CN113298301A - Urban service facility evaluation and optimization method based on crowd digital portrait - Google Patents
Urban service facility evaluation and optimization method based on crowd digital portrait Download PDFInfo
- Publication number
- CN113298301A CN113298301A CN202110532450.5A CN202110532450A CN113298301A CN 113298301 A CN113298301 A CN 113298301A CN 202110532450 A CN202110532450 A CN 202110532450A CN 113298301 A CN113298301 A CN 113298301A
- Authority
- CN
- China
- Prior art keywords
- service
- user
- facility
- daily
- daily service
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000011156 evaluation Methods 0.000 title claims abstract description 27
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 238000012937 correction Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 230000002093 peripheral effect Effects 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000007726 management method Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 3
- 230000002123 temporal effect Effects 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 23
- 230000006399 behavior Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Databases & Information Systems (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Mobile Radio Communication Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a city service facility evaluation and optimization method based on crowd digital portrait, which comprises the steps of inputting and correcting high-precision mobile phone positioning data, identifying daily service users and living points of the service facilities by overlapping with vector data such as land, road network and the like according to spatial and temporal attributes of a stop point, delimiting the service range of the service facilities, evaluating the layout condition of the facilities based on the 15-minute service crowd coverage rate index of the service facilities, if the layout needs to be adjusted, realizing automatic selection and optimization of a service facility layout adjustment scheme by software simulation and calculation of the adjustment scheme, accurately identifying the real service crowd and the use rule of the service facilities by utilizing the high-precision mobile phone positioning data characteristic, realizing rational evaluation and optimization adjustment of the spatial layout by methods such as index measure evaluation, software simulation calculation and the like, the evaluation and optimization method is more scientific.
Description
Technical Field
The invention belongs to the technical field of urban planning, and particularly relates to an urban service facility evaluation and optimization method based on a crowd digital portrait.
Background
With the gradual development of the urban planning towards refinement and humanization, higher requirements are put forward on the layout configuration of urban daily service facilities closely related to the lives of citizens. The daily service facility is a facility type meeting the daily basic life requirements of urban residents, and has great influence on convenience, quality feeling and happiness of the lives of the residents.
As a new community development concept, the '15-minute life circle' requires that various daily service facilities such as clothes, eating and housing can be conveniently used within the range of 15-minute walking reach, including types of business, education, culture, medical treatment, health and the like. In addition to guidance for planning new cities, along with continuous and deep city updating, the current daily service facility layout needs to be evaluated from the perspective of accessibility of residents on the principle of 'gradual progress, adjustment according to local conditions and classification and policy' and is adjusted and optimized aiming at facilities with unreasonable spatial layout so as to define specific updating tasks and gradually improve service facility allocation.
Currently, an evaluation method for daily service facility layout mainly uses residential districts as starting points and service facilities as destinations, calculates a straight line distance or a road network travel distance based on vector space data, and determines whether the service facility layout is reasonable and whether the layout can meet the requirement of comfortable walking of surrounding residents by taking comparison standards such as a comfortable walking distance of 1000m as references. The service facility layout evaluation method has certain limitation, and more ideal calculation is performed from the perspective of static vector data. Firstly, the method of taking a community or a residential district as a starting point ignores the difference of individual living positions, and subjectively delimits service crowds of service facilities, but cannot accurately identify real crowds using the service facilities; secondly, the physical space distance of both the straight line distance and the road network travel distance in an ideal state cannot completely reflect the travel characteristics of different crowds in actual conditions, the individual difference in the crowd activities is blurred, the time standard of 15 minutes is converted into the distance by using the unified walking speed, the method is applied to the evaluation of the service facility space layout, the method is contradictory to the characteristic that whether the walking is comfortable or not can be better reflected by the time scale provided by the '15-minute living circle', the short board of the evaluation method can be effectively compensated by using the mobile phone positioning data to develop the digital portrait of the crowds, and the accuracy and the scientificity of the evaluation are improved.
