CN116307927A - Life circle planning rationality evaluation method and system based on people stream prediction - Google Patents
Life circle planning rationality evaluation method and system based on people stream prediction Download PDFInfo
- Publication number
- CN116307927A CN116307927A CN202310422154.9A CN202310422154A CN116307927A CN 116307927 A CN116307927 A CN 116307927A CN 202310422154 A CN202310422154 A CN 202310422154A CN 116307927 A CN116307927 A CN 116307927A
- Authority
- CN
- China
- Prior art keywords
- community
- track
- life circle
- data
- space
- 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.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 29
- 238000004088 simulation Methods 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000007781 pre-processing Methods 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 7
- 230000001419 dependent effect Effects 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012800 visualization Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000032823 cell division Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 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
- G06Q10/06—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
- 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
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- 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)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a life circle planning rationality evaluation method and system based on people stream prediction, belonging to the field of urban planning; a life circle planning rationality assessment method based on people stream prediction comprises the following steps: collecting multi-source data and constructing a community space-time database; constructing a community people stream track prediction model based on a community space-time database, and predicting a community inflow track vector set and a community outflow track vector set; simulating a community life circle based on the community inflow track vector set and the community outflow track vector set; performing planning rationality evaluation on the simulated life circle; outputting an evaluation result; according to the method, the living circle is simulated by using OD flow prediction data, so that the outflow track and the inflow track of a community are obtained, the service range of the living circle is predicted, the technical method that the living circle of the community can be recognized only according to current situation data is broken through, the simulation and the rationality evaluation method for planning the living circle of the community after the implementation of a community planning scheme are provided, and the evaluation accuracy is improved.
Description
Technical Field
The invention belongs to the field of urban planning, and particularly relates to a life circle planning rationality assessment method and system based on people stream prediction.
Background
Life circle planning and construction can improve resident life quality and satisfaction, so that the problem of definition of life circle concepts and definition of service range is widely focused. However, the current living circle demarcation technology still adopts a method of directly demarcating according to the service radius by taking living facilities as the center, has stronger subjectivity, and does not consider real space-time activities of residents; the defined life circle is deviated from the life circle generated by the actual actions of residents, and the execution of community planning is not facilitated. Moreover, whether the community planning scheme is reasonable or not is checked, if the new land layout generates the problems of increased commute distance, uneven facility distribution and the like, the problem needs to be determined through the living range of residents after the scheme is landed, however, the prior art relies on the current facility resources, and the situation after the scheme implementation cannot be predicted and estimated.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a life circle planning rationality evaluation method and system based on people stream prediction, which rely on people stream data prediction technology to simulate and rationality evaluate a community life circle in a region after the community planning scheme is implemented.
The aim of the invention can be achieved by the following technical scheme:
a life circle planning rationality assessment method based on people stream prediction comprises the following steps:
collecting multi-source data and constructing a community space-time database;
constructing a community people stream track prediction model based on a community space-time database, and predicting a community inflow track vector set and a community outflow track vector set;
simulating a community life circle based on the community inflow track vector set and the community outflow track vector set;
performing planning rationality evaluation on the simulated life circle;
and outputting an evaluation result.
Further, the step of constructing the community space-time database is as follows:
s11, collecting geographical space-time data and community information data, preprocessing the collected data, and measuring travel time and travel direction basic attributes of individual space-time track data of community resident users;
s12, carrying out space matching on the geographic space-time data and the community information data and the community administrative boundaries, extracting a space-time stream data set of each community and establishing an environment data set;
s13, carrying out data preprocessing on the space-time stream data set of each community and the built environment data set, and respectively summarizing and building a community space-time stream database by taking each community as an object.
Further, in S11, preprocessing the collected data includes: dividing and identifying resident users by using a land unit;
the land unit is divided into: taking community land function data as a basic unit boundary and numbering;
identifying a resident user: the community resident users are primarily screened according to the residence time, namely the time difference between two adjacent positioning of the same user in a set time interval and positioned in a designated city is accumulated to the accumulated time T of the corresponding user; judging whether the accumulated time T of the corresponding user is larger than or equal to an experience threshold value, if T is larger than or equal to the experience threshold value, the corresponding user is resident, otherwise, the corresponding user is not resident.
