CN109359164B - 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 a Reduce function to process possible path query and probability calculation, and realizing Reduce 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 the technical field of space-time data, and particularly relates to a road network moving object prediction probability range query method.
Background
The road network moving object prediction probability range query is widely applied to location-based services, for example, a merchant can carry out electronic advertisement push to people possibly passing through the range near a shop; and (3) predicting the striking target within the future time range in the enemy battle, and implementing accurate striking and the like. The existing research for inquiring the prediction probability range of the moving object around the road network assumes that the moving object motion line is determined, and the uncertainty of the moving object motion track caused by low sampling frequency is not considered. And the track uncertainty requires to improve the efficiency and the precision of possible path query and position probability calculation of the road network moving object meeting the query conditions. Therefore, a method for quickly acquiring a possible path candidate vertex set and quickly predicting the probability range query by means of the road network relation and the time relation is designed, and the precision and the efficiency of the query of the prediction range of the uncertain road network moving object caused by the sampling frequency are guaranteed.
Brilinggate et al (Brilinggate A. location-related context in mobile services [ D ], PhD in Computer science, Aalborg University,2006.) predict the route of a mobile object by comparing the current time and location of the mobile object with all previously recorded historical routes. But this method is not suitable for large-scale track databases because all historical data is kept. Jeung et al (Jeung H., Yiu M.L., Zhou X.and Jensen C.S. Path prediction and prediction range prediction in road network databases [ J ]. The VLDB Journal,2010,19(4): 585-. The model obtains all possible steering modes of the mobile object at different intersections and the speed of each object in a road network on a road section, provides a maximum possible and greedy algorithm to predict the motion path of the object, and provides an effective index mechanism to support prediction range query. Abdeltawab et al (Abdeltawab M.H. predictive processing on moving objects [ C ]. In Data Engineering works (ICDEW),2014 IEEE 30th International Conference on Data Engineering ICDE, Illinois, USA, Mar 2014: 340: 344.) propose an iROAD framework giving a new Data structure availability tree capable of achieving the purpose of significantly reducing the computation time by clipping out the relevant space of each moving object, which can be used to handle real large-scale road networks and mass moving object cases. However, the uncertainty of the motion trajectory of the moving object caused by low sampling frequency is not considered in the methods, and the precision and the query efficiency of the prediction probability range query cannot be guaranteed.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a method for querying a prediction probability range of a moving object in a road network.
In order to achieve the purpose, the invention adopts the following technical scheme: a road network moving object prediction probability range query method comprises the following steps: step 1: 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; step 2: finding all moving object OIDs earlier than predicted time tcSample of the latest sampling pointsForm a samplesA data set; and step 3: all samples to be queriedsThe data set is divided into M segments corresponding to M Map tasks; and 4, step 4: calling a Map function to process space limitation and realizing Map operation; and 5: calling a Reduce function to process possible path query and probability calculation, and realizing Reduce operation; step 6: setting an input path and an output path, and starting MapReduce parallel operation; and 7: and calling a sub-query result merging program to merge all query results into a complete result.
Preferably, step 1 specifically comprises: constructing a time-space multi-dimensional index structure: the spatial dimension is a road network index structure, the road network relation is expressed through the boundary vertex of each layer of node, an adjacent matrix and the shortest time of the road section under the maximum speed limit driving, the possible path query is solved, and a Hash table is designed to quickly correspond the leaf node of the spatial dimension to the RID of the road section in the space dimension; time dimension employing time granularity based B+-a tree index creation mode, indexing the sampling time points based on the upper layer granularity; and recording paths between the vertexes of the node boundary step by step during query, and storing the paths in a Region table form to realize indirect indexing of partial uncertain data.
