ranges with the location data by location data acquiring unit real-time reception from Traffic network database, obtains historical location data collection；First matching unit samples the frequency according to current position determination data and historical location data collection is matched for the first time；Fitting unit based on timesharing variable weight heavy-route link library, is fitted correction to obtain final route matching result.
Description
Technical field
The invention belongs to intelligent transportation fields, are more particularly to suitble to floating when sample frequency is relatively low and range of drift is larger
Vehicle location data automatic adaptation cushion route matching system and its Floating Car location data automatic adaptation cushion route matching method.
Background technology
Taxi (Floating Car) in installation alignment system (for example, GPS positioning system or BEI-DOU position system etc., below
In illustrated by taking GPS positioning system as an example) etc. after mobile units, it can be achieved that the traveling floating data of vehicle is passed through channel radio
Letter mode is transmitted to Floating Car information centre in real time, which includes the letters such as vehicle location, time, direction and speed
Breath, is handled using the relevant computation model such as map match, path culculating and algorithm, makes Floating Car position data and city
Road associates over time and space, finally obtains Floating Car and passes through the Vehicle Speed of road and the row of road
The traffic congestion informations such as vehicle hourage, can wide range of services in each traffic department, traffic police department can be such as used as to grasp road real-time
Traffic conditions formulate the voucher for alleviating congestion measure or Urban Traffic Planning, town road construction offer data basis branch
Support.
Taxi is for a long time in outdoor operation, and the acquisition and transmission of data are easily by the factors shadow such as region, road conditions, weather
It rings, some such as loss of data, redundancy, mistake and other issues can be generated, in addition each urban taxi is by equipment, operation ring
The influences such as border, operational regime, the GPS data upload frequency, position deviation rate are different, how to solve in low frequency time sampling condition
Under, and it is a core problem to carry out map match in the case that position excursion range is larger.
Map match is essentially a kind of correction technique, and core content is with the electronics with network topology structure
Subject to map road network information, the GPS data received is modified by map-matching algorithm[2], the mistake of map match
In terms of poor source is broadly divided into following four：External mutation Error, GPS system internal error, electronic map error and coordinate turn
Change error[3], due to the influence of any of the above factor, the precision of map match can be damaged, therefore just need to find a kind of conjunction
Suitable map-matching method fights this trueness error, realizes precisely matching.
Map-matching algorithm can be simply divided into following several at present：Simple projected map matching algorithm, based on weight
Topological theory matching algorithm, the map-matching algorithm based on probability statistics principle and dummy differential algorithm.
1) directly sciagraphy only considers the shape in section, distance, by GPS position information, projects to distance in electronic map
Nearest section, if in more complicated and dense city road network, the matching efficiency of direct sciagraphy is low, stability and
Self-recision energy force difference.
2) the topology matching algorithm based on weight mainly by be with reference to previous moment vehicle position and travel direction, base
In electronic map road network topology structure, candidate road section weight is determined, calculated according to weight coefficient and with reference to history GPS track
Go out this moment final match point, weight coefficient is determined mainly by experience, and weight coefficient can directly affect map match
Precision.
3) principle and method of the map-matching algorithm based on the probability statistics constantly cycle based on probability statistics principle compare
The position of GPS point and candidate roads section position calculate the confidence region of current data point, by comparing, judge in error region
Candidate roads in domain select final matching section and match point, and this algorithm cannot efficiently use vehicle running history letter
Breath, can not also solve GPS point drifting problem.
4) dummy differential algorithm can carry out the estimation of correction amount under current time state using the correction amount of previous moment
And calculating, the position at current time can refer to the correction amount of last moment and obtain pre-matching estimation point, recycle simple geometry
Matching principle will obtain final true match point on pre-matching spot projection to matching road, and dummy differential algorithm can be certain
Wrong identification ratio is reduced in degree, but operation time can be significantly greatly increased, is had centainly to map match real-time and reliability
It influences.
Invention content
In view of above-mentioned problems of the prior art, emphasis solution of the present invention is answered in the city with network topology structure
In miscellaneous road network, Floating Car location data sampling the frequency it is relatively low, loss of data, redundancy and error rate are higher, position excursion range compared with
Under big physical condition, there are matching efficiency is relatively low, precision is not high and real-time and reliability for traditional map matching process
The problems such as not strong.
In order to solve the above-mentioned technical problem, Floating Car location data automatic adaptation cushion route matching system disclosed by the invention
System, which is characterized in that including：Location data acquiring unit, for the location data of real-time reception Floating Car client transmission；Electricity
Sub- map, the section road net data including topological structure, and for the matching result that shows paths；Traffic network database will float
The section road net data matching of the historical location data of vehicle and the topological structure preserves；Timesharing variable weight heavy-route link library, root
According to history Floating Car motion track and current anchor point degree of rarefication dynamic setting link field range, according to link field range by road network
It is cut, and goes out every section according to timesharing category of roads weighted calculation and another indirectly connected road network is reached in link field
Path；And route matching unit, it is described including historical location data collection acquiring unit, first matching unit and fitting unit
Historical location data collection acquiring unit is extracted from the Traffic network database and by the location data acquiring unit real-time reception
The matched section road net data of multiple buffering ranges of location data, and then obtain in the section road net data extracted
Historical location data collection, the first matching unit according to current position determination data sample the frequency to the historical location data collection into
The first matching of row, the fitting unit are based on timesharing variable weight heavy-route link library, and correction is fitted to first matched result
To obtain final route matching result.