Disclosure of Invention
Aiming at the problems, the invention provides an urban service facility evaluation and optimization method based on a crowd digital portrait, which fully utilizes the characteristics of high-precision mobile phone positioning data to accurately depict the digital portrait of the service crowd, accurately identifies the real service crowd and the use rule of the service facility, realizes the rational evaluation and optimization adjustment of the spatial layout of the service facility through measures such as index measure evaluation, software simulation analysis and the like, and provides a scientific method for the layout evaluation and optimization of urban daily service facilities. Aiming at the problems that the service crowd is subjectively planned by the existing evaluation method, the distance is simply calculated by means of static vector data, the real condition and the demand of the crowd are ignored, the real service crowd of the daily service facility is identified by mobile phone positioning data, a travel track is constructed, the actual travel time and the travel distance are calculated, the layout of the daily service facility is evaluated, and a subsequent adjustment scheme is optimized.
The purpose of the invention can be realized by the following technical scheme: a city service facility evaluation and optimization method based on a crowd digital portrait comprises the following steps:
the method comprises the following steps: data input and correction processing
And importing high-precision mobile phone positioning data of urban residents and vector data such as urban land, road network and the like through a data interface, and performing correction and superposition processing.
Step two: daily service user and residence point identification
Selecting an urban daily service facility, screening daily service users of the daily service facility, identifying living points of the daily service users, and constructing a daily service user sub-database E and a daily service user living point database H.
Step three: service facility layout present situation evaluation
Constructing a behavior track between the daily service users from the living point to the daily service facility, and calculating the average travel time of all the daily service users
Average travelling speed
And further calculating the crowd coverage rate A of the daily service facility within the 15-minute service range, judging whether the A is less than 80%, if so, judging that the facility service range exceeds the standard requirement of the daily service facility range, and adjusting the layout of the facility service range.
Step four: automatic selection optimization of service facility layout adjustment scheme
Selecting a plurality of facility positions for adjustment in the peripheral area of the original service facility, establishing a service facility layout optimization model, automatically calculating the crowd coverage rate in the 15-minute service range of the optimized daily service facility through the model, selecting the position with the coverage rate of more than 80 percent and the maximum position as an optimal adjustment scheme, and if the coverage rates are all less than 80 percent, adding the service facility in the area.
As a further scheme of the present invention, in the first step, high-precision mobile phone positioning data of urban residents and vector data of urban land, road network, etc. are imported through a data interface, and are subjected to correction and superposition processing, that is, high-precision mobile phone positioning data of an urban continuous week for 7 days is imported through the data interface, the import frequency is 1 minute, the data content includes a user number, positioning point time, longitude and latitude coordinate information, etc., vector data of the urban land, the road network, etc. are called from an urban planning management department, and the mobile phone positioning data and the data of the urban land, the road network, etc. are superposed through correction processing of spatial coordinates and elevations.
As a further scheme of the present invention, the step two of screening the daily service users of the daily service facilities, and constructing the daily service user sub-database E refers to selecting the urban daily service facilities, defining the land boundary thereof, identifying the user staying points within the land boundary of the service facilities, organizing the user staying points within the land boundary into a plurality of continuous staying point sets according to a time sequence, counting the user continuous staying point sets, and marking the time of the staying point when the user first enters the land boundary as T1The last dwell point within the right-of-way boundary is time-stamped as T2If T is2-T1If the user uses the daily service facility for 4 days or more in a week, the user is a daily service user of the service facility, and a daily service user sub-database E is constructed;
as a further scheme of the present invention, the identifying the living points of the daily service users in the second step means that the staying points of each daily service user in the boundary of the living site are extracted, the staying points between 0:00 and 6:00 are selected as the living points of the corresponding users, which are respectively marked as H1, H2 and H3 … …, and the living point database H of the daily service user is constructed and linked with the sub-database E of the daily service user.
As a further scheme of the invention, the step three is to construct the daily service user from the living point to the daily clothesThe behavior track between the service facilities refers to that the stop points of all the daily service users in the sub-database E are sequentially connected according to the time sequence, the obtained broken line adopts a polynomial difference method, namely, discrete points are fitted through a polynomial interpolation function curve to obtain a smooth curve to approximate the travel track of the users, and all the daily service users are obtained by intercepting from the living point HnTo the daily service facility.
