CN110502567B - Theme-oriented urban rail transit station hierarchy POI extraction method - Google Patents

Theme-oriented urban rail transit station hierarchy POI extraction method Download PDF

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CN110502567B
CN110502567B CN201910681682.XA CN201910681682A CN110502567B CN 110502567 B CN110502567 B CN 110502567B CN 201910681682 A CN201910681682 A CN 201910681682A CN 110502567 B CN110502567 B CN 110502567B
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才智
孙功玉
苏醒
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Abstract

The invention discloses a theme-oriented method for extracting POI (Point of interest) of urban rail transit station hierarchy. The POI mainly refers to entities such as schools, banks and the like which are closely related to daily life, city activities and urban land use can be well described, and in order to know land utilization conditions and passenger flow conditions around a site, a POI set in a coverage area of an urban rail transit site needs to be determined; then, extracting a POI set of related subjects in the site according to the specified subject words and the POI set in the site coverage area; and finally, extracting the hierarchy POI of the site according to the POI set of the related subject in the site based on the skyline idea.

Description

Theme-oriented urban rail transit station hierarchy POI extraction method
Technical Field
The invention belongs to the field of data mining, and relates to a method for extracting a theme-oriented urban rail transit station level POI (point of interest). The characteristics of the station are mined according to the land characteristics around the station, and basis can be provided for urban traffic management and regulation departments to put forward guiding opinions, make a rail traffic operation organization scheme and plan urban land utilization.
Background
With the rapid development of urbanization in China, large cities generally face the outstanding problems of traffic jam, low land use efficiency, environmental deterioration and the like, and the mutual research of urban land optimal configuration and urban public transport is more and more emphasized. By virtue of the advantages of high speed, low pollution, safety, punctuality and the like, urban rail transit planning becomes important content of comprehensive traffic planning of various large cities, and gradually becomes a main direction for development of public transportation of more large and medium-sized cities. Urban land utilization refers to the spatial distribution of urban functional categories such as residential areas, industry, commercial areas, urban greenbelts, and the like. Urban rail transit and land use are in a causal relationship, the land use along the line is pulled by the construction of the traffic facilities, and the change of the travel activities of people is brought by the change of the land use, so that the traffic is induced, and the construction of the traffic facilities is promoted.
In the current research on the relationship between rail transit and land utilization, the influence of land utilization on the passenger flow of rail transit accounts for a high proportion. Pan et al studied the effect of TOD (traffic Oriented development) -based rail transit stations on station daily traffic using Integrated Circuit (IC) card data for subway traffic and cellular signal data for spatial distribution of the population in Shanghai cities. The study concluded the following: (1) the passenger capacity is positively correlated with the employment density and the resident commuting distance near the station. (2) The opening time is earlier and the station as the transfer node has positive correlation with the passenger traffic. (3) The subway station with better business development with the periphery tends to have higher passenger capacity. Li et al analyzed the correlation between land utilization in great prosperity areas in Beijing and passenger flow in subway stations through multivariate data and field survey. Land utilization in the range of 1000m has a great correlation with subway station passenger flow characteristics. Land use in the range of 200 meters around the station has the greatest impact on passenger traffic. The traffic station has great influence on the passenger flow, and the residential deviation of the passenger flow at the station is easily improved.
In the current stage of urban rail transit research, passenger flow prediction based on AFC data is mainly carried out, for example, Wangshi and the like analyze rail transit networked operation passenger flow characteristics from the aspects of space, time distribution and the like of passenger flow on the basis of IC card data of a Beijing urban rail transit system; 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. Very few studies have focused on the utilization of spatial data for urban rail transit and land utilization.
However, the big reason for people to take urban rail transit daily is to visit the environment around the station, and the spatial data has a great influence on the characteristics of the station. Therefore, the invention extracts the characteristics of the site based on the spatial data, which involves the retrieval of the spatial data. Skyline query is a typical multi-objective optimization problem and can be well used in spatial data retrieval. Therefore, the method searches the spatial data based on the skyline idea, and further analyzes the site. Some basic concepts involved in skyline queries are briefly introduced below.
(1) Dominating: point a dominates point B if and only if a is less than or equal to B's corresponding axis coordinate value, and not all.
(2) skyline point: given set P ═ P1,p2,...,pn}, skyline point is a subset of P and any point P that belongs to skyline pointiAre not dominated by other points in the set P.
Disclosure of Invention