Preferably, the specific steps of step 4 include: step 4.1: sample for inputsJudging that the OID is at tcWhether the time of day is likely to be at the link RID; step 4.2: according to sample when the space limitation condition is satisfiedsJudging the sub-division represented by the leaf node of the spatial index, and outputting<Sub-partition ID, samples>(ii) a Step 4.3: output according to the sub-division IDThe sets are sorted to generate tuples<Subdivision ID, list>。
Preferably, step 4.1 specifically comprises: OID predicts time t if moving objectcOn the route segment RID, its possible positions on the RID must be at the two vertices v of the route segment RIDsAnd veTo (c) to (d); suppose viFor v on the RID of a road sectionsAnd veAny point in between, tcAt time OID at viDot, samplesFor all OIDs earlier than predicted time tcLatest sampling point of tsIts sampling instant; with samplesAnd viIs set to Δ t, Δ t must satisfy Δ t ≦ tc-tsThe maximum value of the speed of the moving object is the maximum speed limit s of the actual urban roadmax70km/h, therefore samplesAnd viThe road network distance r is less than or equal to (t)c-ts)﹒smaxCan be obtained as viAs the center of circle, (t)c-ts)﹒smaxIs a circular area of radius, again because of viAre respectively vsAnd veThus the value range of the center of the circle is [ v ]s,ve]The circular area is arranged from v along the center of the circlesPush to veForming a spatially confined area.
Preferably, step 5 specifically comprises the steps of: step 5.1: performing tsAnd tcQuerying possible paths of the OID of the moving object at the time; step 5.2: calculating the position probability of the road section RID in the possible path ph (u, v) between u and v by using the time relation
Preferably, step 5.1 specifically comprises the following steps:
step 5.1.1: calculating Δ t ═ tc-ts；
Step 5.1.2: establishing the vertex range area of the possible path query to form a query vertex set V (t)c)；
Step 5.1.3: at query vertex set V (t)c) In, set u ═ samplesU is the queryStarting a top point;
step 5.1.4: adjacent vertex u of uiAccording to tm(u,ui) Ascending sort, tm(u,ui) Representing a road section (u, u)i) I.e. at a starting point of u, uiSection of road (u, u) as destinationi) Time required for the middle moving object to travel at the maximum speed limit s, tm(u,ui) L/s, l denotes a link (u, u)i) Length of (d);
step 5.1.5: sequentially searching adjacent vertexes u by taking u as a starting pointiThe composition is based on u, which is the starting pointiPossible path to end ph (u, u)i) It is compared with the possible path ph obtained by the queryjTogether generate predicted probable path phjI.e. phj＝phj×ph(u,ui) Where is phjRepresenting possible paths predicted from the query, ph (u, u)i) Indicating a newly joined road section (u, u)i) Possible paths of (2);
step 5.1.6: resetting the starting point u as the adjacent point uiI.e. u-uiSequentially executing the steps S5.1.4 and S5.1.5 until (t)m(phj)>(tc-ts) Is finished).
Preferably, step 5.1.2 specifically comprises: predicting probability range to query for obtaining sample to be queriedsThereafter, the termination conditions of its possible path queries must be determined. Consider the time range of the query as Δ t ═ tc-tsThe maximum speed limit of the actual urban road is s as the maximum value of the speed of the moving objectmaxThe maximum value of the queried road network distance is (t) 70km/hc-ts)﹒smax。
Preferably, step 5.2 specifically comprises: processing position probability on road section RID in path ph (u, v) by adopting time relation
Total time Δ t ═ tc-tsFrom OID to RID start v of road sectionsIs tea(vs) The longest time of the OID on the RID at this time is (t)c-tea(vs) ); OID to RID starting point v of road sectionsIs tcAt this time, the OID is 0 on the RID. The maximum time interval of the OID on the RID is therefore (t)c-tea(vs) -0) from which can be obtained
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
the method rapidly obtains the possible path candidate vertex set through the space range limitation based on the geometric relationship, and realizes the prediction probability range query by means of the road network relationship and the time relationship design. The invention realizes the improvement of the query precision and efficiency of the prediction range of the road network moving object with uncertain track caused by sampling frequency. The method has the advantages of fast obtaining of candidate vertex sets for prediction probability range query, high query precision and efficiency and the like.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the steps of the present invention.
FIG. 2 is a schematic diagram of the spatial bounding region and vertex range region of the present invention.