In addition, the Floating Car location data automatic adaptation cushion route matching system, which is characterized in that the positioning number
According to the location data for being certain standard, which includes alignment system latitude and longitude coordinates, time, direction, speed, carrying shape
The attributes such as state；The location data acquiring unit carries out dissection process, and right to the location data received according to the standard
Abnormal data is cleaned.
In addition, the Floating Car location data automatic adaptation cushion route matching system, which is characterized in that in the road network
Each section data are the directive road sections of tool, which defined by two endpoints and with space coordinate
The point set of attribute and broken line set, and each road section includes an intersection or an entrance；The history is determined
Position data set acquiring unit is extracted and the positioning number by the location data acquiring unit real-time reception from the Traffic network database
According to multiple buffering ranges matched section road net data when, in the positioning number by the location data acquiring unit real-time reception
According to multiple buffering ranges in, intersecting section is found out from the Traffic network database, positive, reversed and two-way three in section
Kind situation carries out angle changing rate, and filter out the section not in angular deviation allowed band according to due north angle respectively, thus
Obtain the matched section road network number of multiple buffering ranges with the location data by the location data acquiring unit real-time reception
According to, and then obtain the historical location data collection in the section road net data extracted.
In addition, the Floating Car location data automatic adaptation cushion route matching system, which is characterized in that described first
When sampling the frequency according to current position determination data with unit the historical location data collection being matched for the first time, according to working as prelocalization
The data sampling frequency sets the time threshold of each calculating cycle automatically, when reaching time threshold trigger condition, according to buffering
Range is grouped the historical location data collection, and each history is concentrated to position number the historical location data of each grouping
According to section data be compared with the section data of preceding several cycle match, remove and the sections of preceding several cycle match do not have
There is the section of topological relation, and ensure that each historical location data only corresponds to a section, it is final to retain and preceding several periods
The one group of historical location data collection of the section quantity that matched section matches at most, obtains initial matching result.
In addition, the Floating Car location data automatic adaptation cushion route matching system, which is characterized in that the fitting is rectified
Just include：Fitting correction for the first time, solution range of drift is big, turn around the matching problems such as traveling, and will be under the conditions of simple road network
Section is effectively connected；Second of fitting correction, using positive sequence method, path connects, while school under the conditions of completing complicated road network
The positive error that fitting generates for the first time；Third time fitting correction, is corrected using inverted order method and repaired in the fit procedure of second of front
The error of generation completes route matching.
In order to solve the above-mentioned technical problem, Floating Car location data automatic adaptation cushion route matching side disclosed by the invention
Method, which is characterized in that including：Location data obtaining step, for the location data of real-time reception Floating Car client transmission；Point
Time-varying weight path link library construction step connects according to history Floating Car motion track and the dynamic setting of current anchor point degree of rarefication
Domain range is connect, is cut road network in Traffic network database according to link field range, and according to timesharing category of roads weighted calculation
Go out the path that every section reaches another indirectly connected road network in link field, the Traffic network database determines the history of Floating Car
The section road net data matching of position data and topological structure preserves；Route matching step is obtained including historical location data collection and is walked
Suddenly, first matching step and fit procedure, the historical location data collection obtaining step from the Traffic network database extract with by
The matched section road net data of multiple buffering ranges of the location data of the location data obtaining step real-time reception, and then
Historical location data collection onto the section road net data extracted, the first matching step is according to current position determination data
The sampling frequency matches the historical location data collection for the first time, and the fit procedure is connected based on timesharing variable weight heavy-route
Library carries out cubic fit correction to obtain final route matching result to first matched result；And path step display,
Show paths matching result on electronic map.
In addition, the Floating Car location data automatic adaptation cushion route matching method, which is characterized in that the positioning number
According to the location data for being certain standard, which includes alignment system latitude and longitude coordinates, time, direction, speed, carrying shape
The attributes such as state；The location data obtaining step carries out dissection process, and right to the location data received according to the standard
Abnormal data is cleaned.