As a further scheme of the present invention, in the third step, the average travel time of all daily service users is calculated
Average travelling speed
The length of a behavior track from a living point of a daily service user to the daily service facility is taken as a travel distance D from the user to the daily service facilitynCounting a plurality of travel tracks in one circle, and calculating corresponding travel time Tn(T1、T2、T3… …), travel distance Dn(D1、D2、D3… …) to obtain the speed V of each trip of the usern(V1, V2, V3 … …), calculating the average user travel speed by the following formula
Namely:
and N is the number of times that the daily service user goes to the daily service facility within one week.
As a further proposal of the invention, the daily service facility 15-minute service range is calculated in the third stepCrowd coverage, which refers to selecting all average travel times
Constructing a service facility daily use crowd sub-database B for 15 minutes at the crowd ID within 15 minutes, and calculating the crowd coverage rate A of the daily service facility within 15 minutes, namely:
wherein, B is the number of daily service users whose average travel time is less than or equal to 15 minutes when the user goes to the daily service facility, and E is the total number of the daily service users of the service facility.
As a further scheme of the present invention, the step four includes selecting a plurality of facility positions for adjustment in the peripheral area of the original service facility, where the plurality of facility positions for adjustment are selected in the peripheral area of the original service facility according to the land type, the current construction situation, and the relevant specification requirements such as "planning standard for urban public service facilities GB 50442", and the land type that can construct the service facility according to the specification requirements is selected in the peripheral area of the original service facility, and a land parcel in which a construction space exists is used as the adjustment position.
As a further aspect of the present invention, the step four of establishing a service facility layout optimization model, and automatically calculating the optimized crowd coverage rate in the 15-minute service range of the daily service facility through the model means that a daily service user living point database H, facility adjustment positions, road network vector data, etc. are imported into simulation software, a "new road network dataset" command is used, a road network model is established based on the road network vector data, an "OD cost matrix map layer creation" command is used to create an OD cost matrix model, all daily service user living points are loaded to a starting point, facility adjustment positions are loaded to a destination, and a "solution" command is used to calculate the shortest path length S from each user to the facility adjustment positionsnCalculating the shortest path length S between each user residence point and the adjustment positionnCalculating the output of each userLine time T'nShortest path length S corresponding to each usernAverage traveling speed corresponding to each user obtained in the third step
The ratio of (a) to (b), namely:
as a further scheme of the present invention, if the coverage rates in the fourth step are all less than 80%, the service facilities need to be added in the area, which means that if the coverage rates of the crowd in the 15 minute service range of each adjustment position are all less than 80%, the service facilities need to be added in the area, and on the basis of reserving the original facilities, the scheme of the newly added service facilities is selected from the facility adjustment positions selected in the previous step, which has the largest coverage rate.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
1. the time and frequency of the user actually using the service facilities are identified through superposition of the user mobile phone positioning data and the urban daily service facility land boundary, and the daily service user group of the daily service facilities is accurately defined, so that the method is more scientific and real compared with the traditional method defined by experience.
2. The method has the advantages that the daily time-space behaviors of the user are identified by the aid of the mobile phone positioning data, the real residence place of the user can be identified more accurately, and compared with a traditional mode that a plurality of residence communities in a certain range are used as residence places of service people, the distribution situation of the residence places of the service people can be reflected more accurately.
3. The crowd coverage rate of the 15-minute service range is calculated, the coverage condition of the 15-minute service range of the urban daily service facility is reflected in a quantitative mode, and compared with a traditional evaluation method, the method is more visual.
4. The method can accurately simulate and calculate the crowd coverage rate within the 15-minute service range of the adjustment scheme, automatically select the optimal layout scheme, improve the efficiency of planning and adjusting the service facilities and save manpower and material resources.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of the present service scope in the layout evaluation and optimization of a community cultural activity center in Nanjing of the present invention;
FIG. 3 is a schematic diagram of an adjustment scheme A in the layout evaluation and optimization of a community cultural activity center in Nanjing;
FIG. 4 is a schematic diagram of an adjustment scheme B in the layout evaluation and optimization of a community cultural activity center in Nanjing;
FIG. 5 is a schematic diagram of an adjustment scheme C in the layout evaluation and optimization of a community cultural activity center in Nanjing;
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings and the embodiment.