The invention aims to provide a theme-oriented method for extracting POI (Point of interest) from a hierarchy of urban rail transit stations. The POI mainly refers to entities such as schools, banks and the like which are closely related to daily life, city activities and urban land use can be well described, and in order to know land utilization conditions and passenger flow conditions around a site, a POI set in a coverage area of an urban rail transit site needs to be determined; then, extracting a POI set of related subjects in the site according to the specified subject words and the POI set in the site coverage area; and finally, extracting the hierarchy POI of the site according to the POI set of the related subject in the site based on the skyline idea.
In order to achieve the above object, the technical solution adopted by the present invention is a topic-oriented method for extracting POI from urban rail transit station hierarchy, and before the specific implementation of the method is introduced, in order to better understand the method proposed by the present invention, two definitions related in the present invention are introduced first.
Given a set of rail transit Stations in a city, states ═ s1,s2,...,si,...,snThe POI set POI of the city is { p }1,p2,...,pj,...,pmWhere m is much larger than n.
Definition 1: urban rail transit station coverage area: the coverage area of the station refers to an area containing urban rail transit stations, and the distance from all points in the area to the station is smaller than that of any other station. Based on the aboveDefinition, urban rail transit station(s)i) Can be expressed as Cov(s)i)＝{(lng1 i,lat1 i),(lng2 i,lat2 i),...,(lngt i,latt i) ,., wherein (lng)t i,latt i) Is the t-th boundary point of the coverage area. All boundary points constitute the closed coverage area. Urban rail transit station(s)i) The set of POIs within the coverage area can be represented as Pi＝{p1 i,p2 i,...,pk i,., wherein pk i∈POIs。
Definition 2: urban rail transit station hierarchy POI: the hierarchy POI of the urban rail transit site refers to a hierarchy POI extracted from a coverage area of the urban rail transit site based on the interest degree of a user in the POI. Urban rail transit station(s)i) The hierarchy POI of (a) may be expressed as Hi ═ h1 i,h2 i,...,hl i,., wherein hl iRepresenting urban rail transit stations(s)i) And h is a layer I POI setl i＝{p1,p2,...,pj,., wherein
Figure BDA0002144924500000031
Figure BDA0002144924500000032
Determining a set of POIs (P) within a coverage area of an urban rail transit sitei) Comprises the following steps:
the method comprises the following steps: and determining the coverage area of the urban rail transit station. The coverage area of a station refers to an area containing urban rail transit stations, and the distance from the station to all points in the area is smaller than the distance from any other station (see definition 1). The main purpose of riding urban rail transit is to visit places around a site, such as a mall, a company, a home, and the like. Therefore, when traveling, the station selected for riding is always closest to the place to be visited. The Voronoi diagram is a subdivision of the whole space where the space target is located according to the nearest neighbor principle of the target, and has the following characteristics:
(1) each polygon (region) contains only one discrete target point;
(2) the distance from a point in the polygon to the corresponding discrete target point is shortest;
(3) the distances from points on the polygon edge to the discrete target points on either side of the edge are equal.
Therefore, the method adopts the Voronoi diagram to determine the coverage area of the urban rail transit station. The whole space is the whole city coverage area, the whole space is divided into the coverage areas of the urban rail transit stations by using the Voronoi diagram, and the coverage area of each urban rail transit station in the city can be obtained.
Step two: having obtained the coverage area of each site, then for site siWe need to determine the set of POIs (P) within the coverage areai). In the present invention, the following method is used to determine a POI point (p)j) Whether or not within the site coverage area. First of all, passing through pjAnd (4) making a ray along any direction, and then calculating the number of the intersection points of the ray and the site coverage area. If the point has an odd number of intersections with the area, the point is within the coverage area; if the point has an even number of intersections with the coverage area, the point is outside the area.
Has obtained site siPOI set (P) within a coverage areai) POI set (P) according to topic word (topic) and site coverage areai) POI set (P) for extracting relevant topics within a site coverage areaiCopic) comprises the following steps:
the method comprises the following steps: and extracting a POI set of related topics in the site coverage area according to the topic words and the POI set of the site coverage area. In order to obtain a POI set of related topics in a site coverage area, similarity calculation can be performed on POI points in the POI set in the site coverage area of the urban rail transit and queried topics, and then POI with high similarity is extracted. Since the POI has semantic information, the jaccard phase can be usedSimilarity coefficients are used for measuring the similarity between the POI points and the query subject. Calculating P by jaccard similarity equationiOne POI point (p) in (1)k i) And the similarity between the subject words, as described below.
Figure BDA0002144924500000041
Set of POIs (P) having obtained relevant topics within the site coverage areaiTopic) extracting site-level POIs (H) from a set of POIs of related subject matter within the site coverage areai) The method comprises the following steps:
