CN116205768A - Public transportation occupancy coverage analysis method based on multi-source data - Google Patents
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Abstract
The invention discloses a public transportation occupancy coverage analysis method based on multi-source data, which comprises the following steps. (1) Identifying the population number and employment post of the base station range according to the mobile phone signaling data; (2) Acquiring land division data and building area data of a target area, further subdividing the land into covered grids, and distributing population and post numbers into grids intersecting the land; (3) Calculating real-time path points from each grid centroid point to the public transportation site according to public transportation site data and path planning API data of the target area; (4) Calculating a three-dimensional path distance by combining the elevation data of the target area DEM; (5) And setting a coverage distance threshold, screening mass center point elements in the threshold, calculating living population and employment posts covered by public transportation, and calculating occupancy coverage rate. The invention provides a tool foundation for analysis of public service facility distribution, urban road network shape design and the like based on multi-source data.
Description
Technical Field
The invention relates to a public transportation occupancy coverage analysis method based on multi-source data, and belongs to the technical field of urban planning and urban transportation systems.
Background
The current mode of calculating the public traffic coverage rate is based on site space buffer zone calculation, which is proposed on the premise of ideal square road network and calculated based on the covered land area. Although the calculation mode has certain convenience and adaptability in the past, the national advocates 'common wealth' and 'common service popularization' nowadays, and the actual living and employment population covered by the public transport system cannot be really calculated through the thinking-back static land space coverage measurement. Taking the covered population proportion as a main index, singapore predicts that 80% of the households will live within 10 minutes of the walking distance of the subway station in 2030. Public transportation is used as an important way for constructing a public transportation city, and the stress is required to promote social fairness and realize equal development opportunities, so that a new public transportation occupancy coverage rate calculation method based on population and post distribution is required.
Furthermore, the traditional site buffer calculation process is based on either an ideal Euclidean distance or a cost matrix of the existing OD road network, and does not consider the dynamic change of traffic flow in the actual road network operation. And at present, a travel real-time path can be acquired by means of an electronic map path planning API, so that the method and the device are more in line with actual conditions. However, the distance of the OD returned directly through the path planning API is a space projection distance based on a coordinate system, the influence of factors such as elevation and the like is not considered, and a certain error exists. Therefore, the secondary treatment is required to be carried out in the GIS platform by combining the path points of the three-dimensional travel distance which is more fit with the actual cognitive travel distance of residents,
disclosure of Invention
The invention aims to solve the technical problems that: the traditional site buffer zone-based calculation method is simple and rough, coverage calculation is not carried out for the occupancy population, the actual road network is not based, and the influence of the actual elevation is not considered.
In order to solve the technical problems, the invention provides a multi-source data-based analysis method for the occupancy coverage of public transportation (including subways), which uses mobile phone signaling and Internet open platform data, combines DEM data and building land data, accurately and efficiently calculates the occupancy coverage of public transportation sites, and provides a tool foundation for analysis of public service facility distribution, urban road network shape design and the like.
The technical scheme adopted for solving the technical problems is as follows: a public transportation coverage rate analysis method based on multi-source data, which comprises the following steps:
s1, acquiring mobile phone base station data of a target area, dividing Thiessen polygons with a base station as a center to serve as a mobile phone base station range, and identifying the population number and employment post of the base station range;
s2, obtaining land division data and building area data of a target area, counting the total building area of each residential land and non-residential land, distributing population and post numbers to the residential land and the non-residential land, and calculating the residential population and employment post numbers of each land block;
s3, carrying out covering type grid division on the target area, distributing population and post numbers into grids intersecting with the land, and calculating living population and employment post numbers of each grid area;
s4, calculating real-time path points from each grid centroid point to the public transportation site according to public transportation site data of the target area and path planning API data;
s5, calculating a three-dimensional path distance by combining the elevation data of the target area DEM;
s6, setting a coverage distance threshold, screening mass center point elements in the threshold, calculating residence population and employment posts covered by public transportation, and calculating occupancy coverage rate.
