CN109359164A - Road network moving object prediction probability range query method - Google Patents
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Abstract
The invention provides a road network moving object prediction probability range query method, which comprises the following steps: according to the Hash table, positioning the spatial index leaf node of the RID of the query road section in the query condition, and positioning the corresponding time index B+-a tree root node; finding all moving object OIDs earlier than predicted time tcSample of the latest sampling pointsForm a samplesA data set; all samples to be queriedsThe data set is divided into M segments corresponding to M Map tasks; calling a Map function to process space limitation and realizing Map operation; calling Reduce function to process possible path inquiry and probability calculationReducing operation; setting an input path and an output path, and starting MapReduce parallel operation; and calling a sub-query result merging program to merge all query results into a complete result. The road network moving object prediction probability range query method provided by the invention has the advantages of fast obtaining of candidate vertex sets of prediction probability range query, high query precision and efficiency and the like.
Description
Technical field
The invention belongs to space-time data technical fields, are specifically related to a kind of road network mobile object prediction probability range query
Method.
Background technique
Road network mobile object prediction probability range query is widely used in location based service, such as businessman is to retail shop
In environs e-advertising push may be carried out by crowd；Strike to being predicted in enemy's operation in future time range region
Target implements precisely strike etc..Mobile object is assumed in the existing research around road network mobile object prediction probability range query
Movement line determines, does not consider the uncertainty because of mobile object motion profile caused by sample frequency is low.And track is not true
Qualitative requirement improves the possible path inquiry and location probability computational efficiency and precision that road network mobile object meets querying condition.Cause
This designs a kind of quick obtaining possible path candidate vertices set, can carry out quick predict by road network relationship and time relationship
The method of probable range inquiry guarantees that track caused by sample frequency does not know the precision of road network mobile object estimation range inquiry
With efficiency.
(the Brilingaite A.Location-related context in mobile such as Brilingaite
Services [D], PhD in Computer science, Aalborg University, 2006.) by the way that mobile object is worked as
The route of mobile object is predicted in preceding moment and position compared with the history route recorded before all.But this method
Because retaining all historical datas not being suitable for extensive track database.Jeung etc. (Jeung H., Yiu M.L.,
Zhou X.and Jensen C.S.Path prediction and predictive range querying in road
Network databases [J] .The VLDB Journal, 2010,19 (4): 585-602.) one kind is proposed based on movement
The crossing behavior prediction model of subjects history track.The model obtains mobile object in all possible steering pattern in different crossings
With speed of the object each in road network on section, maximum possible and greedy algorithm are proposed to predict the motion path of object,
Effective Indexing Mechanism is proposed simultaneously supports estimation range inquiry.(the Abdeltawab such as Abdeltawab
M.H.Predictive query processing on moving objects[C].In Data Engineering
Workshops(ICDEW),2014 IEEE 30th International Conference on Data Engineering
ICDE, Illinois, USA, Mar.2014:340-344.) iROAD frame is proposed, which gives a kind of new data
Structure reachability tree, can reach by cutting off the correlation space of each mobile object to substantially reduce and calculate the time
Purpose, iROAD can be used to Coping with Reality large-scale road network and magnanimity mobile object situation.But the above method does not consider to adopt
The uncertainty of mobile object motion profile caused by sample frequency is low, the precision and search efficiency of prediction probability range query can not
Guarantee.
Summary of the invention
Above-mentioned the deficiencies in the prior art are directed to, the purpose of the present invention is to provide a kind of road network mobile object prediction probabilities
Range query method.
To reach above-mentioned purpose, the present invention adopts the following technical scheme: a kind of road network mobile object prediction probability range is looked into
Inquiry method includes the following steps: step 1: according to spatial index leaf knot where inquiry section RID in Hash table locating query condition
Point positions corresponding time index B+- tree root node；Step 2: finding all mobile object OID earlier than prediction time tcThe latest
Sampled point samples, form samplesData set；Step 3: by all sample to be checkedsSegmentation of Data Set is M piece
Section, corresponding M Map task；Step 4: calling the limitation of Map function processing space, realize Map operation；Step 5: calling Reduce
Function handles possible path inquiry and probability calculation, realizes Reduce operation；Step 6: setting input and outgoing route start
MapReduce concurrent operation；Step 7: calling subquery results consolidation procedure, all query results are merged into complete knot
Fruit.