In addition, the Floating Car location data automatic adaptation cushion route matching method, which is characterized in that in the road network
Each section data are the directive road sections of tool, which defined by two endpoints and with space coordinate
The point set of attribute and broken line set, and each road section includes an intersection or an entrance；The history is determined
Position data set obtaining step extracts and the positioning number by the location data obtaining step real-time reception from the Traffic network database
According to multiple buffering ranges matched section road net data when, in the positioning number by the location data obtaining step real-time reception
According to multiple buffering ranges in, intersecting section is found out from the Traffic network database, positive, reversed and two-way three in section
Kind situation carries out angle changing rate, and filter out the section not in angular deviation allowed band according to due north angle respectively, thus
Obtain the matched section road network number of multiple buffering ranges with the location data by the location data obtaining step real-time reception
According to, and then obtain the historical location data collection in the section road net data extracted.
In addition, the Floating Car location data automatic adaptation cushion route matching method, which is characterized in that described first
When sampling the frequency according to current position determination data with step the historical location data collection being matched for the first time, according to working as prelocalization
The data sampling frequency sets the time threshold of each calculating cycle automatically, when reaching time threshold trigger condition, according to buffering
Range is grouped the historical location data collection, and each history is concentrated to position number the historical location data of each grouping
According to section data be compared with the section data of preceding several cycle match, remove and the sections of preceding several cycle match do not have
There is the section of topological relation, and ensure that each historical location data only corresponds to a section, it is final to retain and preceding several periods
The one group of historical location data collection of the section quantity that matched section matches at most, obtains initial matching result.
In addition, the Floating Car location data automatic adaptation cushion route matching method, which is characterized in that the fitting is rectified
Just include：Fitting correction for the first time, solution range of drift is big, turn around the matching problems such as traveling, and will be under the conditions of simple road network
Section is effectively connected；Second of fitting correction, using positive sequence method, path connects, while school under the conditions of completing complicated road network
The positive error that fitting generates for the first time；Third time fitting correction, is corrected using inverted order method and repaired in the fit procedure of second of front
The error of generation completes route matching.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below to required in specific embodiment part
The attached drawing used does simple introduction, it is therefore apparent that the accompanying drawings in the following description is only some embodiments of the present invention, for this
For the those of ordinary skill of field, without creative efforts, others are can also be obtained according to these attached drawings
Attached drawing.
Fig. 1 is the Floating Car location data automatic adaptation cushion route matching system represented according to the embodiment of the present invention
Block diagram.
Fig. 2 is route matching in the Floating Car location data automatic adaptation cushion route matching system for represent the above embodiment
The block diagram of unit.
Fig. 3 represents the stream of Floating Car location data automatic adaptation cushion route matching method according to the embodiment of the present invention
Cheng Tu.
Fig. 4 is route matching in the Floating Car location data automatic adaptation cushion route matching method for represent the above embodiment
The flow chart of step.
Fig. 5 is to represent to obtain the exemplary plot with the matched section road net data of multiple buffering ranges.
Fig. 6 is GPS point set electron map distribution map in the low frequency of embodiment is 40 minutes secondary.
Fig. 7 represents situations such as GPS point drift value is larger in above-described embodiment, taxi turns around to travel.
Fig. 8 shows viaducts complicated in Fig. 6 in above-described embodiment.
Fig. 9 represents first matching result in above-described embodiment.
Figure 10 represents the partial enlarged view of first matching result in above-described embodiment, and left side and right side represent not go the same way respectively
The partial enlarged view of section.
Figure 11 represents first time fitting result in above-described embodiment.
Figure 12 represents to be fitted the error that insurmountable first matching generates in above-described embodiment for the first time.
Figure 13 represents to be fitted untreated section for the first time.
Figure 14 represents second of fitting result in above-described embodiment.
Figure 15 represents that complicated road network connection is completed in second of fitting, but can generate new error in above-described embodiment.
Figure 16 is to represent third time fitting result in above-described embodiment.
Figure 17 is that final matching results are added to the design sketch of road network.
Specific embodiment
In the following, illustrate the embodiment of the present invention in greater detail with reference to the attached drawings.The embodiment of the present invention can be deformed into various
Mode, the scope of the present invention should not be construed as limited to examples set forth below.
(embodiment)
Fig. 1 is the Floating Car location data automatic adaptation cushion route matching system represented according to the preferred embodiment of the present invention
The block diagram of system 100.
As shown in Figure 1, the Floating Car location data automatic adaptation cushion route matching system 100 of first embodiment includes determining
Position data capture unit 110, electronically Figure 150, Traffic network database 120, timesharing variable weight heavy-route link library 130 and path
With unit 140.
Wherein, location data acquiring unit 110 is used for the location data of real-time reception Floating Car client transmission.This implementation
The Floating Car location data automatic adaptation cushion route matching system 100 of mode is to the location data lattice of the input of progress route matching
Formula has carried out standard definition, which provides foundation for initial data cleaning.Location data standard includes alignment system longitude and latitude
Spend the attributes such as coordinate, time, direction, speed, passenger carrying status.Positioning of the location data acquiring unit 110 preferably also to being received
Data carry out dissection process, and abnormal data is cleaned, and valid data are transferred to next processing according to the standard
Unit.