The technical scheme of the invention is explained in detail in the following by evaluating and optimizing the layout of a certain community culture activity center in the Jiangbei new district of Nanjing city based on mobile phone positioning data and accompanying drawings, and comprises the following steps:
(1) data input and correction processing
(1.1) importing high-precision mobile phone positioning data from 24 days 8 month in 2020 to 30 days 8 month in 2020, 8 month in 30 days in 8 month in all users in the Jiangbei new district in Nanjing through a data interface, wherein the importing frequency is once in 1 minute, the data content comprises a user number, a positioning point date, time and positioning point longitude and latitude coordinate information, 135.25 hundred million pieces of data are acquired in total, the specific data format requirements are shown in Table 1, wherein the user number is a desensitized user identification number and does not relate to privacy information of the users. Cleaning the mobile phone positioning data file, wherein abnormal values such as positioning point time, positioning point longitude and latitude and the like which exceed the range, repeated values of repeated updating time and missing data of partial items are deleted, and the format of the processed data is as follows:
TABLE 1 Mobile phone location data
(1.2) calling vector data such as Nanjing urban land, road network and the like from a Nanjing urban planning management department, superposing the mobile phone positioning data with the vector data such as the urban land, the road network and the like through correction processing of spatial coordinates and elevations, unifying coordinate systems of longitude and latitude information generation points of the positioning data with the vector data such as the urban land, the road network and the like, carrying out spatial alignment superposition, and carrying out attribute association on the positioning points and the numbers of the occupied land;
(2) daily service user and residence point identification
(2.1) selecting a certain community culture activity center in a JiangBei new region in Nanjing, defining the land boundary of the certain community culture activity center, identifying user stop points in the land boundary of the certain community culture activity center, and organizing the user stop points in the land boundary into a plurality of continuous stop point sets according to a time sequence;
(2.2) counting continuous stopping points of the users, and marking the time of the stopping points of the users entering the area range of the community cultural activity center for the first time as T1The last dwell point within the right-of-way boundary is time-stamped as T2If T is2-T1And when the time is more than or equal to 5 minutes, the user is indicated that the user uses the community culture activity center in the activity. Extracting the ID of the crowd using the community cultural activity center, respectively marking the ID as C1, C2 and C3 … …, and constructing a service facility user group database C;
(2.3) if the user uses the community culture activity center for 4 days or more in a week, the user is a daily service user of the service facility, and a daily service user sub-database E is constructed;
(2.4) selecting the daily service user sub-database E, extracting the stop points of each daily service user in the residential land boundary, selecting the stop point with the longest stop time between 0:00 and 6:00 as the residential site of the corresponding user, respectively marking as H1, H2 and H3 … …, constructing the daily service user residential point database H, and carrying out association and connection with the daily service user sub-database E.
(3) Service facility layout present situation evaluation
(3.1) sequentially connecting the staying points of all daily service users in the sub-database E according to a time sequence, and fitting the obtained broken line to discrete points by a polynomial difference method, namely fitting a polynomial interpolation function curve to obtain a smooth curve to approximate a user travel track;
(3.2) finding all daily service subscribers from the residential site HnThe behavior track from the user to the community cultural activity center, wherein the length of the behavior track is the travel distance D of the user to the community cultural activity centern(ii) a Simultaneously calculating the time difference T of the tripnAnd obtaining corresponding travel speed V according to the ratio of the travel distance to the travel timen. Counting a plurality of travel tracks in one week, and calculating corresponding travel time Tn(T1、T2、T3… …), travel distance Dn(D1、D2、D3… …) to obtain the speed V of each trip of the usern(V1, V2, V3 … …), calculating the average user travel speed by the following formula
Namely:
wherein N is the frequency of the daily service user going to the community cultural activity center within one week;
(3.3) selecting all the crowd IDs with the average travel time within 15 minutes, and constructing a crowd sub-database B for daily use of the service facility for 15 minutes;
(3.4) calculating the crowd coverage rate A of the 15-minute service range of the community cultural activity center, wherein 156 daily service users exist in the 15-minute service range of the community cultural activity center, 205 total daily service users exist, and the crowd coverage rate A of the 15-minute service range is 76.79%, as shown in FIG. 2.