Further, in S13, the data preprocessing includes: clustering OD flows and calculating OD flow rates by using the land units;
the land unit OD stream clusters are: space summarizing individual space-time track data by using land units to generate land unit OD streams;
the OD flow rate calculation process comprises the following steps: calculating 24h track flow of OD flows of different land units, removing the land unit OD flows with the sum of the 24h track flow being lower than 20, and numbering j the rest land unit OD flows to obtain the track flow of the land unit OD flow with the number j in a period t within 24 hours; and constructing a space-time complex clustering algorithm, clustering individual space-time track data in the OD stream of the land-using unit by combining the traveling direction, and calculating the flow of various OD streams.
Further, the building and predicting steps of the community people flow track prediction model comprise:
s21, identifying a community outflow track and a community inflow track according to the nature of the starting point land; calculating track travel frequency, clustering the inflow track and the outflow track of the communities respectively, and performing vectorization processing by using a word embedding model to obtain an inflow track vector training set and an outflow track vector training set of each community;
s22, using the community inflow track vector training set, the community outflow track vector training set and the community land unit independent variable index as input variables, constructing a structural equation model, and extracting regression variable indexes among the variables;
s23, automatically generating an inflow track vector set and an outflow track vector set of each community in the target administrative region through the community track prediction model constructed in the S22.
Further, the regression variable index adopts a gradient descent algorithm to solve the fitting goodness, and if each fitting goodness index of the structural equation model meets the fitting standard of the fitting goodness index, the finally obtained structural equation model is used as a community people flow track prediction model:
X1＝Λ1x+δ1 (1)
X2＝Λ2x+δ2 (2)
η＝βx+Γx 2 +...+ζ (3)
wherein, the formulas (1) and (2) are measurement models, the formula (3) is a structural model, X 1 For community outflow track flow, X 2 For community inflow track traffic, η is the dependent variable X 1 、X 2 Is a matrix set of (a); x is an argument; beta is the coefficient matrix of the dependent variable; Γ is the coefficient matrix of the argument; ζ is denoted as residual error.
Further, the specific steps of simulating the community life circle comprise:
s31, marking the community inflow track vector set in S24 as V1 = { V i The community outflow track vector set is marked as v2= { V i According to the travel frequency and the distance interval d= (d) min ,d max ) Screening each prediction set, extracting each track end point in the community outflow track vector set, and forming a point set P 1 The method comprises the steps of carrying out a first treatment on the surface of the Extracting each track starting point in the community outflow track vector set to form a point set P 2 ；
S32, calculating a point set P in the geographic information platform by using a standard deviation elliptical tool 1 、P 2 And form simulated living circles C, S for each community.
Further, the standard deviation elliptical tool is calculated by the following steps:
wherein x and y are respectively point sets P 1 ，P 2 And { x, y } represents the average center of the point set, n is the total number of points in the point set.
Further, the step of evaluating the simulated life circle comprises the following steps:
s41, evaluating the rationality of life circle planning based on a compact index, wherein the calculation mode of the compact index is the ratio of the area of a community simulated life circle to the area of a minimum circumscribed circle;
s42, evaluating the rationality of life circle planning based on the deviation index; the deviation index is calculated in the following way: the ratio of the difference between the distances between the center of the minimum circumscribing circle of the community simulated life circle and the center of mass of the community to the average radius of the basic life circle;
basic life circle means: a walking range with community centroid as center and walking distance of 15min as radius.
A life circle planning rationality assessment system based on people stream prediction, comprising:
and a data acquisition module: collecting multi-source data and constructing a community space-time database;
the prediction model building module: constructing a community people stream track prediction model based on a community space-time database, and predicting a community inflow track vector set and a community outflow track vector set;
life circle simulation module: simulating a community life circle based on the community inflow track vector set and the community outflow track vector set;
and an evaluation module: performing planning rationality evaluation on the simulated life circle;
and a result output module: and outputting an evaluation result.