In the figure: samples-the same moving object OID is earlier than the predicted instant tcAt the latest sampling point of vs-querying the starting point of the route segment RID, veEnquiring the end point of the route section RID, vi-a distance RID on vsAnd veAny point in between, tcPredicting the probability range query time, ts-tcSample of the latest preceding samplesCorresponding sampling instant smax-movingTaking the maximum speed limit s of the actual urban road as the maximum value of the object speedmax＝70km/h，r-r＝(tc-ts)﹒smax。
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the claims, the specification and the drawings of the present invention, the terms "including", "having" and their variants, if used, are intended to be inclusive and not limiting.
Example 1: referring to fig. 1, the method for querying the prediction probability range of the moving object in the road network comprises the following steps:
step S1: 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;
in the present embodiment, given the query route RID, the query time is the predicted time tcProbability threshold α, prediction probability range query q (RID, t)cα) Return to the predicted time tcRID probability values P of all passing road sectionstc,RID(OID) a set of moving object OIDs ≧ α.
In this embodiment, a spatio-temporal multidimensional index structure is constructed: the spatial dimension is a road network index structure, the road network relation is expressed through the boundary vertex of each layer of node, an adjacent matrix and the shortest time of the road section under the maximum speed limit driving, the possible path query is solved, and a Hash table is designed to quickly correspond the leaf node of the spatial dimension to the RID of the road section in the space dimension; time dimension employing time granularity based B+And a tree index creation mode, wherein the sampling time point is indexed based on the upper layer granularity. During the query, the paths between the vertices of the node boundary are recorded step by step and stored in a Region table form, so that the purposes of indirectly indexing partial uncertain data and improving the query efficiency are achieved.
Step S2: finding all moving object OIDs ahead of predictionTime tcSample of the latest sampling pointsForm a samplesA data set;
step S3: all samples to be queriedsThe data set is divided into M segments corresponding to M Map tasks;
step S4: calling a Map function to process space limitation and realizing Map operation;
in the present embodiment, step S4 can be completed by the following substeps.
Step S41: sample for inputsJudging that the OID is at tcWhether the time of day is likely to be at the link RID;
in the present embodiment, if the moving object predicts the time t, the OID predicts the time tcOn the route segment RID, its possible positions on the RID must be at the two vertices v of the route segment RIDsAnd veIn the meantime. Suppose viFor v on the RID of a road sectionsAnd veAny point in between, tcAt time OID at viAnd (4) point. samplesFor all OIDs earlier than predicted time tcLatest sampling point of tsIts sampling instant. With samplesAnd viIs set to Δ t, Δ t must satisfy Δ t ≦ tc-tsThe maximum value of the speed of the moving object is the maximum speed limit s of the actual urban roadmax70km/h, therefore samplesAnd viThe road network distance r is less than or equal to (t)c-ts)﹒smaxCan be obtained as viAs the center of circle, (t)c-ts)﹒smaxIs a circular area of radius, again because of viAre respectively vsAnd veThus the value range of the center of the circle is [ v ]s,ve]The circular area is arranged from v along the center of the circlesPush to veForming a spatially confined area. Referring to FIG. 2, if samplesFor this purpose, it must be in the space-limited region formed by the radius r.
Step S42: according to sample when the space limitation condition is satisfiedsJudging the sub-division represented by the leaf node of the spatial index, and outputting<Sub-partition ID, samples>；
Step S43: sorting the output set according to the subdivision ID to generate a tuple < subdivision ID, list >;
step S5: calling a Reduce function to process possible path query and probability calculation, and realizing Reduce operation;
in the present embodiment, step S5 can be completed by the following substeps.
Step S51: performing tsAnd tcQuerying possible paths of the OID of the moving object at the time;
step S511: calculating Δ t ═ tc-ts；
Step S512: establishing the vertex range area of the possible path query to form a query vertex set V (t)c)；
In this embodiment, the prediction probability range is queried to obtain the sample to be queriedsThereafter, the termination conditions of its possible path queries must be determined. Consider the time range of the query as Δ t ═ tc-tsThe maximum speed limit of the actual urban road is s as the maximum value of the speed of the moving objectmaxThe maximum value of the queried road network distance is (t) 70km/hc-ts)﹒smax. Referring to FIG. 2, once exceeded with samplesAs the center of circle, (t)c-ts)﹒smaxThe query terminates immediately for a circular area of radius.