Preferably, step 1 specifically includes: space multi-dimensional index structures when building: Spatial Dimension is road network index structure, is led to
The lower shortest time expression road network relationship of the border vertices of each layer node, adjacency matrix, section maximum speed limit traveling is crossed, solution can
Energy path query, design Hash table are quickly corresponding by the leaf node of Spatial Dimension and section RID therein；Time dimension uses base
In the B of time granularity+- tree index creation mode is based on upper layer granularity to sampling time point and is indexed；Inquiry carry out in by
Indirect index part uncertain data is realized with the preservation of Region sheet form in path between step records node boundary vertex.
Preferably, specific step is as follows includes: step 4.1 for step 4: for the sample of inputs, judge its OID in tcWhen
It carves and whether is likely to be at section RID；Step 4.2: meeting space restrictive condition then according to samplesSpatial index leaf belonging to judging
The son that node indicates divides, output <sub- division ID, samples>；Step 4.3: ID, which is divided, according to son is ranked up output collection,
Generation tuple<sub- division ID, list>.
Preferably, step 4.1 specifically includes: if mobile object OID prediction time tcOn the RID of section, then it is in RID
On possible position must be in two vertex vs of section RIDsWith veBetween；Assuming that viFor v on the RID of sectionsWith veBetween it is any one
Point, tcMoment, OID was in viPoint, samplesIt is all OID earlier than prediction time tcSampled point the latest, tsFor its sampling instant；If
samplesWith viMaximum time interval be set as Δ t, then Δ t must satisfy Δ t≤tc-ts, the maximum value of mobile object speed
Take the maximum speed limit s of actual cities roadmax=70km/h, therefore samplesWith viRoad network distance r≤(tc-ts) ﹒ smax, can be with
It obtains with viFor the center of circle, (tc-ts) ﹒ smaxFor the border circular areas of radius, and because viBoundary position be respectively vsWith ve, thus
The value range in the center of circle is [vs,ve], by border circular areas along the center of circle from vsIt is pushed into ve, form confined region.
Preferably, step 5 specifically includes step: step 5.1: executing tsWith tcThe possible path of mobile object OID between moment
Inquiry；Step 5.2: calculating the location probability between u, v on the interior section RID of possible path ph (u, v) using time relationship
Preferably, step 5.1 specifically comprises the following steps:
Step 5.1.1: Δ t=t is calculatedc-ts；
Step 5.1.2: establishing the vertex range areas of possible path inquiry, forms inquiry vertex set V (tc)；
Step 5.1.3: in inquiry vertex set V (tc) in, u=sample is sets, u is inquiry initial vertex；
Step 5.1.4: by the adjacent vertex u of uiBy tm(u,ui) ascending sort, tm(u,ui) indicate section (u, ui) most
Short time with u, is being starting point, uiFor section (u, the u of terminali) in mobile object with maximum speed limit s traveling needed for time,
tm(u,ui)=l/s, l indicate section (u, ui) length；
Step 5.1.5: adjacent vertex u is successively searched as starting point using uiIt constitutes with u, is starting point, uiFor the possible path of terminal
ph(u,ui), by itself and the possible path ph that has inquiredjPrediction possible path ph is generated togetherj, i.e. phj=phj×ph(u,
ui), wherein phjIndicate the possible path that prediction has been inquired, ph (u, ui) indicate the section (u, the u that are newly addedi) can energy circuit
Diameter；
Step 5.1.6: starting point u is set as abutment points u againi, i.e. u=ui, successively execute step S5.1.4, step
S5.1.5, until (tm(phj)>(tc-ts)) terminate.