The application is further configured to: the step S2 includes the following steps:
s11, cleaning noise data such as ping-pong data, drifting data and the like in the mobile phone signaling data;
s12, setting a residence population and employment position judging rule, determining a one-to-one correspondence relation between base stations and mobile phone signaling data according to base station codes, and counting residence population and employment position quantity information of the base stations;
the application is further configured to: in step S12, considering that some users will choose to shut down, it is determined that if the distance between the base station that received the signal last in the previous day and the base station that starts to receive the signal the next day is less than 800m, the information is also included in the population information;
the application is further configured to: the step S2 includes the following steps:
s21, carrying out space association on land division data and building area data, associating the building area data to the land division data, and summarizing the building areas to obtain total building area data of each land;
s22, counting total building areas of living land and non-living land in the range of the base station, wherein the total building areas comprise the following two conditions:
(1) If the residential or non-residential land is fully contained within the service range of the base station, then fully incorporating the residential or non-residential land building area into the building area serviced by the base station;
(2) If the residential or non-residential land intersects with the range serviced by the base station but is not fully contained, then the building area of the intersection of the two is referred to as the building area serviced by the base station;
s23, population and post numbers are distributed to living land and non-living land covered by the base station, and living population and employment post numbers of each land are calculated;
the calculation formula is as follows;
wherein n is the residence population or employment post of a certain plot, and m is the number of plots which are intersected with the service area of the base station and are resident or non-resident; n (N) i A residence population or employment post for a certain base station i; r is R i A total building area of a living land or a total building area of a non-living land of a certain base station i; r is (r) i Is the building area of a living or non-living land.
The application is further configured to: the step S3 includes the following steps:
s31, performing covered grid division on the target area, wherein the shape and the size of each grid are the same;
s32, overlapping the grid area and the land division area according to space positions, distributing population and post numbers into grids intersecting with the land, and calculating living population and employment post numbers of each grid area, wherein the two conditions are as follows;
(1) If a certain grid completely contains a certain land block, the population and the post of the land block are all distributed into the grid;
(2) If a certain grid boundary contains a part of a certain land block, only the population and the post numbers of the intersection part are recorded as the population and the post numbers of the grid;
the calculation formula is as follows:
wherein G is the resident population or employment post of a certain grid; k is the number of land parcels intersected with the grid; n is n j The living population or employment post of the land parcel j; s is the building area of the residential or non-residential land in the grid; s is S n Is the building area of the total residential or non-residential land of a certain land block.
If a plot is completely within a grid, s=s n 。
The application is further configured to: in step S4, the following steps are included:
s41, acquiring bus stop data of a target area;
s42, extracting centroid points of grids, taking the centroid point of each grid as a starting point, taking a public transportation site as an end point to establish an OD pair, acquiring specific longitude and latitude information, inputting the specific longitude and latitude information as part of parameters into an API of electronic map walking path planning, acquiring real-time path points of the OD pair, and recording the real-time path points as a point set X= { (X) r ，Y r )，(X r ，Y 1 )，……(X m+n ，X m+n )，(X s ，Y s )}；
The application is further configured to: in step S42, the point coordinates input into the electronic map path planning API need to be converted into the coordinate system required by the map;
the application is further configured to: in step S5, the following steps are included:
s51, obtaining DEM data of a target area, performing geographic registration of ' ArcGIS ', extracting corresponding elevation values of all path points, and obtaining a point set X ' = { (X) containing the elevation data r ，Y r ，H r )，(X r ，，Y 1 ，H 1 )，……(X m+n ，X m+n ，H m+n )， (X s ，Y s ，H s ) -as shown in fig. 6;
s52, calculating a three-dimensional path distance, wherein a calculation formula is as follows;
wherein d n Is the actual path distance of the road network between two adjacent points; d is the actual path distance from the start point to the end point of the road network; h n An elevation value for a point; x is X n Longitude coordinates of a certain point; y is Y n Is the latitude coordinate of a certain point;
the application is further configured to: in step S51, before performing geographic registration, each path point needs to be converted from a geographic coordinate system to a projection coordinate system;
the application is further configured to: in step S6, the following steps are included:
s61, importing the acquired OD travel shortest distance data into an ArcGIS, and assigning values to each lattice according to the spatial relationship;
s62, performing space matching with the grid cells based on the dot matrix data to obtain travel shortest distance data of each grid cell;
s63, setting a distance threshold, screening grid elements in the threshold, and calculating residence population and employment posts covered by public transportation:
the calculation formula is as follows:
wherein p represents the residence population or employment post covered by the public transportation site; g j A resident population or employment post for a certain grid j; w represents a weight, w is 1 when the distance D of the grid centroid to the site is less than the covered distance threshold l, and is 0 otherwise.