Electronically Figure 150 includes the section road net data of topological structure, and for show paths matching result and floating
Vehicle motion track.
Traffic network database 120 protects the section road net data matching of the historical location data of Floating Car and the topological structure
The dictionary data as spatial match is deposited, can convert the section road net data in electronically Figure 150.The road network
Middle each section data are the directive road sections of tool, which is to be defined by two endpoints and sat with space
The point set and broken line set of attribute (for example, latitude and longitude coordinates) are marked, and each road section includes an intersection or one
A entrance.Each broken line can calculate a part of the due north azimuth as basic attribute data according to coordinate, and foundation is led
Chart draws characteristic, and there is the part that positive, reversed and two-way three kinds of characteristics also serve as basic data in each section.
Timesharing variable weight heavy-route link library 130 is the base library of route matching fitting, according to history Floating Car motion track
With current anchor point degree of rarefication dynamic setting link field range, road network is cut, and according to timesharing according to link field range
Category of roads weighted calculation goes out the path that every section reaches another indirectly connected road network in link field.
Fig. 2 is route matching in the Floating Car location data automatic adaptation cushion route matching system for represent the above embodiment
The block diagram of unit 140.
It is single that route matching unit 140 includes historical location data collection acquiring unit 141, first matching unit 142 and fitting
Member 143.
Historical location data collection acquiring unit 141 is extracted from the Traffic network database 120 with being obtained by the location data
The matched section road net data of multiple buffering ranges of the location data of 110 real-time reception of unit, and then obtain extracted institute
State the historical location data collection in the road net data of section.Wherein, the buffering range refer to using the location data of real-time reception as
The range of center certain distance radius, the multiple buffering range refer to multiple and different centered on the location data of real-time reception
The range of distance radius.At this point, historical location data collection acquiring unit 141 is preferably real-time by location data acquiring unit 110
In multiple buffering ranges of the location data of reception, intersecting section is found out from Traffic network database 120, forward direction in section,
Reversely and two-way three kinds of situations carry out angle changing rate, and filter out not in angular deviation allowed band according to due north angle respectively
Section, thus as shown in figure 5, obtaining and location data (such as the GPS numbers by 110 real-time reception of location data acquiring unit
According to) the matched section road net data of multiple buffering ranges, and then obtain the history in extracted section road net data positioning
Data set.
The first matching unit 142 samples the frequency according to current position determination data and the historical location data collection is carried out just
Secondary matching.At this point, the first matching unit 142 preferably samples the frequency according to current position determination data, setting each calculates week automatically
The time threshold of phase, when reaching time threshold trigger condition, (point set of each calculating cycle cannot be very little, otherwise can influence
With accuracy, such as table 1), start by closely becoming caching filtering to remote.That is, it is preferred that according to the distance centered on current position determination data
The sequence of the ascending buffering range of radius is grouped the historical location data collection, and by the history of each grouping
Location data concentrates the section data of each historical location data to be compared with the section data of preceding several cycle match, remove and
The section of preceding several cycle match does not have the section of topological relation, and ensures that each historical location data only corresponds to a road
Section, it is final to retain one group of most historical location data collection of the section quantity to match with the section of preceding several cycle match, it obtains
To initial matching result.Wherein, the historical location data collection is grouped can not be according to using current position determination data as
The sequence of the ascending buffering range of the distance radius at center, can be the descending sequence of the distance radius or
It is according to other rules or random sequence progress.
1 location data of table samples the frequency and route matching calculating cycle threshold value table
The fitting unit 143 is based on timesharing variable weight heavy-route link library, and correction is fitted to first matched result
To obtain final route matching result.The fitting correction preferably includes：Fitting correction for the first time, solution range of drift is big, falls
The matching problems such as head traveling, and the section under the conditions of simple road network is effectively connected, but the redundancy generated during first matching
Section will not be eliminated, at the same under the conditions of complicated road network such as comprising main and side road, ring road interchange overpass can there are errors of fitting；The
Quadratic fit is corrected, and using positive sequence method, path connects under the conditions of completing complicated road network, while corrects the mistake that fitting generates for the first time
Difference, second of fitting can equally generate new error；Third time fitting correction is intended for second using inverted order method correction reparation front
The error generated in the process is closed, completes route matching.
Fig. 3 represents the stream of Floating Car location data automatic adaptation cushion route matching method according to the embodiment of the present invention
Cheng Tu.Path matching step in the Floating Car location data automatic adaptation cushion route matching method of Fig. 4 expression the above embodiments
Flow chart.
In the following, illustrate the Floating Car location data automatic adaptation cushion route matching side of present embodiment with reference to Fig. 3 and Fig. 4
Method, this method include location data obtaining step S110, timesharing variable weight heavy-route link library construction step S120, route matching step
Rapid S130 and path step display S140.