Step S513: at query vertex set V (t)c) In, set u ═ samplesU is the query start vertex;
step S514: adjacent vertex u of uiAccording to tm(u,ui) Ascending sort, tm(u,ui) Representing a road section (u, u)i) I.e. at a starting point of u, uiSection of road (u, u) as destinationi) Time required for the middle moving object to travel at the maximum speed limit s, tm(u,ui) L/s, l denotes a link (u, u)i) Length of (d);
step S515: sequentially searching adjacent vertexes u by taking u as a starting pointiThe composition is based on u, which is the starting pointiPossible path to end ph (u, u)i) It is combined withPossible path ph obtained by queryjTogether generate predicted probable path phjI.e. phj＝phj×ph(u,ui) Where is phjRepresenting possible paths predicted from the query, ph (u, u)i) Indicating a newly joined road section (u, u)i) Possible paths of (2);
step S516: resetting the starting point u as the adjacent point uiI.e. u-uiStep S514, step S515 are executed in sequence until (t)m(phj)>(tc-ts) Is finished).
In this embodiment, path finding is possible to distinguish between two cases: first, if there is no RID for the road segment to be queried in a possible path being queried, i.e. when the sum of time of querying a path exceeds Δ t ═ tc-tsOr the range of the searched vertex exceeds the vertex area in the step S512, but the RID is still not included in the query path; second, there is a query road segment RID in a possible path being queried. For the first case, the query can be stopped as long as any one of two conditions of no RID exists, and the path is up to the previous vertex of the predicted query; for the latter case, it is only necessary to query the starting vertex where the RID appears.
Step S52: calculating the position probability of the road section RID in the possible path ph (u, v) between u and v by using the time relation
In the present embodiment, the position probability on the link RID within the path ph (u, v) is processed using the time relationship
Total time Δ t ═ tc-tsFrom OID to RID start v of road sectionsIs tea(vs) The longest time of the OID on the RID at this time is (t)c-tea(vs) ); OID to RID starting point v of road sectionsIs tcAt this time, the OID is 0 on the RID. So OID is the most important on RIDThe large time interval is (t)c-tea(vs) -0) from which can be obtained
Step S6: setting an input path and an output path, and starting MapReduce parallel operation;
step S7: and calling a sub-query result merging program to merge all query results into a complete result.
The method rapidly obtains the possible path candidate vertex set through the space range limitation based on the geometric relationship, and realizes the prediction probability range query by means of the road network relationship and the time relationship design. The invention realizes the improvement of the query precision and efficiency of the prediction range of the road network moving object with uncertain track caused by sampling frequency. The method has the advantages of fast obtaining of candidate vertex sets for prediction probability range query, high query precision and efficiency and the like.
While the foregoing description shows and describes the preferred embodiments of the present invention, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A road network moving object prediction probability range query method is characterized by comprising the following steps:
step 1: 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;
step 2: finding all moving object OIDs earlier than expectedMeasuring time tcSample of the latest sampling pointsForm a samplesA data set;
and step 3: all samples to be queriedsThe data set is divided into M segments corresponding to M Map tasks;
and 4, step 4: calling a Map function to process space limitation and realize Map operation, which specifically comprises the following steps:
step 4.1: sample for inputsJudging that the OID is at tcWhether the time of day is likely to be at the link RID;
step 4.2: according to sample when the space limitation condition is satisfiedsJudging the sub-division represented by the leaf node of the spatial index, and outputting<Sub-partition ID, samples>；
Step 4.3: sorting the output set according to the subdivision ID to generate a tuple < subdivision ID, list >;
and 5: calling a Reduce function to process possible path query and probability calculation, and realizing Reduce operation, wherein the method specifically comprises the following steps:
step 5.1: performing tsAnd tcQuerying possible paths of the OID of the moving object at the time;
step 5.2: calculating the position probability of the road section RID in the possible path ph (u, v) between u and v by using the time relation
Step 6: setting an input path and an output path, and starting MapReduce parallel operation;
and 7: and calling a sub-query result merging program to merge all query results into a complete result.
2. The road network moving object prediction probability range query method according to claim 1, wherein the step 1 specifically comprises:
constructing a time-space multi-dimensional index structure: the spatial dimension is a road network index structure, the road network relation is expressed through the boundary vertex of each layer of node, an adjacent matrix and the shortest time of the road section under the maximum speed limit driving, the possible path query is solved, and a Hash table is designed to quickly correspond the leaf node of the spatial dimension to the RID of the road section in the space dimension;
time dimension employing time granularity based B+-a tree index creation mode, indexing the sampling time points based on the upper layer granularity;
and recording paths between the vertexes of the node boundary step by step during query, and storing the paths in a Region table form to realize indirect indexing of partial uncertain data.
3. The road network moving object prediction probability range query method according to claim 1, wherein the step 4.1 specifically comprises:
OID predicts time t if moving objectcOn the route segment RID, its possible positions on the RID must be at the two vertices v of the route segment RIDsAnd veTo (c) to (d);
suppose viFor v on the RID of a road sectionsAnd veAny point in between, tcAt time OID at viDot, samplesFor all OIDs earlier than predicted time tcLatest sampling point of tsIts sampling instant;
with samplesAnd viIs set to Δ t, Δ t must satisfy Δ t ≦ tc-tsThe maximum value of the speed of the moving object is the maximum speed limit s of the actual urban roadmax70km/h, therefore samplesAnd viThe road network distance r is less than or equal to (t)c-ts)﹒smaxCan be obtained as viAs the center of circle, (t)c-ts)﹒smaxIs a circular area of radius, again because of viAre respectively vsAnd veThus the value range of the center of the circle is [ v ]s,ve]The circular area is arranged from v along the center of the circlesPush to veForming a spatially confined area.
4. The road network moving object prediction probability range query method according to claim 1, wherein the step 5.1 specifically comprises the following steps:
step 5.1.1: calculating Δ t ═ tc-ts；
Step 5.1.2: establishing the vertex range area of the possible path query to form a query vertex set V (t)c)；
Step 5.1.3: at query vertex set V (t)c) In, set u ═ samplesU is the query start vertex;
step 5.1.4: adjacent vertex u of uiAccording to tm(u,ui) Ascending sort, tm(u,ui) Representing a road section (u, u)i) I.e. starting from u, uiSection of road (u, u) as destinationi) Time required for the middle moving object to travel at the maximum speed limit s, tm(u,ui) L/s, l denotes a link (u, u)i) Length of (d);
step 5.1.5: sequentially searching adjacent vertexes u by taking u as a starting pointiThe composition takes u as a starting point, uiPossible path to end ph (u, u)i) It is compared with the possible path ph obtained by the queryjTogether generate predicted probable path phjI.e. phj＝phj×ph(u,ui) Where is phjRepresenting possible paths predicted from the query, ph (u, u)i) Indicating a newly joined road section (u, u)i) Possible paths of (2);
step 5.1.6: resetting the starting point u as the adjacent point uiI.e. u-uiStep 5.1.4, step 5.1.5 are performed in sequence until (t)m(phj)>(tc-ts) Is finished).
5. The road network moving object prediction probability range query method according to claim 4, wherein the step 5.1.2 specifically comprises:
predicting probability range to query for obtaining sample to be queriedsThen, the termination condition of its possible path query must be determined;
consider the time range of the query as Δ t ═ tc-tsThe maximum speed limit of the actual urban road is s as the maximum value of the speed of the moving objectmax70km/h, due toThe maximum road network distance for this query is (t)c-ts)﹒smax。
6. The method as claimed in claim 1, wherein said method comprises the steps of: the step 5.2 specifically comprises the following steps:
processing position probability on road section RID in path ph (u, v) by adopting time relation
Total time Δ t ═ tc-tsFrom OID to RID start v of road sectionsIs tea(vs) The longest time of the OID on the RID at this time is (t)c-tea(vs) ); OID to RID starting point v of road sectionsIs tcAt this time, the OID time on the RID is 0, so the maximum time interval of the OID on the RID is (t)c-tea(vs) ) 0) obtained therefrom
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