The invention has the beneficial effects that:
1. according to the method, the living circle is innovatively simulated by using the OD flow prediction data, so that the outflow point set and the inflow point set of the community are obtained, the service range of the living circle is predicted, the technical method that the living circle of the community can be recognized only by mastering the living circle of the community according to the current situation data is broken through, the simulation and the rationality evaluation method for planning the living circle of the community after the implementation of the community planning scheme are provided, and the evaluation accuracy is remarkably improved.
2. The invention evaluates the planning rationality of the simulated life circle from the aspects of compact index and deviation index, effectively judges the influence of the land layout of the current community planning scheme on the future people stream, and improves the design efficiency of designers.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a plot of the division of cells used in the present invention;
FIG. 3 is a diagram of the structure equation model variable relationship in the present invention;
FIG. 4 is a schematic diagram of a life circle simulation in the present invention;
FIG. 5 is a flow chart of a life circle rationality evaluation in the present invention;
FIG. 6 is a schematic diagram showing the output of the evaluation result in the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below in connection with a certain case;
as shown in fig. 1, a life circle planning rationality evaluation method based on people stream prediction comprises the following steps:
s1, collecting multi-source data and constructing a community space-time database;
the method specifically comprises the following steps:
s11, collecting geographic space-time data and community information data;
the method comprises the following steps: and obtaining geographic space-time data and community information data in a target city range from the regional data open platform, wherein the community information data comprises community administrative boundary vector data, amateur POI point data, road network data, land function data, building data, community population data and household annual income data, and the geographic space-time data is mobile phone location service data (LBS data) and is represented as community current crowd space-time distribution data. And preprocessing the mobile phone position service data, and measuring the travel time and travel direction basic attribute of individual space-time track data of the community resident user.
Wherein, the preprocessing of the mobile phone location service data comprises: dividing and identifying resident users by using a land unit; as shown in fig. 2, the land cell division is to use community land function data as a basic cell boundary, and number i (i=1, 2...n); identifying a resident user as: preliminarily screening community resident users according to the stay time, namely accumulating the time difference between two adjacent positioning of the same user in a set time interval and positioned in a designated city into the accumulated time T of the corresponding user; judging whether the accumulated time T of the corresponding user is larger than or equal to an experience threshold value, if T is larger than or equal to the experience threshold value, the corresponding user is resident, otherwise, the corresponding user is not resident.
In addition, the travel time and travel direction basic attribute of the individual space-time track data are measured, and the calculation and induction modes are as follows:
the travel time is as follows: dividing individual space-time track data into 24 classes according to the time for reaching the track end point, and identifying travel time T (T=0, 1,2 … …);
the travel direction means: the land unit number for the inductive calculation starting point is i x The destination land unit is i y Individual spatiotemporal trajectory data of (a);
initially screening community resident users according to the stay time, namely accumulating the time difference between two adjacent positioning of the same user in a set time interval and positioned in a designated city into the accumulated time T of the corresponding user; judging whether the accumulated time T of the corresponding user is larger than or equal to an experience threshold value, if T is larger than or equal to the experience threshold value, the corresponding user is resident, otherwise, the corresponding user is not resident.
Converting the data into skp format files, and accurately locating the skp format files to specific space positions; stored on a workstation configured to be no less than Intel XeonProcessor E-2620V 4 processor, 512G SSD,128GdDDR4 memory, and perform specific operations on the workstation.
S12, extracting a space-time stream data set and building an environment data set: and performing space matching on the geographic space-time data and the community information data and the community administrative boundaries, and extracting a space-time stream data set of each community to build an environment data set.