In location data obtaining step S110, the location data of real-time reception Floating Car client transmission.Furthermore it is preferred that also
To the location data received, dissection process is carried out, and abnormal data is cleaned according to the standard, and by valid data
It is transferred to next processing unit.
In timesharing variable weight heavy-route link library construction step S120, according to history Floating Car motion track and current anchor point
Degree of rarefication dynamic setting link field range, cuts road network in Traffic network database, and according to timesharing according to link field range
Category of roads weighted calculation goes out the path that every section reaches another indirectly connected road network in link field.Wherein, the road network
In database, the section road net data matching of the historical location data of Floating Car and topological structure is preserved.
As shown in figure 4, route matching step S130 includes historical location data collection obtaining step S131, first matching step
S132 and fit procedure S133.
The historical location data collection obtaining step S131 is extracted from the Traffic network database with being obtained by the location data
The matched section road net data of multiple buffering ranges of the location data of unit real-time reception is taken, and then obtains being extracted described
Historical location data collection in the road net data of section.At this point, it is preferred that being extracted from the Traffic network database and by the location data
During the matched section road net data of multiple buffering ranges of the location data of obtaining step real-time reception, by the location data
In multiple buffering ranges of the location data of obtaining step real-time reception, intersecting section is found out from the Traffic network database,
Positive, reversed and two-way three kinds of situations in section carry out angle changing rate, and filter out not in angle according to due north angle respectively
Section in deviation allowed band, thus as shown in figure 5, obtaining and the positioning by the location data obtaining step real-time reception
The matched section road net data of multiple buffering ranges of data, and then obtain the history in the section road net data extracted
Location data collection.
The first matching step S132 samples the frequency according to current position determination data and the historical location data collection is carried out
First matching.At this point, it is preferred that according to current position determination data sample the frequency historical location data collection is matched for the first time when,
The time threshold of each calculating cycle according to the current position determination data sampling frequency is set automatically, is reaching time threshold trigger condition
When, the historical location data collection is grouped according to the buffering range, and by the historical location data of each grouping
The section data of each historical location data is concentrated to be compared with the section data of preceding several cycle match, are removed and preceding several weeks
Phase matched section does not have the section of topological relation, and ensures that each historical location data only corresponds to a section, finally
Retain one group of most historical location data collection of the section quantity to match with the section of preceding several cycle match, obtain initial
With result.
The fit procedure S133 is based on timesharing variable weight heavy-route link library, and cubic fit is carried out to first matched result
It corrects to obtain final route matching result.The fitting correction preferably includes：Fitting correction for the first time solves range of drift
Greatly, turn around the matching problems such as traveling, and the section under the conditions of simple road network is effectively connected；Second of fitting correction, is adopted
With positive sequence method, path connects under the conditions of completing complicated road network, while corrects the error that fitting generates for the first time；Third time, which is fitted, to be rectified
Just, the error repaired and generated in the fit procedure of second of front is corrected using inverted order method, completes route matching.
Finally, in path step display S140, show paths matching result on the electronic map.
By above-mentioned steps, the Floating Car location data automatic adaptation cushion route matching system of present embodiment completes floating
Motor-car is travelled on Various Complex road network road, such as includes main and side road, overpass, the tubaeform of ring road, diamond shape, alfalfa leaf type
Interchange viaduct, and the exact path under the physical conditions such as GPS point (anchor point) the sampling frequency is low, range of drift is big
Match, can accomplish real-time perception sampling, adjust calculating cycle automatically according to GPS point (anchor point) the sampling frequency and degree of rarefication, do
Taxi run trace is restored to low latency response computation and in electronic map.
<Embodiment>
Below with or so about 40 minutes duration of one group of taxi sample frequency relatively low (40~60 seconds/point) and drift
The calculating process of the above embodiment, this sample point are described for the GPS data of shifting range (peak excursion is close to 20 meters) bigger than normal
Distribution such as Fig. 6 in navigation picture, including range of drift big data point such as Fig. 7, complicated interchange viaduct such as Fig. 8.
First, the location data acquiring unit real-time reception of Floating Car location data automatic adaptation cushion route matching system arrives
The laggard rower quasi- neutralizing analysis of GPS point, then carries out road network, in the buffering range of the GPS point, according to the sky improved above
Between object intersection method calculate intersecting section, and be filtered using forward and reverse and due north angle deviation, obtain the GPS point
After section set in different buffering ranges, the GPS point gathered into the section is concentrated.
Later, according to the sampling frequency of current GPS adjustment calculating cycle time threshold, (this sample sampling frequency is 40 to system
~60 seconds/point, time threshold was automatically regulated to be 5 minutes), start after meeting time threshold trigger condition by being closely cached to remote change
Filter is grouped, and will be described in each grouping GPS point set according to buffering range (each GPS point only corresponds to a section)
History GPS point concentrates the section data of each history GPS point to be compared with the section data of preceding several cycle match, removes with before
The section of several cycle match does not have the section of topological relation, and ensures that each history GPS point only corresponds to a section, most
Retain one group of most history GPS point collection of the section quantity to match with the section of preceding several cycle match eventually, obtain initial
With as a result, such as Fig. 9, partial enlargement can see a large amount of parallel section, such as non-intersect section, Figure 10.
