CN107748896A - A kind of multi-level main body of Urban population flows to generation method - Google Patents
A kind of multi-level main body of Urban population flows to generation method Download PDFInfo
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- CN107748896A CN107748896A CN201711034826.XA CN201711034826A CN107748896A CN 107748896 A CN107748896 A CN 107748896A CN 201711034826 A CN201711034826 A CN 201711034826A CN 107748896 A CN107748896 A CN 107748896A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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- G06F18/2193—Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a kind of multi-level main body of Urban population to flow to generation method, track based on colony's trip, weighted comprehensive is carried out to resident trip stream on all directions in a certain position, obtain flowing mostly to for direction crowd trip, and from multi-level space scale, crowd's stream is integrated to macroscopic view from microcosmic, obtain that different zones crowd in city on different spaces yardstick goes on a journey flows mostly to, help to understand the direction character that crowd goes on a journey in the different spatial of city, intuitively resident trip knowledge is provided for urban planning and traffic administration.
Description
【Technical field】
The invention belongs to geographic information system technology field, technical problems to be solved be how colony's rail based on magnanimity
Mark data flow to generate crowd's main body of the multi-level space scale in city.
【Background technology】
In recent years, China is poured in city quickly propelling urbanization, a large amount of crowds in a short time, and this gives the pipe in city
Reason brings huge pressure, causes many "urban disease"s occur, the one kind for having become domestic large size city such as traffic congestion is led to
Disease.Traffic congestion not only carrys out huge loss to economy-zone, while has also aggravated the air pollution in city.In order to alleviate city
Traffic congestion, city manager start to put into substantial amounts of fund progress Transportation Infrastructure Construction (such as building road, subway),
Due to lacking understanding to crowd's trip requirements, cause city space infrastructure with not occurring before crowd activity's demand not
Match somebody with somebody, so as to can not fundamentally solve the trip requirements of resident, traffic jam issue can not still be solved.Therefore,
Understand that Urban population Move Mode is significant to solving Urban Residential Trip and traffic problems.
In recent years, the fast development of information and mechanics of communication (ICT) greatly changes the life style of people, while
So that we enter the big data epoch, various kinds of sensors become increasingly popular so that obtain extensive, long-term sequence, it is fine when
The individual mobile trajectory data of empty granularity becomes easy [1-2].These data include GPS hire out car data, mobile phone location data,
Social media network is registered data, bus IC card brushing card data etc., and these data provide abundant for study population's Move Mode
Data basis [3].The use of current phone is popularized very much, and especially in city, mobile phone turns into resident must can not
Few communication tool, while mobile phone is brought convenience to crowd, it can also perceive position of the crowd in city and change with time,
Make it possible gather magnanimity prolonged individual space-time trajectory data.Compared with traditional survey data, mobile phone
The sample size of data is big, record period length, updating decision and required input cost and labour are smaller.In the data in mobile phone of magnanimity
Contain abundant One-male unit information, can be used for analyzing the space-time mobile behavior rule of Urban population, therefore, data in mobile phone
Have become the significant data source of research Urban population Move Mode so that we can be with relatively low cost, and from one
The mobile behavior [4] of Urban population is studied under unprecedented spatial and temporal scales.
Data in mobile phone is that study population's mobile behavior brings huge challenge and opportunity, and the scholar from different field opens
Begin study population's Move Mode from different angles, and the space-time predictability [5- of crowd's movement is such as analyzed from the angle of statistics
6], analyze the activity space of crowd [7] based on time suboptimal control, lived instead of traditional survey data to study Urban population duty
It is distributed and extracts OD matrixes [8-9], is distributed from the angle of planning to study the Multi center structure in city and supposition land use
[10-11] etc..These researchs is we provide the knowledge of substantial amounts of Urban population activity, but existing research does not relate to
And crowd's bulk flow direction calculating.In city the flowing of crowd be with purposive, city's spatial structure function there is also
Difference, cause different zones crowd flow direction in city that there is certain directionality.Identify in the different spaces dimensional area of city
Cellphone subscriber colony flows mostly to, and can help to extract the main traffic gallery of Urban population flowing, to urban planning, traffic pipe
The construction of reason and smart city etc. is significant.
In foregoing description, the bibliography related to application includes：
[1] Li Qingquan, Li Deren .2014. big datas GIS [J] Wuhan University Journals:Information science version, 39 (6), 641-
644.
[2] Lu Feng, Zhang Hengcai .2014. big datas and broad sense GIS [J] Wuhan University Journals:Information science version, 39 (6),
645-654.
[3] some human geography basic problems that the societies of Liu Yu 2016. are perceived under visual angle think deeply [J] Geography Journals again,
71 (4), 564-575.
[4] Becker, R., C á ceres, R., Hanson, K., et al.2013.Human Mobility
Characterization from Cellular Network Data [J] .Communications of the Acm, 56
(1), 74-82.
[5] Gonz á lez, M.C., Hidalgo, C.A.＆Barab á si, A.L.2008.Understanding
Individual human mobility patterns [J] .Nature, 453 (7196), 779-782.
[6] Song, C., Qu, Z., Blumm, N., et al.2010.Limits of predictability in
Human mobility [J] .Science, 327 (5968), 1018-1021.
[7] Xu, Y., Shaw, S.L., Zhao, Z., et al.2015.Understanding aggregate human
mobility patterns using passive mobile phone location data:a home-based
Approach [J] .Transportation, 42 (4), 625-646.
[8] Calabrese, F., Lorenzo, G.D., Liu, L., et al.2011.Estimating Origin-
Destination Flows Using Mobile Phone Location Data[J].IEEE Pervasive
Computing, 10 (4), 36-44.
[9] permitted peaceful, Yin Ling, resident is identified in the mobile phone location data that Hu Jinxing .2014. sample from extensive short-term rale
Duty residence [J] Wuhan University Journals:Information science version, 39 (6), 750-756.
[10] Gao, S., Liu, Y., Wang, Y., et al.2013.Discovering Spatial Interaction
Communities from Mobile Phone Data [J] .Transactions in Gis, 17 (3), 463-481.
[11] Pei, T., Sobolevsky, S., Ratti, C., et al.2014.A new insight into land
use classification based on aggregated mobile phone data[J].International
Journal of Geographical Information Science, 28 (9), 1988-2007.
【The content of the invention】
It is an object of the invention to provide a kind of multi-level main body of Urban population to flow to generation method, based on colony's trip
Track, obtain flowing mostly to all direction crowd trips in a certain position, crowd's stream is integrated to macroscopic view from microcosmic, obtained
Flowed mostly to what different zones crowd in city on different spaces yardstick went on a journey.
To reach above-mentioned purpose, the present invention adopts the following technical scheme that：
A kind of multi-level main body of Urban population flows to generation method, it is characterised in that comprises the following steps：
Step a) calculates crowd's moving direction, moves flow and direction according to crowd and is flowed mostly to calculate crowd's flowing；
The arrow visualizing expression of step b) definition region crowds main body flow direction, i.e. start position, final position and arrow
Head length；
The rule-based grid of step c), realize multi-layer body flow direction life of crowd's bulk flow direction on Spatial Dimension
Into.
Further, calculating crowd's moving direction is specially in the step a)：
To be started from due east to 0 ° to 360 ° of direction calculating counterclockwise, its crowd is calculated by beginning and end coordinate and moved
Dynamic direction value；Using a certain communication base station as starting point, each pair OD direction and flow are spilt out with the base station, power is used as using flow
Weight, the main body flow direction for averagely, calculating base station crowd is weighted to its direction.
Further, the calculating of flow weight size is specially in the step a)：Flow weight is by each OD pairs of weight
Try to achieve, by calculating contribution margin of each OD streams in main body flow direction, then summation obtains the weight size of the main body stream.
Further, region crowd's main body flows to arrow visualizing expression in the step b), specifically includes arrow starting point position
The Geometric center coordinates of base station location in region are set to, arrow final position calculates according to the direction θ and weight w of main body stream
, and control parameter is introduced to control the length of arrow.
Further, the determination of multi-level regional extent is specially in the step c)：
Using grid as regional extent, whole city is divided using the grid of different space scales, from thin
To multi-level city grid is slightly built, then crowd's OD streams between each layer of region are calculated, excluded excessively single
Crowd inside region flows OD, obtains the crowd OD streams between the level regions and region.
Further, the size of the initial mesh length of side is set as 500m, and hereafter each grade grid length of side size is upper first-class
2 times of the level grid length of side, the determination formula of grid length of side size is as follows：
kn=k0·2n-1Wherein n=1,2L n (3)
K in above formulanFor the length of side size of n-th grade of grid.
The multi-level main body of Urban population of the present invention flows to generation method, based on the track of colony's trip, to a certain position institute
There is on direction resident trip stream carry out weighted comprehensive, obtain flowing mostly to for direction crowd trip, and from multi-level space chi
Degree, is integrated from microcosmic to macroscopic view to crowd's stream, and the different zones crowd in city on different spaces yardstick that obtains goes on a journey main
Flow direction, help to understand the direction character that crowd goes on a journey in the different spatial of city, provided directly for urban planning and traffic administration
The resident trip knowledge of sight.Multi-level crowd's main body flow calculation methodologies provided by the invention can be helped from microcosmic to macro-scale
Understand the Main way of city different spaces region crowd movement, city manager, planning participant can be helped to be better understood from
The direction character that different zones crowd is flowed in city, important references are provided for urban planning, traffic administration.
The inventive method advantage is：
First, this method can identify the main flow direction of a certain region crowd from mixed and disorderly track data, can help
Help and intuitively observe region crowd flow direction feature；
Secondly, this method can generate multi-level crowd's main body flow direction, from microcosmic to city from the angle of macroscopic view not
With region crowd flow direction；
Finally, this method is applicable not only to mobile phone location data, can be also used for other crowd's mobile trajectory datas and such as goes out
Hire a car data, bus card-reading data etc., therefore this method has huge potentiality in the big data research of track.
【Brief description of the drawings】
Fig. 1 a are that the crowd flow direction that the present invention defines calculates schematic diagram.
Fig. 1 b are the weight calculation schematic diagrames that the present invention defines.
The main body flow direction that Fig. 2 is the Shenzhen 500m*500m grid crowds that the present invention generates applies example.
The main body flow direction that Fig. 3 is the Shenzhen 1000m*1000m grid crowds that the present invention generates applies example.
The main body flow direction that Fig. 4 is the Shenzhen 2000m*2000m grid crowds that the present invention generates applies example.
The main body flow direction that Fig. 5 is the Shenzhen 4000m*4000m grid crowds that the present invention generates applies example.
【Embodiment】
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only the part of the embodiment of the present invention, rather than whole embodiments.Base
Embodiment in the present invention, those of ordinary skill in the art obtained under the premise of creative work is not made it is all its
His embodiment, belongs to the scope of protection of the invention.
For a certain position, resident trip direction is indefinite, and the trip flow on each direction is also different
, first with the angle of due east rotate counterclockwise come weigh crowd go on a journey direction value, then with going out on each direction in the position
Row flow averagely, obtains the Main way that crowd goes on a journey in this direction as weight to be weighted.The present invention using arrow come
The direction of visualization crowd's main body stream, the coordinate of arrow starting point are determined that emphasis coordinate is by every by the geometric center of base station in region
Contribution margin sum of crowd's flow in main body flow direction determines on individual direction.The last present invention uses the regular grid of different scale
To be integrated to crowd's stream, obtain crowd's main body in the multi-level different spaces in city and flow to.
Therefore, method of the invention comprises the following steps：
1) crowd's moving direction is calculated, flow and direction are moved according to crowd to propose a certain main stream of position crowd flowing
To Computing Principle；
2) visable representation of definition region crowd main body flow direction, i.e. start position, final position and arrow length；
3) the more crowd's bulk flow directions of rule-based grid carry out multi-level space scale calculating.
The multi-level main body of Urban population of the present invention flows to generation method, and based on mobile phone location data, it is applied to mobile phone
Colony's track data identifies flowing mostly to for city different zones crowd, including three steps, is described in detail as follows：
(1) calculating of crowd's main body flow direction
Data in mobile phone positions by base station, thus the OD that is flowed in city of crowd be using the position of base station as
Standard, for single base station, the crowd in its service range can flow to many places in city, therefore single O (starting point) comes
Multiple D (terminal) be present in lecture.The definition that crowd goes out line direction here first is to be started from due east to telegoniometer counterclockwise
Calculate 0 ° to 360 °.O (x are assumed as shown in Figure 1aO,yO) it is start position, D (xD,yD) it is final position, formula can be usedOD pairs of angle is calculated, and crowd's flow value between OD pairs is designated as wi, for single position
Multiple (θ be present in speechi,wi), make improvements and add to obtain below equation using crowd's flow value between position as weight：
Wherein θmFor the weighted average of the locality, n represents that base station OD to quantity, thus calculates the base station
The flow direction of main body stream, obtain the Main way of crowd's stream of single base station.
Based on the flow direction of main body stream, the weight size of main body stream is further calculated, the weight of main body stream is equally by each OD
To weight try to achieve, as shown in Figure 1 b, dotted arrow be formula (1) calculate base station body stream direction average θm,It is by θmWith
θiTry to achieve jointly, as angle of the direction average with OD to direction, then：As θiDirection is to θmThe weight contribution in direction
Value is projection value, and all contribution margins are summed, that is, obtain θmThe weight in direction, formula are as follows：
wmAs the weight size of main body stream, the value can reflect the size of main body person who lives in exile group's flow.
With reference to formula (1) and (2), crowd's main body flow vector of single position can be obtained, by all positions in city
Crowd's flow vector obtains the main body flow direction that can obtain diverse location crowd in city.
(2) expression of regional body flow direction
The main method for expressing for introducing region crowd's main body flow vector of this part, represents people from region using arrow here
The main body flow direction of group, the Coordinate calculation method of arrow beginning and end is given below：
Starting point coordinate：Starting point is also the average geometric central point s in regionr, it is assumed that the base station s in certain region r, region r1,
s2,L sn, then srCoordinate,
Terminal point coordinate：Terminal point coordinate is calculated according to the direction θ and weight w of main body stream, and weight w is by formula (2)
Determine, i.e.,K is constant parameter, primarily to the length of control arrow；Due to
Main body stream weight w is considered when seeking terminal, therefore the length of direction arrow can represent the size of crowd's flow.
(3) calculating of multi-level crowd's main body flow direction
The present invention is using grid as spatial analysis unit, the size k of the initial mesh length of side0It is set as 500m, hereafter often
One grade grid length of side size is 2 times of the upper grade grid length of side, and the determination formula of grid length of side size is as follows：
kn=k0·2n-1Wherein n=1,2L n (3)
K in above formulanFor the length of side size of n-th grade of grid.
The synthesis of multi-level crowd's main body flow direction refers to draw whole city using the grid of different space scales
Point, from carefully to multi-level city grid is slightly built, then crowd's flow vector field between each layer of mesh region is carried out comprehensive
Close, finally obtain each mesh region crowd main body flow direction.Mesh region crowd's stream refers to that the grid flows to other mesh regions
Crowd's stream, the flowing of crowd inside the grid need to be excluded, its essence is the OD to crowd's flow is each to being integrated to obtain
Crowd's flow OD between level mesh region, synthesis here refer to the comprehensive lattice on the basis of last layer time crowd moves OD matrixes
The internal crowd's flowing of net, the crowd obtained between the level grid flow OD matrixes.Aggregative formula is as follows：
WhereinOD set is flowed for n-th grade of crowd,OD set, OD are flowed for n-1 crowdnFor after n-th grade of yardstick increases
OD set of the beginning and end of crowd's stream in same mesh region, in same area after increasing this eliminates space scale
Crowd's flowing inside domain, with the continuous increase of space scale, the crowd's stream being integrated into increases, and only surplus displacement compares
Crowd's stream between big mesh region, the combined process of crowd's stream OD matrixes will be specifically introduced below.
IfBetween base station OD to set, andBe i-th layer of base station OD to set, be need to carry out it is comprehensive
Conjunction can just obtain, and wherein m is the number of plies,For setElement；RiFor i-th layer of regional ensemble,For Ri
Element, n is number of regions, for known range constraint condition；For i-th layer of interregional flow OD,It is by last layer
OD gathersWith constraints RiIntegrated to obtain,BySynthesis obtains, and detailed process is as follows：
1) fromMiddle one OD pairs of extractionAnd by its fromRemove, i.e.,
2) set R is traveled throughiMiddle region, if there is regionMeetAndIllustrate OD starting and terminal point
In the same area, then return to step 1) continue executing with；
, will if 3) step 2) is falseIt is added to setIn, continue executing with step 1) untilMiddle member
Element is all traversed, and is thus gathered
4)OD of base station for i-th layer, it is necessary to comprehensive into OD pairs of flow grid, ifWithFor two difference of the layer
Grid, only need to be fromIn pick outAndOD pairs, to its flow summation i.e. can obtain gridArrivePeople
Group's flowIt is added to set
5) region adfluxion is obtained to closeAfterwards, can be obtained by flowing to Computing Principle using the main body of formula (1) and (2) by i-th layer
Each grid in crowd main body flow direction.