(3.5) determining that the crowd coverage rate A in the 15-minute service range of the community cultural activity center is less than 80%, and the layout needs to be adjusted;
(4) and (3) automatically selecting and optimizing a service facility layout adjustment scheme:
(4.1) selecting 3 places of the plots A, B, C which have construction spaces and can be used as positions for adjustment, wherein the 3 places of the plots A, B, C can be used for constructing the community cultural activity center according to the land types, the current construction conditions, the urban public service facility planning standard GB50442 and other relevant standard requirements in the surrounding area of the community cultural activity center obtained in the step (3.8); as shown in the figures 3-5 of the drawings,
(4.2) importing daily service user living points, facility adjusting positions and road network vector data in Arcgis software, constructing a road network model based on the road network vector data by using a 'newly-built road network data set' command, constructing an OD Cost Matrix model by using a 'create OD Cost Matrix Layer' command, loading all daily service user living points to a starting point, loading the facility adjusting position points to a destination, using routes as impedances, and calculating the shortest route length S of each user to the facility adjusting positions by using a 'solving' commandnCalculating travel time T 'of each user'nShortest path length S corresponding to each usernAverage traveling speed corresponding to each user obtained in step 3
The ratio of (a) to (b), namely:
(4.3) calculating the crowd coverage rate of the 15-minute service range of each adjustment position, as shown in fig. 3-5, the crowd coverage rate of the 15-minute service range of position a in fig. 3 is 85.57%, the crowd coverage rate of the 15-minute service range of position B in fig. 4 is 76.87%, and the crowd coverage rate of the 15-minute service range of position C in fig. 5 is 82.24%, wherein the crowd coverage rate of the 15-minute service range of a is greater than 80%, and the coverage rate of the 15-minute service range of a is the highest among the three, so that the position a is the most suitable adjustment scheme.
It will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the spirit and scope of the invention, and any equivalents thereto, such as those skilled in the art, are intended to be embraced therein.
Claims (10)
1. A city service facility evaluation and optimization method based on a crowd digital portrait is characterized by comprising the following steps:
the method comprises the following steps: data input and correction processing
Importing high-precision mobile phone positioning data of urban residents and vector data of urban land, road network and the like through a data interface, and performing correction and superposition processing;
step two: daily service user and residence point identification
Selecting an urban daily service facility, screening daily service users of the daily service facility, identifying living points of the daily service users, and constructing a daily service user sub-database E and a daily service user living point database H;
step three: service facility layout present situation evaluation
Constructing a behavior track between the daily service users from the living point to the daily service facility, and calculating the average travel time of all the daily service users
Average travelling speed
Further calculating the crowd coverage rate A of the daily service facility within the 15-minute service range, judging whether the A is less than 80%, if so, judging that the facility service range exceeds the standard requirement of the daily service facility range, and adjusting the layout of the facility service range;
step four: automatic selection optimization of service facility layout adjustment scheme
Selecting a plurality of facility positions for adjustment in the peripheral area of the original service facility, establishing a service facility layout optimization model, automatically calculating the crowd coverage rate in the 15-minute service range of the optimized daily service facility through the model, selecting the position with the coverage rate of more than 80 percent and the maximum position as an optimal adjustment scheme, and if the coverage rates are all less than 80 percent, adding the service facility in the area.
2. The method as claimed in claim 1, wherein the step one includes importing high-precision mobile phone positioning data of urban residents and vector data of urban land, road network and the like through a data interface, and performing correction and superposition processing, wherein the importing frequency is 1 minute and one time, the data content includes user number, positioning point time, longitude and latitude coordinate information and the like, the vector data of urban land, road network and the like are retrieved from an urban planning management department, and the mobile phone positioning data is superposed with the data of urban land, road network and the like through correction processing of spatial coordinates and elevations.
3. The method as claimed in claim 1, wherein the step two is to select daily service users of the daily service facilities, construct the daily service user sub-database E by selecting the urban daily service facilities, defining land boundaries thereof, identify user stops within the land boundaries of the service facilities, and organize the user stops within the land boundaries into a plurality of continuous stops according to a time sequencePoint set, counting the continuous stop point set of the user, and marking the time of the stop point when the user firstly enters the boundary of the user as T1The last dwell point within the right-of-way boundary is time-stamped as T2If T is2-T1And if the user uses the daily service facility for 4 days or more in a week, the user is the daily service user of the service facility, and a daily service user sub-database E is constructed.