Preferably, step 5.1.2 is specifically included: prediction probability range query is obtaining sample to be checkedsAfterwards, it is necessary to
Determine the termination condition of its possible path inquiry.The time range for considering inquiry is Δ t=tc-ts, the maximum of mobile object speed
It is s that value, which takes the maximum speed limit of actual cities road,max=70km/h, therefore the road network inquired is (t apart from maximum valuec-ts) ﹒ smax。
Preferably, step 5.2 specifically includes: handling the position on the interior section RID in path ph (u, v) using time relationship
ProbabilityTotal time is Δ t=tc-ts, OID to section RID starting point vsEarliest time be tea(vs), at this time
Maximum duration of the OID on RID is (tc-tea(vs))；OID to section RID starting point vsLatest time be tc, OID exists at this time
Time on RID is 0.So maximum time interval of the OID on RID is (tc-tea(vs)) -0, it can be obtained accordingly
Compared to the prior art, technical solution provided by the invention has the following beneficial effects:
The present invention limits quick obtaining possible path candidate vertices set by the spatial dimension based on geometrical relationship, by
Prediction probability range query is realized in road network relationship and time relationship design.The present invention is realized to track caused by sample frequency not
Determine the raising of road network mobile object estimation range inquiry precision and efficiency.Time with quick obtaining prediction probability range query
The advantages that selecting vertex set, inquiring precision and is high-efficient.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes a part of the invention, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is step flow chart of the invention.
Fig. 2 is confined region and vertex range areas schematic diagram of the invention.
In figure: samplesSame mobile object OID is earlier than prediction time tcSampled point the latest, vsInquire section RID
Starting point, veInquire the terminal of section RID, viV on the RID of sectionsWith veBetween any point, tcPrediction probability range query
Moment, ts-tcSampled point sample the latest beforesCorrespondence sampling instant, smaxThe maximum value of mobile object speed, takes reality
The maximum speed limit s of urban roadmax=70km/h, r-r=(tc-ts) ﹒ smax。
Specific embodiment
In order to be clearer and more clear technical problems, technical solutions and advantages to be solved, tie below
Drawings and examples are closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
In claims of the present invention, specification and above-mentioned attached drawing, such as using term " includes ", " having " and they
Deformation, it is intended that " including but not limited to ".
Embodiment 1: referring to Fig. 1, this road network mobile object prediction probability range query method the following steps are included:
Step S1: it is corresponded to according to spatial index leaf node, positioning where inquiry section RID in Hash table locating query condition
Time index B+- tree root node；
In the present embodiment, inquiry section RID is given, the inquiry moment is prediction time tc, probability threshold value α, prediction is generally
Rate range query q (RID, tc, α) and return to prediction time tcIt is all by section RID probability value Ptc,RID(OID) >=α movement pair
As OID gathers.
In the present embodiment, space multi-dimensional index structures when building: Spatial Dimension is road network index structure, passes through each layer
Shortest time under border vertices, adjacency matrix, the section maximum speed limit of node travel indicates road network relationship, solves possible path
Inquiry, design Hash table are quickly corresponding by the leaf node of Spatial Dimension and section RID therein；Time dimension, which uses, is based on the time
The B of granularity+- tree index creation mode is based on upper layer granularity to sampling time point and is indexed.Inquiry gradually records in carrying out
Path reaches indirect index part uncertain data with the preservation of Region sheet form between lower node boundary vertex, improves inquiry effect
Rate purpose.
Step S2: all mobile object OID are found earlier than prediction time tcSampled point sample the latests, formed
samplesData set；
Step S3: by all sample to be checkedsSegmentation of Data Set is M segment, corresponding M Map task；
Step S4: calling the limitation of Map function processing space, realizes Map operation；
In the present embodiment, step S4 can be completed by following sub-step.