the method comprises the following steps: and acquiring a skyline point set according to the POI set of the related subject in the coverage area of the site. When passengers visit POIs around an urban rail transit site, they may be based on a variety of decision considerations, such as the distance between the POI entity and the urban rail transit site, the score of the POI entity, and the like. These decisions need to be taken into account in order to layer POIs according to the degree of interest of the user. Skyline queries are a typical problem for multi-decision optimization. Using skyline queries, POIs that are better than other points under multi-decision conditions can be queried. Therefore, POI in the coverage area of the urban rail transit station is extracted according to the hierarchy based on the skyline idea. This allows POIs of equal interest to the user to be assigned to the same tier under multi-decision conditions.
Step two: and (5) extracting POI (point of interest) at a site level. Taking the skyline point set obtained in the step one as the first layer (h) of the hierarchy POIl i) Then from PiThese skyline points are deleted in topic. Then from the rest PiContinuously using the method of step one in _topicto extract skyline point as the second layer (h) of the hierarchy POI2 i). So as to reach PiCopic is empty.
Compared with the existing research, the research on urban rail transit at the present stage is mainly passenger flow prediction based on AFC data, and little research focuses on the research on urban rail transit and land utilization by utilizing spatial data. The invention uses the spatial data, and the land utilization condition of the adjacent area of the site is more concerned.
Drawings
Fig. 1 is a schematic view of a Voronoi partition site.
Fig. 2 is a flowchart of hierarchical POI extraction.
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 urban rail transit stations and all POI sets in Beijing as examples, the specific implementation mode of the invention is as follows:
first, a set of POIs within a coverage area of an urban rail transit site is determined.
The method comprises the following steps: first, regarding the Beijing urban administrative district as the whole space, and dividing the whole space into coverage areas of various urban rail transit stations by using a Voronoi diagram according to the space information of the urban rail transit stations. The resulting Voronoi diagram is shown in fig. 1. Wherein, the smaller points represent urban rail transit stations, the polygon in which each urban rail transit station (the smaller points) is located is the coverage area of the station, and the larger points forming the polygon are boundary points.
Step two: having obtained the coverage areas of the various sites, we next need to determine, for each site, a set of POIs within the coverage area.
The entire process of determining a set of POIs within the coverage area of an urban rail transit site is described in Algorithm 1.
Figure BDA0002144924500000051
And then, extracting a POI set of related subjects in the site according to the specified subject words and the POI set in the site coverage area.
The method comprises the following steps: calculating POI points (p) in a POI set in a coverage areak i) Similarity to the topic word (topic).
For site siSuppose a rootObtaining the POI set P of the site according to the previous stepsiThe university of Beijing, university of college students, Yuanming garden ancient park, Hai lake district of Beijing primary school of experiment }. Let topic be university, calculated according to equation 1, PiThe similarity of each POI to the subject term in table 1 is shown.
TABLE 1 site siSimilarity of POI and topic ═ university' in coverage area
Figure BDA0002144924500000052
Figure BDA0002144924500000061
Assuming that the POI points with the similarity larger than 0 are designated as the POIs of the relevant subjects, the POI set P of the relevant subjects of the sitei_topic ═ experimental elementary school in the Shang Dynasty district of Beijing university of sports, university of college students, Beijing City.
POI set (P) based on previously derived related topicsiCopic). If all POIs in the area are used directly to measure the characteristics of the site, the result becomes meaningless, which is not desirable in the present invention. So POI will be extracted layer by layer, with k representative POIs extracted per layer. The specific implementation steps for extracting the hierarchy POI by using the skyline idea are as follows:
POI set (P) according to related topics within a site coverage areaiTpic) extract site hierarchy POI (H)i). A flow chart of this step is shown in fig. 2. For urban rail transit station siSuppose that the related subject POI set P in the coverage area of the site is obtained according to the previous stepsiI _, c, d, e, f, g, h, i, k, l, m. First, from PiSelecting skyline point set h from _ topicl iAnd { a, h, i } as the first level of the hierarchy POI. Then, h is mixedl iPOI in (1) from PiDeleted in topic, when P isiP, { b, c, d, e, f, g, k, l, m }, andiselecting skyline Point from _ topicSet h2 iB, g, l as the second layer of the hierarchy POI. Then, h is mixed2 iPOI in (1) from PiDeleted in topic, when P isiC, d, e, f, k, m, and from PiSelecting skyline point set h from _ topic3 iAnd { c, e, f, m } as the third level of the hierarchy POI. Finally, h is3 iPOI in (1) from PiDeleted in topic, when P isiFrom P, _ topic ═ d, g }, andiselecting skyline point set h from _ topic4 iAnd { d, g } as the fourth layer of the hierarchy POI. Now PiTopic is empty and the traversal stops. Hierarchy POI (H) of the sitei) Is { hl i,h2 i,h3 i,h4 i}. The hierarchical POI extraction results are shown in table 2.
Table 2 hierarchical POI extraction results
Number of layers The layer of POI collections
First layer a,h,i
Second layer b,g,l
Third layer c,e,f,m
The fourth layer d,g
The entire process of extracting hierarchical POIs based on skyline ideas is described in Algorithm 2.
Figure BDA0002144924500000062
Figure BDA0002144924500000071