S64, calculating the occupancy coverage rate of a certain area, wherein the calculation formula is as follows:
wherein s represents the occupancy population or employment coverage rate of public transportation sites in a certain area; p represents the residence population or employment post covered by the public transportation site; n (N) i For the residence population or employment post of a certain base station i. Wherein s represents the occupancy population or employment coverage rate of public transportation sites in a certain area;
compared with the prior art, the invention has the beneficial technical effects that:
the invention utilizes big data such as mobile phone signaling data, internet map data and the like, traditional DEM elevation data, building area measurement data and the like to construct a more accurate and more humanized calculation mode of the occupancy coverage rate of public transportation, and quantizes specific employment posts and living population covered by the public transportation. The new occupancy coverage rate calculation mode enriches the definition of the coverage rate of the bus station (including the subway), solves the problems of slow updating, time consuming and labor consuming of the traditional geographic data, and can provide a tool foundation for analysis of public service facility distribution, urban road network shape design and the like.
Drawings
FIG. 1 is a flow chart of a method for analyzing occupancy coverage of a public transportation site based on multi-source data according to the present invention.
FIG. 2 is a diagram of the process of calculating the number of plots living population and employment posts according to the present invention.
Fig. 3 is a plot of occupancy population distribution for each plot of land according to an embodiment of the present invention.
Fig. 4 is a employment post distribution diagram of each plot of land according to an embodiment of the invention.
Fig. 5 is a bus stop distribution layout diagram of an embodiment of the present invention.
FIG. 6 is a three-dimensional distance graph of the various path points in the OD pair acquired in the present invention.
Fig. 7 is a distribution diagram of the coverage occupancy population of a bus within 500m of an embodiment of the invention.
Fig. 8 is a distribution diagram of bus coverage employment within 500m of an embodiment of the present invention.
Detailed Description
Specific embodiments of the invention are described in further detail below with reference to the drawings and examples.
The invention designs a multi-source data-based public transportation site occupancy coverage analysis method, and an implementation flow chart of the method is shown in figure 1. Taking the analysis of the occupancy coverage of bus stops in Kunshan, jiangsu province as an example, the method specifically describes the occupancy coverage, and comprises the following steps:
firstly, acquiring mobile phone base station data of a target area, dividing Thiessen polygons with a base station as a center to serve as mobile phone base station service ranges, and defining unique numbers for the ranges respectively, wherein the unique numbers are 1, 2, 3, … and i.
In this example, the chinese mobile base station within the administrative scope of kunshan is divided into a total of 2602 study units, of which there are 974 study units covering the urban core area.
Step two, acquiring mobile phone signaling data of a target area, cleaning the data, setting a residence population and employment post judgment rule of a base station range, and identifying the residence population and employment post of the base station range; see fig. 2.
In this example, after the redundant data, the ping-pong data, the drift data, and the like are cleaned, the daily effective data amount is 55820 ten thousand.
In the example, the user information which is monitored to stay at the average night of 1:00-7:00 is judged to be the living population information of the base station, and the living population information is judged to be a data set A; working day time 9:00-11:30 and 14:00-17:00 is set as a working time period, and the user information which is monitored and averagely connected with the base station for more than 3 hours per day is judged to be employment population information of the base station and is a data set B;
in this example, for a user who selects the evening shutdown, the distance between the base station which receives the signal last in the previous day and the base station which starts to receive the signal the next day needs to be determined, if the distance is less than 800m, the living population information of the base station which receives the signal last in the previous day is determined;
in this example, the residence population identified based on the mobile phone signaling data is about 40 ten thousand people, and the employment posts are about 21 ten thousand.