Taking community land units as objects, and measuring and calculating relevant indexes of the built environment of each community based on current situation data: community economic development characteristics, community space morphological characteristics, community traffic accessibility characteristics, community amateur facility characteristics and community population constitution characteristics;
the community economic development is characterized in that: annual income of family, ten thousand yuan per year;
the community space morphological characteristics comprise volume rate and building density, wherein the volume rate is the ratio of all building areas to land area of the community, and the building density is the ratio of the sum of building bottom area to community land area;
the community traffic reachable features comprise road network density and traffic facility density, wherein the road network density is the ratio of the sum of the lengths of all the central lines of roads in the community to the area of the community, and the traffic facility density is the density of points of subway stations and bus stations in the community;
the community performance facility characteristics comprise various performance resource point densities, the facility classifications are divided into 3 major categories 13 minor categories (shown in table 1), and the performance resource point densities refer to the ratio of the number of various performance resource points in each community to the community land area;
table 1 facility classification table
Community demographics include a floating population ratio, which is the ratio of the population of the household to the population of the community, and an aging population ratio, which is the ratio of the population of the household over 60 years old to the population of the community, obtained by dealing with local streets or government.
S13, constructing a community space-time flow database: performing data preprocessing on the community space-time stream data sets and the built environment data sets in the step S12, and respectively summarizing and building a community space-time stream database by taking each community as an object;
wherein the pretreatment step comprises the following steps: clustering the OD flows of the land units and calculating the OD flow;
the land unit OD stream clusters are: space summarizing individual space-time track data by using land units to generate land unit OD streams;
the OD flow rate calculation process is as follows: calculating 24h track flow of the OD flows of different land units, removing the OD flows of the land units with the sum of the 24h track flow being lower than 20, and numbering j the OD flows of the rest land units to obtain the track flow Qjt of the OD flows of the land units with the number j in a period t within 24 hours; and constructing a space-time complex clustering algorithm, clustering individual space-time track data in the OD stream of the land-using unit by combining the travel direction, and calculating various OD stream flows (Qjt-type).
Combining the clustering result of the individual space-time track data with the OD stream flow, and respectively summarizing and constructing a community space-time stream database by taking each community as an object; the method comprises the steps of carrying out a first treatment on the surface of the The community space-time flow database comprises OD flow (Qjt-type) in each direction of each period of 24 hours between two land units and city building environment data corresponding to starting and ending land x and y.
S2, constructing a people stream track prediction model based on the community space-time stream database in S1, and predicting each community track in the target administrative district; the method specifically comprises the following steps:
s21, constructing an inflow and outflow track vector training set based on an S1 community space-time flow database;
identifying a community outflow track and a community inflow track according to the nature of the starting and stopping point land; calculating track travel frequency, clustering the inflow track and the outflow track of the communities respectively, and performing vectorization processing by using a word embedding model to obtain an inflow track vector training set and an outflow track vector training set of each community;
the track vector training set comprises travel directions, travel distances and corresponding travel frequency information;
it is worth mentioning that the land units with the residence time ratio of more than 90% of each track at night (22:00 to 06:00) are calculated as residential places, and the land units with the residence time ratio of more than 90% of the track at daytime (09:00 to 18:00) are calculated as employment places.
The travel frequency of the track is calculated as follows: the number of travel sections after the travel track is interrupted by the resident points within 24 hours;
in addition, taking the track taking the residence place as a starting point and other community employment places as end points in each community space-time flow database as a community outflow track; the track taking other community residence as a starting point and the community employment as an ending point is taken as a community inflow track. Taking a community administrative boundary as an object, clustering all community outflow tracks and community inflow tracks in the same community by using OPTICS respectively, inputting word embedding models, and integrating an inflow track vector set and an outflow track vector set of each community.
S22, constructing a community people stream track prediction model according to the inflow track vector training set and the outflow track vector training set which are obtained in the S21 and based on the community space stream database;
constructing a structural equation model by taking the community inflow track vector training set, the community outflow track vector training set and the community land unit independent variable index as input variables, and extracting a regression variable index table among the variables; the extraction method of the regression variable index comprises the following steps: solving the goodness-of-fit by adopting a gradient descent algorithm, and taking the finally obtained structural equation model as a community people stream track prediction model if all the goodness-of-fit indexes of the structural equation model meet the adaptation standard of the goodness-of-fit indexes:
the basic framework of the equation is as follows:
X1＝Λ1x+δ1 (1)
X2＝Λ2x+δ2 (2)
η＝βx+Γx 2 +...+ζ (3)
wherein formula 1 and formula 2 are measurement models, formula 3 is a structural model, and X 1 For community outflow track flow, X 2 For community inflow track traffic, η is the dependent variable X 1 、X 2 Is a matrix set of (a); x is an argument; beta is the coefficient matrix of the dependent variable; Γ is the coefficient matrix of the argument; ζ is denoted as residual, which is the part that is not explained in the pattern, and the model variables are constructed as shown in fig. 3.