On the basis of first matching result, based on timesharing variable weight heavy-route link library, cubic fit correction is carried out.First
Secondary fitting can solve the matching problems such as range of drift is big, turns around and be attached simpied method network section, such as Figure 11, but first
The error section that timing generates is still remained such as Figure 12, at the same complicated road network such as main and side road, overhead route and interchange viaduct without
Method connects, such as Figure 13.Complicated road network connection such as Figure 14, and correct the error that fitting generates for the first time is completed in second of fitting, together
When can generate new error section such as Figure 15.Error is generated in third time fitting emphasis correction front fit procedure, as a result such as Figure 16,
Road network bandwagon effect such as Figure 17 is superimposed, this sample is completed since then and collects route matching and track displaying overall process from GPS point.
Industrial applicibility
The technical issues of technology disclosed in the present application is suitble to institute's positioning map in need of intelligent transportation field to match, and then
It is suitably applied in various alignment systems, navigation system, traffic administration monitoring system.
Claims (10)
1. a kind of Floating Car location data automatic adaptation cushion route matching system, which is characterized in that
Including：
Location data acquiring unit, for the location data of real-time reception Floating Car client transmission；
Electronic map, the section road net data including topological structure, and for the matching result that shows paths；
Traffic network database preserves the section road net data matching of the historical location data of Floating Car and the topological structure；
Timesharing variable weight heavy-route link library is connected according to history Floating Car motion track and the dynamic setting of current anchor point degree of rarefication
Domain range cuts road network according to link field range, and goes out every section even according to timesharing category of roads weighted calculation
Connect the path that another indirectly connected road network is reached in domain；And
Route matching unit, including historical location data collection acquiring unit, first matching unit and fitting unit, the history is determined
Position data set acquiring unit is extracted and the positioning number by the location data acquiring unit real-time reception from the Traffic network database
According to the matched section road net data of multiple buffering ranges, and then the history obtained in the section road net data extracted is determined
Position data set, the first matching unit sample the frequency according to current position determination data and the historical location data collection are carried out for the first time
Matching, the fitting unit are based on the timesharing variable weight heavy-route link library, the first matched result are fitted strong
Just obtaining final route matching result.
2. Floating Car location data automatic adaptation cushion route matching system according to claim 1, which is characterized in that
The location data is the location data of certain standard, which includes alignment system latitude and longitude coordinates, time, side
To, attributes such as speed, passenger carrying status；
The location data acquiring unit carries out dissection process, and to exception to the location data received according to the standard
Data are cleaned.
3. Floating Car location data automatic adaptation cushion route matching system according to claim 1, which is characterized in that
Each section data are the directive road sections of tool in the road network, which defined by two endpoints
And with the point set of space coordinate attribute and broken line set, and each road section includes an intersection or a discrepancy
Mouthful；
The historical location data collection acquiring unit is extracted from the Traffic network database and by location data acquiring unit reality
When multiple buffering ranges of location data for receiving matched section road net data when, by location data acquiring unit reality
When multiple buffering ranges of location data for receiving in, intersecting section is found out from the Traffic network database, in section just
Angle changing rate is carried out respectively to, reversed and two-way three kinds of situations, and is filtered out according to due north angle and do not allowed model in angular deviation
Interior section is enclosed, thus obtains multiple buffering ranges with the location data by the location data acquiring unit real-time reception
The section road net data matched, and then obtain the historical location data collection in the section road net data extracted.
4. Floating Car location data automatic adaptation cushion route matching system according to claim 1, which is characterized in that
The first matching unit samples the frequency according to current position determination data and the historical location data collection is matched for the first time
When, the time threshold of each calculating cycle according to the current position determination data sampling frequency is set automatically, is reaching time threshold triggering
During condition, the historical location data collection is grouped according to the buffering range, and the history of each grouping is positioned
The section data of each historical location data are compared with the section data of preceding several cycle match in data set, remove with it is former
The section of a cycle match does not have the section of topological relation, and ensures that each historical location data only corresponds to a section,
It is final to retain one group of most historical location data collection of the section quantity to match with the section of preceding several cycle match, it obtains just
Beginning matching result.
5. Floating Car location data automatic adaptation cushion route matching system according to claim 1, which is characterized in that
The fitting correction includes：
Fitting correction for the first time, solves that range of drift is big, turn around the matching problems such as traveling, and by the section under the conditions of simple road network
Effectively connected；
Second of fitting correction, using positive sequence method, path connects under the conditions of completing complicated road network, while corrects fitting production for the first time
Raw error；
Third time fitting correction, the error repaired and generated in the fit procedure of second of front is corrected using inverted order method, completes path
Matching.