Thirdly, carrying out space association on land division data and building area data, associating the building area data to the land division data, and summarizing the building area to obtain total building area data of each land; the total building area of the residential land and the non-residential land within the range of the base station is counted as a data set C and a data set D respectively, and the method comprises the following two cases:
(1) If the residential or non-residential land is fully contained within the service range of the base station, then fully incorporating the residential or non-residential land building area into the building area serviced by the base station;
(2) If the residential or non-residential land intersects with the range serviced by the base station but is not fully contained, then the building area of the intersection of the two is referred to as the building area serviced by the base station;
the population and the post numbers are distributed to the living land and the non-living land covered by the base station, and the living population and the employment post number of each land are calculated and respectively recorded as a data set E and a data set F;
the calculation formula is as follows;
wherein n is the residence population or employment post of a certain plot, and m is the number of plots which are intersected with the service area of the base station and are resident or non-resident; n (N) i A residence population or employment post for a certain base station i; r is R i A total building area of a living land or a total building area of a non-living land of a certain base station i; r is (r) i Is the building area of a living or non-living land.
In this example, the distribution of the population number and the employment post number allocated to each plot is shown in fig. 3 and 4;
in practical application, the third step comprises the following steps;
in this example, building area data is correlated to the land partition data by tools such as ArcGIS intersection, identification, etc. The area of construction of the residential land in the core area of Kunshan is about 2154 hectares, and the area of construction of the non-residential land (except for park greenbelts, road facilities, etc.) is about 2651 hectares;
fourth, carrying out covered grid division on the target area, wherein the shape and the size of each grid are the same, overlapping each grid area and the land division area according to the space position, distributing population and post numbers into grids intersected with the land, calculating living population and employment post numbers of each grid area, and recording the living population and employment post numbers as a data set G and a data set H, wherein the following two conditions are included;
(1) If a certain grid completely contains a certain land block, the population and the post of the land block are all distributed into the grid;
(2) If a certain grid boundary contains a part of a certain land block, only the population and the post numbers of the intersection part are recorded as the population and the post numbers of the grid;
the living population and employment post of each grid area are calculated according to the following formula:
wherein G is the resident population or employment post of a certain grid; k is the number of land parcels intersected with the grid; n is n j The living population or employment post of the land parcel j; s is the building area of the residential or non-residential land in the grid; s is S n Is the building area of the total residential or non-residential land of a certain land block.
If a plot is completely within a grid, s=s n 。
In this example, the target area is divided into a grid of 30m by 30 m.
Fifthly, acquiring public transportation site data of a target area; centroid points of each grid are extracted, the centroid including the population of residence and the number of employment posts for each grid. Establishing an OD pair by taking a centroid point of each land block as a starting point and a public transportation site as an end point, acquiring specific longitude and latitude information, inputting the specific longitude and latitude information as partial parameters into an API (application program interface) of the electronic map walking path planning, acquiring real-time path points of the OD pair, and marking the real-time path points as a point set X= { (X) r ，Y r )，(X r ，Y 1 )，……(X m+n ，X m+n )，(X s ，Y s )；
In this example, by using the Goldmap "search service API" as a keyword "public transportation stations" through the python programming, polygon search obtains 1497 public transportation stations in the 500m buffer of the research scope, 1197 public transportation stations in the research scope completely, and the distribution diagram of the public transportation stations in the research scope is shown in FIG. 5.
In practical application, path planning API interfaces provided by map companies such as hundred degrees, golds and the like can be used, but the conversion of the OD point data into a coordinate system corresponding to the API requirements is required to be ensured, and the returned JSON format commute data is directly accessed to obtain each real-time path point of the OD pair;
in this example, the longitude and latitude of the starting point and the ending point are converted into a Mars coordinate system and then input into an API of the electronic map walking path planning.