Further, extracting each community to build an environment data set, and building an independent variable index to obtain an independent variable index table; the index table contains the following specific contents:
TABLE 2 independent variable index Table
| Sequence number | Index (I) | Code | |
| 1 | Annual income of | e1 | |
| 2 | Volume fraction | e2 | |
| 3 | Building density | e3 | |
| 4 | Road network density | e4 | |
| 5 | Density of traffic facilities | e5 | |
| 6 | Amateur facility density | e6 | |
| 7 | Ratio of the floating population | e7 | |
| 8 | Age population ratio | e8 |
Further, if each fitting goodness index of the structural equation model meets the fitting goodness index adapting standard, the finally obtained structural equation model is used as a community people flow track prediction model, wherein the model is required to be corrected according to the fitting goodness index adapting standard, and the model is corrected mainly by arranging model correction values in sequence from large to small and sequentially establishing a correlation between variables according to the sequence, or deleting insignificant variables and paths.
S23, predicting a community track model of the target administrative district;
inputting a target administrative region in which community planning scheme data are located, wherein the form of the built environment data of the target administrative region is the same as that of S1; and automatically generating an inflow track vector set and an outflow track vector set of each community in the target administrative region through the community track prediction model constructed in the S22.
S3, simulating a community life circle based on the inflow track vector set and the outflow track vector set of each community in the target administrative district community predicted in the S23; the method comprises the following specific steps:
s31, screening a predicted point set;
the community inflow trajectory vector set in S24 is marked as v1= { V i The community outflow track vector set is marked as v2= { V i According to the travel frequency and the distance interval d= (d) min ,d max ) Screening each prediction set, extracting each track end point in the community outflow track vector set, and forming a point set P 1 The method comprises the steps of carrying out a first treatment on the surface of the Extracting each track starting point in community inflow track vector set to form a point set P 2 。
S32, simulating a community life circle;
as shown in FIG. 4, a set of points P is calculated in a geographic information platform using a standard deviation elliptical tool 1 、P 2 Is set to P 1 Is marked as a travel life circle { C=C for each community i P is }, P 2 Is marked as S, serving life circle { s=s for each community i Form simulated circles of life for each community { C, S }.
The standard deviation elliptical tool is calculated as follows:
wherein x and y are respectively a point set P 1 ，P 2 Is used for the purpose of determining the coordinates of (a),
represents the average center of the point set, and n is the total number of the point set points.
S4, carrying out planning rationality evaluation on the life circle simulated in the step S3;
as shown in fig. 5, the specific evaluation steps are:
s41, evaluating the rationality of life circle planning based on the compact index;
the method is used for reflecting the centralized opening degree of each life circle shape in the simulated life circles { C, S } based on people stream prediction, and the calculation mode of the compact index P is the ratio of the area A of the simulated life circles { C, S } of the community to the area A0 of the minimum circumscribed circle of the simulated life circles { C, S }; if the travel life circle compactness index P_c is more than or equal to 0.8, marking as a 'compliance life circle', otherwise marking Ci as a 'life circle to be modified'; the judgment rule of the service life circle compactness index P_s is the same as that above; if both { C, S } are judged to be compliant, then S42 is entered, otherwise S51 is entered;
for the simulated living circles { C, S } in this example, calculation was performed, p_c=0.9, and p_s=0.81, and compliance was determined.