4. The method as claimed in claim 1, wherein the step two of identifying the residential points of the daily service users comprises extracting the residential points of each daily service user within the residential land boundary, selecting the residential points between 0:00 and 6:00 as the residential points of the corresponding users, respectively labeled as H1, H2 and H3 … …, constructing a daily service user residential point database H, and linking and hooking with the daily service user sub-database E.
5. The method as claimed in claim 1, wherein the step three of constructing the behavior track from the living point of the daily service user to the daily service facility means that the staying points of all the daily service users in the sub-database E are sequentially connected according to the time sequence, the obtained polyline is fitted to the discrete points by a polynomial interpolation function curve to obtain a smooth curve to approximate the user travel track, and the obtained line is intercepted to obtain the living point H of all the daily service users from the living point HnTo the daily service facility.
6. The method as claimed in claim 1, wherein the method comprises calculating average travel time of all users in daily service in the third step
Average travelling speed
The length of a behavior track from a living point of a daily service user to the daily service facility is taken as a travel distance D from the user to the daily service facilitynCounting a plurality of travel tracks in one circle, and calculating corresponding travel time Tn(T1、T2、T3… …), travel distance Dn(D1、D2、D3… …) to obtain the speed V of each trip of the usern(V1, V2, V3 … …), calculating the average user travel speed by the following formula
Namely:
and N is the number of times that the daily service user goes to the daily service facility within one week.
7. The method as claimed in claim 1, wherein the step three comprises calculating the crowd coverage rate of the daily service facility within 15 minutes of service range, that is, selecting all average travel time
Constructing a service facility daily use crowd sub-database B for 15 minutes at the crowd ID within 15 minutes, and calculating the crowd coverage rate A of the daily service facility within 15 minutes, namely:
wherein, B is the number of daily service users whose average travel time is less than or equal to 15 minutes when the user goes to the daily service facility, and E is the total number of the daily service users of the service facility.
8. The method for evaluating and optimizing urban service facilities based on digital crowd representation as claimed in claim 1, wherein the step four of selecting a plurality of facility positions for adjustment in the peripheral area of the original service facility means that a plurality of facility positions for adjustment are selected in the peripheral area of the original service facility according to land types, current construction conditions, and relevant standard requirements such as "planning standard for urban public service facilities" GB50442 ", and means that land types for construction of the service facility can be selected in the peripheral area of the original service facility according to the standard requirements, and a parcel with a construction space is used as the adjustment position.
9. The method as claimed in claim 1, wherein the step four is to create a service layout optimization model, and automatically calculate the optimized daily service coverage of the daily service facility for 15 minutes by using the model, which means to introduce a daily service user living point database H, facility adjustment positions, and road network vector data into the simulation software, to construct a road network model based on the road network vector data by using a "new road network dataset" command, to create an OD cost matrix map layer by using an "OD cost matrix map layer creation" command, to load all the daily service user living points to the starting point, to load the facility adjustment positions to the destination, and to calculate the shortest path length S from each user to the facility adjustment positions by using a "solving" commandnCalculating the shortest path length S between each user residence point and the adjustment positionnCalculating travel time T 'of each user'nShortest path length S corresponding to each usernAnd step (d)Thirdly, obtaining the average travelling speed corresponding to each user
The ratio of (a) to (b), namely:
10. the method as claimed in claim 1, wherein if the coverage is less than 80%, the service is required to be added in the area, and if the coverage of the adjustment position is less than 80%, the service is required to be added in the area, and on the basis of reserving the original service, the new service scheme is selected from the adjustment positions selected in the previous steps, the scheme with the largest coverage is selected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110532450.5A CN113298301B (en) | 2021-05-17 | 2021-05-17 | Urban service facility evaluation and optimization method based on crowd digital portraits |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110532450.5A CN113298301B (en) | 2021-05-17 | 2021-05-17 | Urban service facility evaluation and optimization method based on crowd digital portraits |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113298301A true CN113298301A (en) | 2021-08-24 |