Claims (1)

1. A method for extracting POI of urban rail transit station hierarchy facing to subject is characterized in that,
determining POI set P in coverage area of urban rail transit stationiComprises the following steps:
the method comprises the following steps: determining a coverage area of an urban rail transit station; the coverage area of the station refers to an area containing urban rail transit stations, and the distance from all points in the area to the station is smaller than that of any other station; the main purpose of riding urban rail transit is to visit markets, companies and home places around a station; therefore, when traveling, the station selected to be taken is always closest to the place to be visited; the Voronoi diagram is a subdivision of the whole space where the space target is located according to the nearest neighbor principle of the target, and has the following characteristics:
(1) each polygonal area contains only one discrete target point;
(2) the distance from a point in the polygon to the corresponding discrete target point is shortest;
(3) the distances from points on the polygonal edge to the discrete target points on either side of the edge are equal;
determining the coverage area of the urban rail transit station by adopting a Voronoi diagram; the whole space is a whole city coverage area, and is divided into coverage areas of urban rail transit stations by using a Voronoi diagram to obtain the coverage area of each urban rail transit station in a city;
step two: having obtained the coverage area of each site, then for site siNeed to ensurePOI set P in fixed coverage areai(ii) a Is used below to determine a POI point pjWhether it is within the site coverage area; first of all, passing through pjMaking rays in any direction, and then calculating the number of intersection points of the rays and a station coverage area; if the point has an odd number of intersections with the area, the point is within the coverage area; if the point has an even number of intersections with the coverage area, the point is outside the area;
has obtained site siPOI set P within a coverage areaiPOI set P according to subject term topic and site coverage areaiPOI set P for extracting related subject in site coverage areaiTopic comprises the following steps:
the method comprises the following steps: extracting a POI set of related subjects in the site coverage area according to the subject words and the POI set of the site coverage area; in order to obtain a POI set of related topics in a site coverage area, similarity calculation is carried out on POI points in the POI set in the site coverage area of the urban rail transit and queried topics, and then POI with high similarity is extracted; since the POI has semantic information, similarity between the POI point and a query subject is measured by using a jaccard similarity coefficient; calculating P by jaccard similarity equationiOne POI point p ink iAnd the similarity between the subject term, as described below;
Figure FDA0002901743060000011
POI set P with relevant topics within the site coverage areaiA topic, extracting H in the POI of the site hierarchy according to the POI set of the related subject in the coverage area of the siteiThe method comprises the following steps:
the method comprises the following steps: acquiring a skyline point set according to a POI set of a relevant subject in a site coverage area; when a passenger accesses POI around the urban rail transit station, the distance between a POI entity and the urban rail transit station and the score of the POI entity are considered; in order to layer POIs according to the degree of interest of the user, the decisions need to be comprehensively considered; querying POIs which are superior to other points under the condition of multi-decision by using skyline query; extracting POI in the coverage area of the urban rail transit station according to the hierarchy based on the skyline idea; in this way, under the condition of multi-decision, POI which is equally interesting to the user are distributed to the same layer;
step two: extracting POI (point of interest) of a site hierarchy; taking the skyline point set obtained in the step one as a first layer h of the hierarchy POIl iThen from PiDeleting the skyline points in topic; then from the rest PiContinuously using the method of the step one in _topicto extract skyline point as a second layer h of the hierarchy POI2 i(ii) a So as to reach PiCopic is empty.
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