Sixth, obtaining DEM data of the target area and carrying out ArcGIS geographyRegistering, extracting corresponding elevation values of all path points to obtain a point set X' = { (X) containing elevation data r ，Y r ，H r )，(X r ，Y 1 ，H 1 )，……(X m+n ，X m+n ，H m+n )， (X s ，Y s ，H s ) -j }; calculating a three-dimensional path distance, wherein a calculation formula is as follows;
wherein d n Is the actual path distance of the road network between two adjacent points; d is the actual path distance from the start point to the end point of the road network; h n An elevation value for a point; x is X n Longitude coordinates of a certain point; y is Y n Is the latitude coordinate of a certain point;
in this example, the WGS geographic coordinate system needs to be converted into the projection coordinate system of UTM before geographic registration is performed;
and seventhly, importing the acquired OD travel shortest distance data into an ArcGIS, assigning values to each lattice according to the spatial relationship, and performing spatial matching with the grid cells based on the lattice data to obtain the travel shortest distance data of each grid cell. And setting a coverage distance threshold, screening mass center point elements in the threshold, calculating living population and employment posts covered by public transportation, and calculating occupancy coverage rate.
The public transportation covered living population and employment post have the following calculation formula:
wherein p represents the residence population or employment post covered by the public transportation site; g j A resident population or employment post for a certain grid j; w represents a weight, w is 1 when the distance D of the grid centroid to the site is less than the covered distance threshold l, and is 0 otherwise.
S64, calculating the occupancy coverage rate of a certain area, wherein the calculation formula is as follows:
wherein s represents the occupancy population or employment coverage rate of public transportation sites in a certain area; p represents the residence population or employment post covered by the public transportation site; n (N) i For the residence population or employment post of a certain base station i. Wherein s represents the occupancy population or employment coverage rate of public transportation sites in a certain area;
in this example, the lattice data is associated to the grid cell elements through the ArcGIS space-position-based connection function; in this example, the distance threshold is set to be 300m, 400m, 500m, and the public transportation coverage residence population distribution and employment post distribution are shown in fig. 7 and 8;
in this example, as shown in Table 1, the 500 meter coverage of the core area of the Kunshan had approximately 77% of the resident population and the employment position coverage, about 58% of the 400 meter resident population coverage, 78% of the employment position coverage, 40% of the 300 meter resident population coverage, and 65% of the employment position coverage. Overall, the coverage of the core employment sites is greater than the coverage of the resident population.
TABLE 1 bus occupancy coverage of Kunshan core
There are many ways in which the invention may be practiced, and what has been described above is merely a preferred embodiment of the invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be comprehended within the scope of the present invention. The components not explicitly described in this embodiment can be implemented by using the prior art.
Claims (10)
1. The public transportation occupancy coverage rate analysis method based on the multi-source data is characterized by comprising the following steps of:
s1, acquiring mobile phone base station data of a target area, dividing Thiessen polygons with a base station as a center to serve as a mobile phone base station range, and identifying the population number and employment post of the base station range;
s2, obtaining land division data and building area data of a target area, counting the total building area of each residential land and non-residential land, distributing population and post numbers to the residential land and the non-residential land, and calculating the residential population and employment post numbers of each land block;
s3, carrying out covering type grid division on the target area, distributing population and post numbers into grids intersecting with the land, and calculating living population and employment post numbers of each grid area;
s4, calculating real-time path points from each grid centroid point to the public transportation site according to public transportation site data of the target area and path planning API data;
s5, calculating a three-dimensional path distance by combining the elevation data of the target area DEM;
s6, setting a coverage distance threshold, screening mass center point elements in the threshold, calculating residence population and employment posts covered by public transportation, and calculating occupancy coverage rate.
2. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S1 comprises the following steps:
s11, cleaning noise data in the mobile phone signaling data, including: ping-pong data, drifting data;
s12, setting a resident population and employment position judging rule, determining a one-to-one correspondence relation between the base stations and mobile phone signaling data according to base station codes, and counting resident population and employment position quantity information of the base stations.
3. The method according to claim 2, wherein in step S12, considering that some users will choose to turn off, it is determined that the information is included in the living population information as long as the distance between the base station that received the signal last in the previous day and the base station that starts to receive the signal the next day is less than 800 m.
4. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S2 comprises the following steps:
s21, carrying out space association on land division data and building area data, associating the building area data to the land division data, and summarizing the building areas to obtain total building area data of each land;
s22, counting total building areas of living land and non-living land in the range of the base station, wherein the total building areas comprise the following two conditions:
(1) If the residential or non-residential land is fully contained within the service range of the base station, then fully incorporating the residential or non-residential land building area into the building area serviced by the base station;
(2) If the residential or non-residential land intersects with the range serviced by the base station but is not fully contained, then the building area of the intersection of the two is referred to as the building area serviced by the base station;
s23, population and post numbers are distributed to living land and non-living land covered by the base station, living population and employment post numbers of each land are calculated, and a calculation formula is shown as follows;
wherein n is the residence population or employment post of a certain plot, and m is the number of plots which are intersected with the service area of the base station and are resident or non-resident; n (N) i A residence population or employment post for a certain base station i; r is R i A total building area of a living land or a total building area of a non-living land of a certain base station i; r is (r) i Is the total building area of a living or non-living land.
5. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S3 comprises the following steps:
s31, performing covered grid division on the target area, wherein the shape and the size of each grid are the same;
s32, overlapping the grid area and the land division area according to space positions, distributing population and post numbers into grids intersecting with the land, and calculating living population and employment post numbers of each grid area, wherein the two conditions are as follows;
(1) If a certain grid completely contains a certain land block, the population and the post of the land block are all distributed into the grid;
(2) If a certain grid boundary contains a part of a certain land block, only the population and the post numbers of the intersection part are recorded as the population and the post numbers of the grid;
the calculation formula is as follows:
wherein G is the resident population or employment post of a certain grid; k is the number of land parcels intersected with the grid; n is n j The living population or employment post of the land parcel j; s isBuilding area of residential or non-residential land within the grid; s is S n Building area for the total residential or non-residential land of a certain plot;
if a plot is completely within a grid, s=s n 。
6. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S4 comprises the following steps:
s41, acquiring bus stop data of a target area;
s42, extracting centroid points of grids, taking the centroid point of each grid as a starting point, taking a public transportation site as an end point to establish an OD pair, acquiring specific longitude and latitude information, inputting the specific longitude and latitude information as part of parameters into an API of electronic map walking path planning, acquiring real-time path points of the OD pair, and recording the real-time path points as a point set X= { (X) r ，Y r )，(X r ，Y 1 )，……(X m+n ，X m+n )，(X s ，Y s )}。
7. The method according to claim 1, wherein in step S42, the coordinates of the points in the input electronic map path planning API are converted into the coordinate system required by the map.
8. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S5 comprises the steps of:
s51, acquiring DEM data of a target area, performing ArcGIS geographic registration, extracting corresponding elevation values of all path points, and obtaining a point set X' = { (X) containing the elevation data r ，Y r ，H r )，(X 1 ，Y 1 ，H 1 )，......(X m+n ，Y m+n ，，H m+n )，(X s ，Y s ，H s )}；
S52, calculating a three-dimensional path distance, wherein a calculation formula is as follows;
wherein d n Is the actual path distance of the road network between two adjacent points; d is the actual path distance from the start point to the end point of the road network; h n An elevation value for a point; x is X n Longitude coordinates of a certain point; y is Y n Is the latitude coordinate of a certain point.
9. The method according to claim 8, wherein in step S5, each route point is converted into a projection coordinate system.
10. The method for analyzing the coverage rate of public transportation occupancy based on multi-source data according to claim 1, wherein the step S6 comprises the steps of:
s61, importing the acquired OD travel shortest distance data into an ArcGIS, and assigning values to each lattice according to the spatial relationship;
s62, performing space matching with the grid cells based on the dot matrix data to obtain travel shortest distance data of each grid cell;
s63, setting a distance threshold, screening grid elements in the threshold, calculating residence population and employment posts covered by public transportation,
the calculation formula is as follows:
wherein p represents the residence population or employment post covered by the public transportation site; g j A resident population or employment post for a certain grid j; w represents a weight, and when the distance D from the centroid of the grid to the site is smaller than the covered distance threshold value l, w is 1, otherwise, w is 0;
s64, calculating the occupancy coverage rate of a certain area, wherein the calculation formula is as follows:
wherein s represents the occupancy population or employment coverage rate of public transportation sites in a certain area; n (N) i For the residence population or employment post of a certain base station i.
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