To method proposed by the present invention, it is tested using the workaday mobile phone location data in Shenzhen, used
Amount about 16,000,000, the track of user is built first, morning 07 is extracted from track:00-09:00 Urban population moves OD squares
Battle array is used as input data, selects the period to can be seen that flowing mostly to for commuting time in morning city different zones crowd.Using 4
The grid of individual grade divides to whole city respectively, and initial mesh size is 500m*500m, then by being moved to crowd
Dynamic OD matrixes are integrated, and calculating Urban population main body under each grade grid yardstick respectively using the algorithm of proposition flows to, figure
2- Fig. 5 sets forth this four grade grid crowd, and commuting time flows mostly in the morning, is reflected from microcosmic to macroscopic view
The direction character of city different spaces region crowd flowing, can clearly find out Urban Marginal Areas crowd to city from Fig. 4
Internal flow.The region of red collimation mark note is Enterprises of Futian District and the downtown of Luohu District in figure, it can be seen that is commuted in the morning
Time, the crowd of peripheral region mostly flowed to central city, because the region is Shenzhen urban district busy areas, have accumulated depth
The substantial amounts of office building of ditch between fields, finance service, shopping square etc., substantial amounts of crowd pours in the region in the morning.These result tables
Understand that the algorithm is generating the feasibility of multi-level crowd's main body flow direction.
Described above is the preferred embodiment of the present invention, passes through described above content, the related work of the art
Personnel can carry out various improvement and replacement on the premise of without departing from the technology of the present invention principle, and these improve and replaced
It should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of multi-level main body of Urban population flows to generation method, it is characterised in that comprises the following steps：
Step a) calculates crowd's moving direction, moves flow and direction according to crowd and is flowed mostly to calculate crowd's flowing；
The arrow visualizing expression of step b) definition region crowds main body flow direction, i.e. start position, final position and arrow length
Degree；
The rule-based grid of step c), realize multi-layer body flow direction generation of crowd's bulk flow direction on Spatial Dimension.
2. the multi-level main body of Urban population as claimed in claim 1 flows to generation method, it is characterised in that：In the step a)
Calculating crowd's moving direction is specially：
To be started from due east to 0 ° to 360 ° of direction calculating counterclockwise, calculate what its crowd moved by beginning and end coordinate
Direction value；Using a certain communication base station as starting point, each pair OD direction and flow are spilt out with the base station, using flow as weight,
The main body flow direction for averagely, calculating base station crowd is weighted to its direction.
3. the multi-level main body of Urban population as claimed in claim 2 flows to generation method, it is characterised in that：In the step a)
The calculating of flow weight size is specially：Flow weight is tried to achieve by each OD pairs of weight, by calculating each OD streams in main body
Contribution margin in flow direction, then summation obtain the weight size of the main body stream.
4. the multi-level main body of Urban population as claimed in claim 1 flows to generation method, it is characterised in that：In the step b)
Region crowd's main body flows to arrow visualizing expression, specifically includes geometric center of the arrow start position for base station location in region
Coordinate, arrow final position calculates according to the direction θ and weight w of main body stream, and introduces control parameter to control arrow
Length.
5. the multi-level main body of Urban population as claimed in claim 1 flows to generation method, it is characterised in that：In the step c)
In the determination of multi-level regional extent be specially：
Using grid as regional extent, whole city is divided using the grid of different space scales, from carefully to thick
Multi-level city grid is built, then crowd's OD streams between each layer of region are calculated, crosses and excludes single region
Internal crowd flows OD, obtains the crowd OD streams between the level regions and region.
6. the multi-level main body of Urban population as claimed in claim 5 flows to generation method, it is characterised in that：The initial mesh length of side
Size be set as 500m, hereafter each grade grid length of side size is 2 times of the upper grade grid length of side, grid length of side size
Determination formula it is as follows：
kn=k0·2n-1Wherein n=1,2L n (3)
K in above formulanFor the length of side size of n-th grade of grid.
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