A kind of 6. Floating Car location data automatic adaptation cushion route matching method, which is characterized in that
Including：
Location data obtaining step, for the location data of real-time reception Floating Car client transmission；
Timesharing variable weight heavy-route link library construction step, according to history Floating Car motion track and current anchor point degree of rarefication dynamic
Link field range is set, is cut road network in Traffic network database, and add according to timesharing category of roads according to link field range
Power calculates the path that every section reaches another indirectly connected road network in link field, and the Traffic network database is by Floating Car
The section road net data matching of historical location data and topological structure preserves；
Route matching step, including historical location data collection obtaining step, first matching step and fit procedure, the history is determined
Position data set obtaining step extracts and the positioning number by the location data obtaining step real-time reception from the Traffic network database
According to the matched section road net data of multiple buffering ranges, and then the history obtained in the section road net data extracted is determined
Position data set, the first matching step sample the frequency according to current position determination data and the historical location data collection are carried out for the first time
Matching, the fit procedure are based on timesharing variable weight heavy-route link library, and carrying out cubic fit correction to first matched result comes
Obtain final route matching result；And
Path step display, show paths matching result on the electronic map.
7. Floating Car location data automatic adaptation cushion route matching method according to claim 6, which is characterized in that
The location data is the location data of certain standard, which includes alignment system latitude and longitude coordinates, time, side
To, attributes such as speed, passenger carrying status；
The location data obtaining step carries out dissection process, and to exception to the location data received according to the standard
Data are cleaned.
8. Floating Car location data automatic adaptation cushion route matching method according to claim 6, which is characterized in that
Each section data are the directive road sections of tool in the road network, which defined by two endpoints
And with the point set of space coordinate attribute and broken line set, and each road section includes an intersection or a discrepancy
Mouthful；
The historical location data collection obtaining step extracts from the Traffic network database and by location data obtaining step reality
When multiple buffering ranges of location data for receiving matched section road net data when, by location data obtaining step reality
When multiple buffering ranges of location data for receiving in, intersecting section is found out from the Traffic network database, in section just
Angle changing rate is carried out respectively to, reversed and two-way three kinds of situations, and is filtered out according to due north angle and do not allowed model in angular deviation
Interior section is enclosed, thus obtains multiple buffering ranges with the location data by the location data obtaining step real-time reception
The section road net data matched, and then obtain the historical location data collection in the section road net data extracted.
9. Floating Car location data automatic adaptation cushion route matching method according to claim 6, which is characterized in that
The first matching step samples the frequency according to current position determination data and the historical location data collection is matched for the first time
When, the time threshold of each calculating cycle according to the current position determination data sampling frequency is set automatically, is reaching time threshold triggering
During condition, the historical location data collection is grouped according to buffering range, and by the historical location data of each grouping
The section data of each historical location data is concentrated to be compared with the section data of preceding several cycle match, are removed and preceding several weeks
Phase matched section does not have the section of topological relation, and ensures that each historical location data only corresponds to a section, finally
Retain one group of most historical location data collection of the section quantity to match with the section of preceding several cycle match, obtain initial
With result.
10. Floating Car location data automatic adaptation cushion route matching method according to claim 6, which is characterized in that
The fitting correction includes：
Fitting correction for the first time, removal range of drift is big, the sections such as traveling of turning around, and the section under the conditions of simple road network is carried out
Effectively connection；
Second of fitting correction, using positive sequence method, path connects under the conditions of completing complicated road network, while corrects fitting production for the first time
Raw error；
Third time fitting correction, the error repaired and generated in the fit procedure of second of front is corrected using inverted order method, completes path