| CN113298301B CN113298301B (en) | 2024-04-30 |
Family
ID=77322494
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110532450.5A Active CN113298301B (en) | 2021-05-17 | 2021-05-17 | Urban service facility evaluation and optimization method based on crowd digital portraits |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113298301B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703008A (en) * | 2023-08-02 | 2023-09-05 | 山东高速股份有限公司 | Traffic volume prediction method, equipment and medium for newly built highway |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886532A (en) * | 2014-03-24 | 2014-06-25 | 江苏省城市规划设计研究院 | Standardization test method for urban public facilities |
| CN110532337A (en) * | 2019-08-28 | 2019-12-03 | 北京市测绘设计研究院 | Communal facility service ability method for improving towards intelligence community |
| CN111385753A (en) * | 2019-10-24 | 2020-07-07 | 南京瑞栖智能交通技术产业研究院有限公司 | Medical facility accessibility evaluation method based on mobile phone signaling data |
| WO2020172954A1 (en) * | 2019-02-28 | 2020-09-03 | 东南大学 | Living circle identification method based on positioning data |
-
2021
- 2021-05-17 CN CN202110532450.5A patent/CN113298301B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103886532A (en) * | 2014-03-24 | 2014-06-25 | 江苏省城市规划设计研究院 | Standardization test method for urban public facilities |
| WO2020172954A1 (en) * | 2019-02-28 | 2020-09-03 | 东南大学 | Living circle identification method based on positioning data |
| CN110532337A (en) * | 2019-08-28 | 2019-12-03 | 北京市测绘设计研究院 | Communal facility service ability method for improving towards intelligence community |
| CN111385753A (en) * | 2019-10-24 | 2020-07-07 | 南京瑞栖智能交通技术产业研究院有限公司 | Medical facility accessibility evaluation method based on mobile phone signaling data |
Non-Patent Citations (1)
| Title |
|---|
| YI SHI 等: "Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data", 《GEO-INFORMATION》, 13 January 2020 (2020-01-13), pages 1 - 17 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116703008A (en) * | 2023-08-02 | 2023-09-05 | 山东高速股份有限公司 | Traffic volume prediction method, equipment and medium for newly built highway |
| CN116703008B (en) * | 2023-08-02 | 2023-10-31 | 山东高速股份有限公司 | Traffic volume prediction method, equipment and medium for newly built highway |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113298301B (en) | 2024-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhong et al. | Inferring building functions from a probabilistic model using public transportation data | |
| Wu et al. | Analysis and optimization of 15-minute community life circle based on supply and demand matching: A case study of Shanghai | |
| Biba et al. | A new method for determining the population with walking access to transit | |
| Gong et al. | Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement | |
| CN104254865A (en) | Empirical expert determination and question routing system and method | |
| Miller et al. | Geographic information systems for transportation in the 21st century | |
| CN106909692B (en) | Method for calculating urban public facility coverage radiation index | |
| CN103995837A (en) | Personalized tourist track planning method based on group footprints | |
| Czepkiewicz et al. | Geo-questionnaire: a spatially explicit method for eliciting public preferences, behavioural patterns, and local knowledge–an overview | |
| Xiao et al. | Nine-nine-six work system and people’s movement patterns: using big data sets to analyse overtime working in Shanghai | |
| Tomintz et al. | Optimising the location of antenatal classes | |
| CN110134865A (en) | A kind of commuting passenger's social recommendation method and platform based on urban public transport trip big data | |
| Pouke et al. | Practical simulation of virtual crowds using points of interest | |
| Schoier et al. | Spatial data mining for highlighting hotspots in personal navigation routes | |
| Yuan et al. | Recognition of functional areas based on call detail records and point of interest data | |
| CN114187420A (en) | Real-time online city planning sand table simulation method | |
| CN113298301A (en) | Urban service facility evaluation and optimization method based on crowd digital portrait | |
| CN105844031A (en) | Mobile phone positioning data based urban traffic corridor identification method | |
| Schiel | Migrant labour in contemporary South Africa | |
| Liu et al. | Quantifying and characterizing urban leisure activities by merging multiple sensing big data: A case study of Nanjing, China | |
| Wang et al. | Spatial evaluation of the accessibility of public service facilities in Shanghai: A community differentiation perspective | |
| CN113343164A (en) | Rural residential point social network modeling method based on public service facility configuration | |
| Chaudhuri et al. | Application of web-based Geographical Information System (GIS) in tourism development | |
| Davis Jr et al. | A Framework for Web and Mobile Volunteered Geographic Information Applications. | |
| Niyomubyeyi et al. | An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |