CN109408615B - Method for extracting top-k POIs from site based on diversity and equal proportionality of bounded region - Google Patents
Method for extracting top-k POIs from site based on diversity and equal proportionality of bounded region Download PDFInfo
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Abstract
The invention discloses a method for extracting top-k POIs from a site based on the diversity and the equal proportionality of a bounded area. And extracting top-k from POI in the site, and providing two methods of diversity and equal proportionality based on two dimensions of space and semantics. The sites are classified according to the land features around the sites, and guidance opinions and the formulation of rail transit operation organization plans can be provided for urban traffic management and regulation departments as bases.
Description
Technical Field
The invention belongs to the field of text retrieval, and relates to a method for lifting top-k POI (point of interest) by a site based on the diversity and the equal proportionality of a bounded area.
Background
The urban rail transit is developed vigorously, so that the traveling efficiency of citizens is improved, the life quality is improved, the pressure of dense population and tight housing in urban centers can be relieved, the air pollution is improved, the greening area is small, and other common urban diseases are avoided. In China, a plurality of cities continuously accelerate the development of urban rail transit, and the scale of the established and planned lines is further enlarged.
The urban rail transit has large traffic volume, and meanwhile, the punctuality rate is higher, the traffic jam condition and the danger of traffic accidents do not exist, and a large number of passengers are attracted. Meanwhile, with the continuous development of rail transit, the surrounding land is paid more and more attention, so that the surrounding land is utilized to different degrees. Due to the development and utilization of the sections around the station, the development strength, the development property and the like of the land are obviously changed, which can bring great influence on the passenger flow of the rail.
The basis of urban rail transit operation is passenger flow characteristic analysis. Characteristics of passenger flow are researched and analyzed for scientifically and reasonably operating urban rail transit and meeting travel demands of residents. The passenger flow is regular and dynamic change, and the space and time distribution passenger flow of the passenger flow is researched, so that the most reasonable arrangement can be made on the operation of the rail transit in time, good service can be provided for passengers, and the cost can be saved to the greatest extent.
Currently, the main research direction is the prediction of passenger flow. Wanjing and the like analyze the characteristics of the rail transit networked operation passenger flow from the aspects of space, time distribution and the like of the passenger flow on the basis of IC card data of a rail transit system in Beijing. With the widespread application of machine learning methods, it is also the mainstream way to predict passenger flow by using machine learning algorithms. Liu Jianfeng and the like find that the Beijing railway traffic system presents obvious unbalanced characteristics in different time dimensions and area ranges by analyzing the fluctuation of station passenger flow. And clustering analysis is carried out according to the characteristics of the stations, the characteristic rules of the time-space distribution difference of the passenger flows of the stations with different land types at the periphery are summarized, and the characteristics of the passenger flows of the inbound stations, the passenger flows of the transfer stations and the passenger flow connection characteristics of the inbound and outbound stations are further analyzed.
In the aspect of the relationship between rail transit and land utilization, the Zhai-Zhiqiang introduces the necessity of using the peripheral land of urban rail transit stations by a development (SOD) mode using government to adjust the land service function as the guide and a development (TOD) mode using public transport to initially explore land development modes suitable for different types of stations. Through the systematic analysis of Shenzhen city track card swiping data and building general survey data, the relationship between the train station entrance and exit passenger flow volume and time distribution and peripheral land utilization is mastered, city update planning and long-term track network planning in the future year can be effectively guided through the research, and the traffic planning, city planning and land utilization planning can be more effectively integrated, so that the sustainable development of the track is driven, and new vitality is injected for the city development.
Disclosure of Invention
The invention aims to provide a method for lifting top-k POIs by a station based on the diversity and equal proportion characteristics of a bounded area. And extracting top-k from POI in the site, and providing two methods of diversity and equal proportionality based on two dimensions of space and semantics.
In order to achieve the purpose, the technical scheme adopted by the invention is a method for extracting top-k POIs (point of interest) from a station based on the diversity and the equal proportionality of a bounded region, and subway station is defined as s1,s2,…,sl}，POIs＝{p1, p2,…,pm}，RC-tree＝{R1(c1),R2(c2),Rl(cl)}，ciIs node RiThe color of (c). The method for dividing the POI area by the RC-tree comprises the following steps:
the method comprises the following steps: because the object space is divided according to the range by the R-tree, when a high-dimensional space is required to be queried, only pointers contained in a few leaf nodes need to be traversed, and whether data pointed by the pointers meet requirements or not is checked. It is exactly this that meets the requirements of the storage space POI, so one R-tree is generated with all POI containing sites. But the R-tree is generated such that each leaf node contains at most one site.
Step two: having obtained an R-tree index containing all sites and POIs around the sites, the POIs whose influence ranges are to be divided for each site are obtained. The RC-tree contains the color of the site. The leaf nodes in the generated R-tree may contain one or zero sites, with all the leaf nodes containing sites first painted a different color.
Step three: for leaf nodes containing POI only, i.e. RiThe following three rules are used to handle different situations in order to partition POIs into the area of a site.
(1) If R isiOnly one sibling node contains a site, then RiPainted as the color of the sibling node;
(2) if R isiIf there are multiple siblings containing sites, then calculate the distance between POI of Ri and the site in the siblings, and RiIs painted as a brother containing the nearest siteThe color of the node.
(3) If R isiAll nodes of (2) do not contain sites, then pass through RiThe ancestors of (1) search leaf nodes of other branches, and combine RiAnd the color of the leaf node most closely related to the color is coated.
And finally, coating the parent node with color according to the color of the leaf node.
The method for lifting top-k POIs by the site based on the diversity of the bounded area comprises the following steps:
the method comprises the following steps: the standard goal in measuring diversity is to maximize the sum of the differences between nodes. And extracting top-kPOI by using a diversity method based on semantics and space. Since the features of similarity and diversity are orthogonal to each other, the semantic diversity representation of POIs, Divsem, is computed by their semantic similarity representation, simmem, which is described below.
Where num ═ eQue | represents the number of POIs extracted, pjIs an extracted POI, piIs a candidate POI, Simmem (,) is pjAnd piSemantic similarity between them.
The similarity of the two POIs is calculated by the Jaccard similarity equation, described below.
Step two: bounded area spatial diversity refers to the distribution of positions of POIs relative to a site, and includes two main factors: the direction of the POI to the site and the distance between the POI and the site. The idea of directional diversity of POIs is that candidate POIs are attenuated, i.e. attenuated with p, by the extracted POIsjP in the same directioni. The calculation of the directional diversity of POIs is described below.
The method proposes a normalized adjustment ratio B that attenuates POIs far from s in the range of (0,1) in view of the distance of the stations.
The computation of the spatial diversity of the bounded region is described as follows.
Divspa_border(pi)＝B*Divspa(pi) (5)
Step three: after calculating the semantic and spatial diversity of the POI, the two values are combined by adding the coefficient β. The coefficient β adjusts the effect of different values on the final value of the POI. The final diversity value of the POI is calculated as follows.
The method for lifting top-k POIs by the station based on the proportional characteristic of the bounded area comprises the following steps:
the method comprises the following steps: the equal proportion is that the corresponding proportion quantity is selected for each category according to the proportion of the quantity of each category in the total number. The properties and diversity of equal proportions are exactly the opposite. The top-kPOI is extracted by an equal proportion method based on both the geographic space and semantic text. And in the aspect of semantic texts, weakening the weight according to the proportion of the number of the category labels of the POI to the total number. Because the value of k is small, in order to cover different sets in equal proportion, an increment selection strategy is used; i.e. in each iteration, a different node is added, which is beneficial to some degree of loop selection. The semantic based equal-scale property is described below.
Wherein type (p)i) Represents piType (p) ofi) Represents a certain type (p)i) Number of (2), ext (type (p)i) Is piType (p) ofi) The number of times to be added to the selected queue; α is a constant, and the adjustment ratio α is 2. When a POI in a certain genre is extracted, the proportion of the genre is significantly reduced so as to increase the chance of selecting other types of POIs.
Step two: for the spatial scale, it is the density of the directions of the sites where the POI is located. In each direction, a corresponding number of POIs is extracted. To divide the different directions, the concept of the same direction is described as follows. The bounded area is divided into sectors or quadrants in most cases, centered on the site, and POIs in the same sector are considered to be in the same direction.
The location of the station is not always located at the center of the bounded area. In this case, the spatial scale of the POI is calculated as follows:
step three: similar to the calculation of the final value of diversity, the final value of the equal proportion characteristic is calculated by adding the coefficients β, and the calculation formula is as follows:
Pro(pi)＝β*Prospa_border(pi)+(1-β)*Prosem(pi) (9)
drawings
FIG. 1 is a schematic leaf node coloring diagram of a site.
FIG. 2 is a schematic diagram of an RC-tree.
Fig. 3 is a schematic diagram of a subway station.
Detailed Description
In order to make the objects, technical solutions and features of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. Taking all POI and subway stations in Beijing as examples, the specific implementation mode of the invention is as follows:
the method comprises the following steps: firstly, generating an R-tree by all POI and sites in Beijing city, wherein the generated R-tree is required to satisfy that each leaf node comprises at most one site. To extract the POIs of the area affected and covered by each site, the leaf node dimension of the generated R-tree should be determined to be the minimum density that cannot contain two sites. If two sites are still in the same leaf node when the leaf node contains 200 POIs, the size of the leaf node should be 199 so that each site can be in one leaf node. I.e., each site is in a leaf node, some leaf nodes may not contain a site.
Step two: each leaf node containing a site is painted with one color, as shown in fig. 1, R7, R10, R11, R13 each contain sites painted with green, purple, blue and orange, respectively.
Step three: if R isiOnly one sibling node contains a site, then RiIs painted as the color of the sibling node. Then R8 and R9 are painted green in fig. 1 and only R7 contains the site, and similarly R14 and R15 are painted the color of R13 (orange).
If R isiIf there are multiple siblings containing sites, then calculate the distance between POI of Ri and the site in the siblings, and RiPainted with a color that contains the sibling of the nearest site. R10 and R11 both contain sites, then the distances of R12 from R10 and R11, dis (R10, R11) and dis (R10, R12) are calculated, assuming dis (R10, R11)<dis (R10, R12), then R12 is painted blue.
If R isiAll nodes of (2) do not contain sites, then pass through RiThe ancestors of (1) search leaf nodes of other branches, and combine RiAnd the color of the leaf node most closely related to the color is coated. The branches R16, R17 and R18 do not contain sites, so that the brother branch R6 is searched, and the whole branch R6 is painted orange if only R13 in R5 contains sites.
Step four: and D, according to the method in the third step, painting layer by layer, and painting the whole R-tree with color to generate the RC-tree. The resulting RC-tree is shown in FIG. 2.
Algorithm 1 describes the entire process of partitioning POIs into regions based on an RC-tree.
The invention introduces a specific implementation mode of extracting top-kPOI by a diversity method in detail. And obtaining the RC-tree according to the step four, namely l areas. If the similarity between sites is calculated directly from all POIs in the area, the result becomes meaningless, and the similarity between all sites is high, which is not desirable in the present invention. So k representative POIs will be extracted and not considered for those points where there is no meaning, the frequency of appearance is too high (e.g. public toilets, chain of fast food restaurants, etc.). The specific implementation steps for extracting top-kPOI by using the diversity method are as follows:
the method comprises the following steps: and calculating the distance between the station and the POIs in each area, finding the POI closest to the station, recording the POI as top-1, and putting the POI into a queue eQue.
Step two: equation 3 does not consider the boundary case, but the diversity according to the direction is weakened, as shown in FIG. 3, s1，s2And s3Representing a subway station, the station is at the center of the area in (a) of fig. 3, the station is at the boundary of the area in 3(b) of fig. 3, and the POIs are concentrated and distributed at the other side, and the station is at the boundary of the area in 3(c) of fig. 3 and is adjacent to the POIs. POIs ═ p1,p2,p3,p4,p5,p6Let us assume the coordinate s in the figure1＝(10,10)，p1＝(9,12)， p2＝(16,17)，p3＝(4,3)，p4＝(16,4)，p5＝(19,5)，p6＝(18,2)，s1And p1The distance between them is denoted d(s)1, p1) Then d(s)1,p1)＝2.236，d(s1,p2)＝9.2195，d(s1,p3)＝9.2195，d(s1,p4)＝8.485，d(s1, p5)＝10.296，d(s1,p6)＝11.314。
P closest to the site1As top-1, then p1Adding an eQue queue, wherein k is 1; computing
(i is 2,3,4,5,6), and if the maximum value is obtained, p is6As top-2, then there is p in the eQue queue1And p6K is 2; top-3 is selected based on the calculation
P as the maximum value of (i ═ 2,3,4,5)3. Continuing with this equation, the sequence of 3(a) top-6 is p1->p6->p3->p2->p4 ->p5. In the figure p1Station and p4，p6On the same straight line, due to p4And p6Is relatively close to p1Has a large difference in distance, so p is preferentially selected when selecting top-26。
The sequence obtained according to equation 3 is only attenuated in direction, but POIs far from the site have little influence on the site, so the present invention proposes to normalize the adjustment ratio B to attenuate POIs in distance. The arrangement in fig. 3 is shown in table 1 using equations 3 and 5. The ranking results of formula 3 and formula 5 in table 1 have obvious changes, and the ranking result of formula 5 is more desirable.
Table 1 fig. 3 ordering according to equation 3 and equation 5
| Drawing (A) | Formula (II) | Sorting |
| FIG. 3(a) | Equation 3 | p1->p6->p3->p2->p4->p5 |
| FIG. 3(a) | Equation 5 | p1->p4->p3->p2->p5->p6 |
| FIG. 3(b) | Equation 3 | p1->p6->p3->p2->p5->p4 |
| FIG. 3(b) | Equation 5 | p1->p3->p2->p4->p5->p6 |
| FIG. 3(c) | Equation 3 | p6->p2->p3->p5->p1->p4 |
| FIG. 3(c) | Equation 5 | p6->p5->p4->p3->p1->p2 |
The whole process of the diversity-based top-kPOI extraction method is described in algorithm 2.
Claims (1)
1. A method for extracting top-k POIs from a site based on the diversity and the equal proportionality of a bounded region is characterized by comprising the following steps: defining station of subway as s1,s2,…,sl}，POIs＝{p1,p2,…,pm}，RC-tree＝{R1(c1),R2(c2),Rl(cl)}，ciIs node RiThe color of (a); the method for dividing the POI area by the RC-tree comprises the following steps:
step 11: because the R-tree divides the object space according to the range, when a high-dimensional space is required to be queried, only pointers contained in a few leaf nodes need to be traversed, and whether data pointed by the pointers meet requirements or not is checked; the POI storage space requirement is met, so that all POI containing sites generate an R-tree; but the generated R-tree needs to satisfy that each leaf node comprises at most one site;
step 12: obtaining an R-tree index containing all sites and POIs around the sites, and then dividing the POIs with the influence ranges for each site; the RC-tree contains the color of the site; the leaf nodes in the generated R-tree may contain one or zero sites, and the leaf nodes of all the sites are painted with different colors;
step 13: for leaf nodes containing POI only, i.e. RiThe following three rules are used to handle different situations in order to partition the POI into the area of the site;
(1) if R isiOnly one sibling node contains a site, then RiPainted as the color of the sibling node;
(2) if R isiCalculating POI and POI of Ri if a plurality of brother nodes comprise sitesDistance between sites in sibling nodes, and RiPainted as a color containing the sibling of the nearest site;
(3) if R isiAll nodes of (2) do not contain sites, then pass through RiThe ancestors of (1) search leaf nodes of other branches, and combine RiCoating the color of the leaf node most closely related to the color;
finally, according to the color of the leaf node, the father node is painted with color;
the method for lifting top-k POIs by the site based on the diversity of the bounded area comprises the following steps:
step 21: the standard goal for measuring diversity is to maximize the sum of differences between nodes; extracting top-kPOI by a diversity method based on semantics and space; since the features of similarity and diversity are orthogonal to each other, the semantic diversity representation of POIs, Divsem, is computed by their semantic similarity representation, simmem, which is described below;
where num ═ eQue | represents the number of POIs extracted, pjIs an extracted POI, piIs a candidate POI, Simmem (.,) is pjAnd piSemantic similarity between them;
calculating the similarity of the two POIs through the Jaccard similarity equation, which is described below;
step 22: bounded area spatial diversity refers to the distribution of positions of POIs relative to a site, and includes two main factors: the direction from the POI to the site and the distance between the POI and the site; the idea of directional diversity of POIs is that candidate POIs are attenuated, i.e. attenuated with p, by the extracted POIsjP in the same directioni(ii) a The calculation of the directional diversity of POIs is described below;
a normalized adjustment ratio B is proposed, which attenuates POIs far from s in consideration of the distance of the stations in the range of (0, 1);
the computation of the spatial diversity of the bounded region is described below;
Divspa-border(pi)＝B*Divspa(pi) (5)
step 23: after calculating the semantic and spatial diversity of the POI, combining the two values by adding the coefficient β; adjusting the influence of different values on the final value of the POI by the coefficient beta; the final diversity value of the POI is calculated as follows;
the method for lifting top-k POIs by the station based on the proportional characteristic of the bounded area comprises the following steps:
step 31: the equal proportion is that the corresponding proportion quantity is selected for each category according to the proportion of the quantity of each category in the total number; the properties and diversity of equal proportions are exactly the opposite; the top-kPOI is extracted by an equal proportion method and is also based on two aspects of geographic space and semantic text; in the aspect of semantic texts, weight weakening is carried out according to the proportion of the number of the category labels of the POI to the total number; because the value of k is small, in order to cover different sets in equal proportion, an increment selection strategy is used; namely, in each iteration, a different node is added, which is beneficial to certain degree of cycle selection; the semantic-based isometric property is described below;
wherein type (p)i) Represents piType (p) ofi) Represents a certain type (p)i) Number of (2), ext (type (p)i) Is piType (p) ofi) The number of times to be added to the selected queue; α is a constant, and the adjustment ratio α is 2; when POI in a certain category is extracted, the proportion of the category is obviously weakened so as to increase the opportunity of selecting POI in other categories;
step 32: for the space proportion, the density of the direction of the site where the POI is located is obtained; extracting a corresponding number of POIs in each direction; in order to divide the different directions, the concept of the same direction is described as follows; with the site as the center, the bounded area is divided into sectors or quadrants in most cases, and POIs in the same sector are considered to be in the same direction;
the location of the site is not always located at the center of the bounded area; in this case, the spatial scale of the POI is calculated as follows:
step 33: similar to the calculation of the final value of diversity, the final value of the equal proportion characteristic is calculated by adding the coefficients β, and the calculation formula is as follows:
Pro(pi)＝β*Prospa_border(pi)+(1-β)*Prosem(pi) (9)。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107656987A (en) * | 2017-09-13 | 2018-02-02 | 大连理工大学 | A kind of subway station function method for digging based on LDA models |
| CN107704524A (en) * | 2017-09-13 | 2018-02-16 | 大连理工大学 | A kind of subway station function method for digging based on doc2vec |
| CN108053240A (en) * | 2017-12-11 | 2018-05-18 | 北京奇虎科技有限公司 | Generate the method and device that Ads on Vehicles launches public bus network scheme |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107656987A (en) * | 2017-09-13 | 2018-02-02 | 大连理工大学 | A kind of subway station function method for digging based on LDA models |
| CN107704524A (en) * | 2017-09-13 | 2018-02-16 | 大连理工大学 | A kind of subway station function method for digging based on doc2vec |
| CN108053240A (en) * | 2017-12-11 | 2018-05-18 | 北京奇虎科技有限公司 | Generate the method and device that Ads on Vehicles launches public bus network scheme |
Non-Patent Citations (2)
| Title |
|---|
| 中小城市城乡公交站点的筛选方法研究;严海等;《交通运输研究》;20170228;第54-59页 * |
| 基于精细化用地的轨道客流直接估计模型;王淑伟等;《交通运输系统工程与信息》;20150630;第37-43页 * |
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