Matching.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109163732A (en) * | 2018-08-07 | 2019-01-08 | 武汉中海庭数据技术有限公司 | A kind of high-precision road calculating matching process and system based on intersection |
| CN109286399A (en) * | 2018-08-14 | 2019-01-29 | 同济大学 | The compression method of GPS track data based on lzw algorithm |
| CN109657713A (en) * | 2018-12-11 | 2019-04-19 | 武汉大学 | A kind of multiple-factor road network method and system based on crowd-sourced road net data |
| CN111311675A (en) * | 2020-02-11 | 2020-06-19 | 腾讯科技（深圳）有限公司 | Vehicle positioning method, device, equipment and storage medium |
| CN112033418A (en) * | 2020-09-15 | 2020-12-04 | 四川大学 | Offline map matching method |
| CN117706478A (en) * | 2024-02-02 | 2024-03-15 | 腾讯科技（深圳）有限公司 | Positioning drift identification method, device, equipment and storage medium |
| CN117706478B (en) * | 2024-02-02 | 2024-05-03 | 腾讯科技（深圳）有限公司 | Positioning drift identification method, device, equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101694749A (en) * | 2009-08-25 | 2010-04-14 | 北京世纪高通科技有限公司 | Method and device for speculating routes |
| 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 |
| CN104504898A (en) * | 2014-12-11 | 2015-04-08 | 王东宇 | Online map matching method based on floating car data on tunnel road section |
| CN104634352A (en) * | 2015-03-02 | 2015-05-20 | 吉林大学 | Road matching method based on fusion of probe vehicle movement track and electronic map |
| CN105261212A (en) * | 2015-09-06 | 2016-01-20 | 中山大学 | Travel space-time analysis method based on taxi GPS data map matching |
| CN105509753A (en) * | 2015-12-28 | 2016-04-20 | 北京握奇智能科技有限公司 | Map matching method and system based on floating car satellite positioning data |
| JP2016133386A (en) * | 2015-01-19 | 2016-07-25 | 一般財団法人道路交通情報通信システムセンター | Map matching system and map matching method |
| CN106023588A (en) * | 2016-06-15 | 2016-10-12 | 重庆云途交通科技有限公司 | Traffic big data-based travel time extraction, prediction and query method |
| CN106023587A (en) * | 2016-05-25 | 2016-10-12 | 电子科技大学 | Track data road network precise matching method based on multi-information fusion |
| CN106469505A (en) * | 2015-08-20 | 2017-03-01 | 方正国际软件(北京)有限公司 | A kind of floating wheel paths method for correcting error and device |
| US20170322035A1 (en) * | 2016-05-05 | 2017-11-09 | Here Global B.V. | Method and apparatus for matching probe points to road segments utilizing a trajectory identifier |
-
2017
- 2017-12-29 CN CN201711473687.0A patent/CN108253974B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101694749A (en) * | 2009-08-25 | 2010-04-14 | 北京世纪高通科技有限公司 | Method and device for speculating routes |
| 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 |
| CN104504898A (en) * | 2014-12-11 | 2015-04-08 | 王东宇 | Online map matching method based on floating car data on tunnel road section |
| JP2016133386A (en) * | 2015-01-19 | 2016-07-25 | 一般財団法人道路交通情報通信システムセンター | Map matching system and map matching method |
| CN104634352A (en) * | 2015-03-02 | 2015-05-20 | 吉林大学 | Road matching method based on fusion of probe vehicle movement track and electronic map |
| CN106469505A (en) * | 2015-08-20 | 2017-03-01 | 方正国际软件(北京)有限公司 | A kind of floating wheel paths method for correcting error and device |
| CN105261212A (en) * | 2015-09-06 | 2016-01-20 | 中山大学 | Travel space-time analysis method based on taxi GPS data map matching |
| CN105509753A (en) * | 2015-12-28 | 2016-04-20 | 北京握奇智能科技有限公司 | Map matching method and system based on floating car satellite positioning data |
| US20170322035A1 (en) * | 2016-05-05 | 2017-11-09 | Here Global B.V. | Method and apparatus for matching probe points to road segments utilizing a trajectory identifier |
| CN106023587A (en) * | 2016-05-25 | 2016-10-12 | 电子科技大学 | Track data road network precise matching method based on multi-information fusion |
| CN106023588A (en) * | 2016-06-15 | 2016-10-12 | 重庆云途交通科技有限公司 | Traffic big data-based travel time extraction, prediction and query method |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109163732A (en) * | 2018-08-07 | 2019-01-08 | 武汉中海庭数据技术有限公司 | A kind of high-precision road calculating matching process and system based on intersection |
| CN109163732B (en) * | 2018-08-07 | 2020-12-11 | 武汉中海庭数据技术有限公司 | High-precision road calculation matching method and system based on intersection |
| CN109286399A (en) * | 2018-08-14 | 2019-01-29 | 同济大学 | The compression method of GPS track data based on lzw algorithm |
| CN109286399B (en) * | 2018-08-14 | 2022-04-15 | 同济大学 | Compression method of GPS track data based on LZW algorithm |
| CN109657713A (en) * | 2018-12-11 | 2019-04-19 | 武汉大学 | A kind of multiple-factor road network method and system based on crowd-sourced road net data |
| CN111311675A (en) * | 2020-02-11 | 2020-06-19 | 腾讯科技（深圳）有限公司 | Vehicle positioning method, device, equipment and storage medium |
| CN111311675B (en) * | 2020-02-11 | 2022-09-16 | 腾讯科技（深圳）有限公司 | Vehicle positioning method, device, equipment and storage medium |
| CN112033418A (en) * | 2020-09-15 | 2020-12-04 | 四川大学 | Offline map matching method |
| CN112033418B (en) * | 2020-09-15 | 2023-05-12 | 四川大学 | Offline map matching method |
| CN117706478A (en) * | 2024-02-02 | 2024-03-15 | 腾讯科技（深圳）有限公司 | Positioning drift identification method, device, equipment and storage medium |
| CN117706478B (en) * | 2024-02-02 | 2024-05-03 | 腾讯科技（深圳）有限公司 | Positioning drift identification method, device, equipment and storage medium |
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