Step S41: for the sample of inputs, judge its OID in tcWhether the moment is likely to be at section RID；
In the present embodiment, if mobile object OID prediction time tcOn the RID of section, then its possibility on RID
It position must be in two vertex vs of section RIDsWith veBetween.Assuming that viFor v on the RID of sectionsWith veBetween any point, tcWhen
OID is carved in viPoint.samplesIt is all OID earlier than prediction time tcSampled point the latest, tsFor its sampling instant.If samples
With viMaximum time interval be set as Δ t, then Δ t must satisfy Δ t≤tc-ts, the maximum value of mobile object speed takes practical city
The maximum speed limit s of city's roadmax=70km/h, therefore samplesWith viRoad network distance r≤(tc-ts) ﹒ smax, available with vi
For the center of circle, (tc-ts) ﹒ smaxFor the border circular areas of radius, and because viBoundary position be respectively vsWith ve, thus the center of circle takes
Value range is [vs,ve], by border circular areas along the center of circle from vsIt is pushed into ve, form confined region.Referring to fig. 2, if
samplesIt, must be in the confined region that r is radius formation to be required.
Step S42: meet space restrictive condition then according to samplesThe son that spatial index leaf node belonging to judging indicates is drawn
Point, output <sub- division ID, samples>；
Step S43: foundation divides ID and is ranked up output collection, and generation tuple<sub- division ID, list>；
Step S5: the processing possible path inquiry of Reduce function and probability calculation are called, realizes Reduce operation；
In the present embodiment, step S5 can be completed by following sub-step.
Step S51: t is executedsWith tcThe possible path inquiry of mobile object OID between moment；
Step S511: Δ t=t is calculatedc-ts；
Step S512: establishing the vertex range areas of possible path inquiry, forms inquiry vertex set V (tc)；
In the present embodiment, prediction probability range query is obtaining sample to be checkedsAfterwards, it must be determined that it may
The termination condition of path query.The time range for considering inquiry is Δ t=tc-ts, the maximum value of mobile object speed takes practical city
The maximum speed limit of city's road is smax=70km/h, therefore the road network inquired is (t apart from maximum valuec-ts) ﹒ smax.Referring to fig. 2, one
Denier exceeds with samplesFor the center of circle, (tc-ts) ﹒ smaxIt is terminated immediately for the border circular areas inquiry of radius.
Step S513: in inquiry vertex set V (tc) in, u=sample is sets, u is inquiry initial vertex；
Step S514: by the adjacent vertex u of uiBy tm(u,ui) ascending sort, tm(u,ui) indicate section (u, ui) it is most short
Time with u, is being starting point, uiFor section (u, the u of terminali) in mobile object with maximum speed limit s traveling needed for time, tm
(u,ui)=l/s, l indicate section (u, ui) length；
Step S515: adjacent vertex u is successively searched as starting point using uiIt constitutes with u, is starting point, uiFor the possible path of terminal
ph(u,ui), by itself and the possible path ph that has inquiredjPrediction possible path ph is generated togetherj, i.e. phj=phj×ph(u,
ui), wherein phjIndicate the possible path that prediction has been inquired, ph (u, ui) indicate the section (u, the u that are newly addedi) can energy circuit
Diameter；
Step S516: starting point u is set as abutment points u againi, i.e. u=ui, step S514, step S515 are successively executed,
Until (tm(phj)>(tc-ts)) terminate.
In the present embodiment, possible path searches area in two kinds of situation: the first, if certain possibility inquired
Section RID to be checked is not present in path, i.e., when the time for inquiring certain paths and be more than Δ t=tc-tsOr search vertex
Range beyond the apex region in step S512, but still do not include RID in the query path；Second, certain is being looked into
There is inquiry section RID in the possible path of inquiry.For the first case, as long as practical occur that two kinds of conditions of RID are not present
It is any can stop inquiring, until the previous vertex in path to predicted query；For latter situation, as long as practical inquire
There is the initial vertex of RID.
Step S52: the location probability between u, v on the interior section RID of possible path ph (u, v) is calculated using time relationship
In the present embodiment, the location probability on the interior section RID in path ph (u, v) is handled using time relationshipTotal time is Δ t=tc-ts, OID to section RID starting point vsEarliest time be tea(vs), OID exists at this time
Maximum duration on RID is (tc-tea(vs))；OID to section RID starting point vsLatest time be tc, OID is on RID at this time
Time is 0.So maximum time interval of the OID on RID is (tc-tea(vs)) -0, it can be obtained accordingly
Step S6: setting input and outgoing route start MapReduce concurrent operation；
Step S7: subquery results consolidation procedure is called, all query results are merged into complete result.
The present invention limits quick obtaining possible path candidate vertices set by the spatial dimension based on geometrical relationship, by
Prediction probability range query is realized in road network relationship and time relationship design.The present invention is realized to track caused by sample frequency not
Determine the raising of road network mobile object estimation range inquiry precision and efficiency.Time with quick obtaining prediction probability range query
The advantages that selecting vertex set, inquiring precision and is high-efficient.
The preferred embodiment of the present invention has shown and described in above description, as previously described, it should be understood that the present invention is not office
Be limited to form disclosed herein, should not be regarded as an exclusion of other examples, and can be used for various other combinations, modification and
Environment, and can be changed within that scope of the inventive concept describe herein by the above teachings or related fields of technology or knowledge
It is dynamic.And changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention, then it all should be appended by the present invention
In scope of protection of the claims.
Claims (8)
1. a kind of road network mobile object prediction probability range query method, characterized by the following steps:
Step 1: according to spatial index leaf node where inquiry section RID in Hash table locating query condition, positioning the corresponding time
Index B+- tree root node；
Step 2: finding all mobile object OID earlier than prediction time tcSampled point sample the latests, form samplesData
Collection；
Step 3: by all sample to be checkedsSegmentation of Data Set is M segment, corresponding M Map task；
Step 4: calling the limitation of Map function processing space, realize Map operation；
Step 5: calling the processing possible path inquiry of Reduce function and probability calculation, realize Reduce operation；
Step 6: setting input and outgoing route start MapReduce concurrent operation；
Step 7: calling subquery results consolidation procedure, all query results are merged into complete result.
2. a kind of road network mobile object prediction probability range query method as described in claim 1, it is characterised in that: step 1
It specifically includes:
Space multi-dimensional index structures when building: Spatial Dimension is road network index structure, passes through the border vertices of each layer node, adjoining
Shortest time under matrix, section maximum speed limit traveling indicates road network relationship, solves possible path inquiry, design Hash table will be empty
Between dimension leaf node and section RID therein it is quickly corresponding；
Time dimension uses the B based on time granularity+- tree index creation mode carries out sampling time point based on upper layer granularity
Index；
Indirect index part is realized with the preservation of Region sheet form in path between gradually recording node boundary vertex in inquiry progress
Uncertain data.
3. a kind of road network mobile object prediction probability range query method as described in claim 1, it is characterised in that: step 4
Specific step is as follows includes:
Step 4.1: for the sample of inputs, judge its OID in tcWhether the moment is likely to be at section RID；
Step 4.2: meeting space restrictive condition then according to samplesThe son that spatial index leaf node belonging to judging indicates divides, defeated
Out <sub- division ID, samples>；
Step 4.3: foundation divides ID and is ranked up output collection, and generation tuple<sub- division ID, list>.
4. a kind of road network mobile object prediction probability range query method as claimed in claim 3, it is characterised in that: step
4.1 specifically include:
If mobile object OID prediction time tcOn the RID of section, then its possible position on RID must be section RID's
Two vertex vssWith veBetween；
Assuming that viFor v on the RID of sectionsWith veBetween any point, tcMoment, OID was in viPoint, samplesIt is all OID earlier than pre-
Survey moment tcSampled point the latest, tsFor its sampling instant；
If samplesWith viMaximum time interval be set as Δ t, then Δ t must satisfy Δ t≤tc-ts, mobile object speed is most
Big value takes the maximum speed limit s of actual cities roadmax=70km/h, therefore samplesWith viRoad network distance r≤(tc-ts) ﹒ smax,
It is available with viFor the center of circle, (tc-ts) ﹒ smaxFor the border circular areas of radius, and because viBoundary position be respectively vsWith ve,
Thus the value range in the center of circle is [vs,ve], by border circular areas along the center of circle from vsIt is pushed into ve, form confined region.
5. road network mobile object prediction probability range query method according to claim 1, it is characterised in that: step 5 tool
Body comprising steps of
Step 5.1: executing tsWith tcThe possible path inquiry of mobile object OID between moment；
Step 5.2: calculating the location probability between u, v on the interior section RID of possible path ph (u, v) using time relationship
6. road network mobile object prediction probability range query method according to claim 5, it is characterised in that: step 5.1 tool
Body includes the following steps:
Step 5.1.1: Δ t=t is calculatedc-ts；
Step 5.1.2: establishing the vertex range areas of possible path inquiry, forms inquiry vertex set V (tc)；
Step 5.1.3: in inquiry vertex set V (tc) in, u=sample is sets, u is inquiry initial vertex；
Step 5.1.4: by the adjacent vertex u of uiBy tm(u,ui) ascending sort, tm(u,ui) indicate section (u, ui) most in short-term
Between, i.e., it with u, is being starting point, uiFor section (u, the u of terminali) in mobile object with maximum speed limit s traveling needed for time, tm
(u,ui)=l/s, l indicate section (u, ui) length；
Step 5.1.5: adjacent vertex u is successively searched as starting point using uiIt constitutes with u, is starting point, uiFor the possible path ph of terminal
(u,ui), by itself and the possible path ph that has inquiredjPrediction possible path ph is generated togetherj, i.e. phj=phj×ph(u,
ui), wherein phjIndicate the possible path that prediction has been inquired, ph (u, ui) indicate the section (u, the u that are newly addedi) can energy circuit
Diameter；
Step 5.1.6: starting point u is set as abutment points u againi, i.e. u=ui, step S5.1.4, step S5.1.5 are successively executed,
Until (tm(phj)>(tc-ts)) terminate.
7. road network mobile object prediction probability range query method according to claim 6, it is characterised in that: step
5.1.2 it specifically includes:
Prediction probability range query is obtaining sample to be checkedsAfterwards, it must be determined that the termination condition of its possible path inquiry.
The time range for considering inquiry is Δ t=tc-ts, the maximum value of mobile object speed takes the maximum limit of actual cities road
Speed is smax=70km/h, therefore the road network inquired is (t apart from maximum valuec-ts) ﹒ smax。
8. road network mobile object prediction probability range query method according to claim 5, it is characterised in that: step 5.2
It specifically includes:
Location probability on the interior section RID in path ph (u, v) is handled using time relationshipTotal time is Δ
T=tc-ts, OID to section RID starting point vsEarliest time be tea(vs), maximum duration of the OID on RID is (t at this timec-tea
(vs))；OID to section RID starting point vsLatest time be tc, time of the OID on RID is 0 at this time.So OID is on RID
Maximum time interval be (tc-tea(vs)) -0, it can be obtained accordingly
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102402602A (en) * | 2011-11-18 | 2012-04-04 | 航天科工深圳（集团）有限公司 | B+ tree indexing method and device of real-time database |
| CN104036139A (en) * | 2014-06-12 | 2014-09-10 | 中国科学院软件研究所 | Moving object trajectory monitoring method |
| EP3264314A1 (en) * | 2016-06-30 | 2018-01-03 | Huawei Technologies Co., Ltd. | System and method for searching over encrypted data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102402602A (en) * | 2011-11-18 | 2012-04-04 | 航天科工深圳（集团）有限公司 | B+ tree indexing method and device of real-time database |
| CN104036139A (en) * | 2014-06-12 | 2014-09-10 | 中国科学院软件研究所 | Moving object trajectory monitoring method |
| EP3264314A1 (en) * | 2016-06-30 | 2018-01-03 | Huawei Technologies Co., Ltd. | System and method for searching over encrypted data |
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