S42, evaluating the planning rationality of the simulated life circle based on the deviation index;
for reflecting the degree of positional deviation of each of the simulated living circles { C, S } with respect to the basic living circle Gi; the basic life circle is a walking range taking a community centroid as a center and a walking distance of 15min of residents as a radius, the extraction method comprises the steps of calculating the community centroid by using a calculation geometric tool, calculating a range area of the 15min basic life circle by combining the community road network data by using a road finding algorithm tool, and marking the range area as Gi;
the calculation mode of the deviation index Q is as follows: the ratio of the difference between the distances between the center of the minimum circumscribing circle of the community simulated life circle { C, S } and the center of the community centroid to the average radius of Gi; the travel life circle deviation index is expressed as Q_c, the service life circle deviation index is expressed as Q_s, and if Q_c is smaller than 0.2 and Q_s is smaller than 0.2, the travel service double-reasonable life circle is judged; q_c is more than or equal to 0.2, Q_s is less than 0.2, and the service life circle is judged to be 'service reasonable life circle'; q_c is less than 0.2, Q_s is more than or equal to 0.2, and the travel reasonable life circle is judged; jumping to S53 when any one of the above conditions is satisfied; if Q_c is more than or equal to 0.2 and Q_s is more than or equal to 0.2, marking as a life circle to be modified, and entering S51;
for example tiles { C, S }, q_c=18%, q_s=40%, and it is determined as "service rational life circle".
S5, feeding back and outputting an evaluation result; the method comprises the following specific steps:
s51, evaluating result feedback;
exporting all communities determined as 'life circles to be modified' in the S41 and the S42 to a geographic space information platform, adjusting the types, the layouts and the quantity of community facilities by changing the geographic space information platform, and feeding back adjusted community geographic information data to the S24; until the compact index is judged to be compliant and the deviation index evaluation result is "travel service double reasonable life circle", "service reasonable life circle" and "travel reasonable life circle", S52 is entered.
S52, generating a life circle simulation and evaluation thematic map;
and (3) carrying out type and hierarchical visualization on the life circle simulation result and the life circle planning rationality evaluation result in the S32 in the geographic information platform.
The visualization mode of the life circle simulation result is as follows: respectively carrying out layer superposition on the community simulated living circles { C, S } in the S32 and the target administrative district map to obtain a living circle simulated thematic map; the visualization mode of the life circle planning rationality assessment result is as follows: and the space distribution of the travel service double reasonable life circles, the service reasonable life circles and the travel reasonable life circles in S41 and S42 is displayed in a grading manner, and the compact index and the deviation type information are combined to output a life circle planning rationality evaluation chart.
S53, outputting life circle simulation and evaluation results.
Hanging the obtained life circle simulation and planning rationality evaluation results with databases of various planning management departments, and outputting community life circle simulation and planning rationality evaluation reports through a Stratasys industrial 3d printer for guiding land optimization decisions of a community planning scheme; as shown in fig. 6.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (10)
1. A life circle planning rationality evaluation method based on people stream prediction is characterized by comprising the following steps:
collecting multi-source data and constructing a community space-time database;
constructing a community people stream track prediction model based on a community space-time database, and predicting a community inflow track vector set and a community outflow track vector set;
simulating a community life circle based on the community inflow track vector set and the community outflow track vector set;
performing planning rationality evaluation on the simulated life circle;
and outputting an evaluation result.
2. The life circle planning rationality assessment method based on people stream prediction according to claim 1, wherein the step of constructing the community spatiotemporal database is:
s11, collecting geographical space-time data and community information data, preprocessing the collected data, and measuring travel time and travel direction basic attributes of individual space-time track data of community resident users;
s12, carrying out space matching on the geographic space-time data and the community information data and the community administrative boundaries, extracting a space-time stream data set of each community and establishing an environment data set;
s13, carrying out data preprocessing on the space-time stream data set of each community and the built environment data set, and respectively summarizing and building a community space-time stream database by taking each community as an object.
3. The life circle planning rationality assessment method based on people stream prediction according to claim 2, wherein in S11, preprocessing the collected data comprises: dividing and identifying resident users by using a land unit;
the land unit is divided into: taking community land function data as a basic unit boundary and numbering;
the identifying resident users is as follows: preliminarily screening community resident users according to the stay time, namely accumulating the time difference between two adjacent positioning of the same user in a set time interval and positioned in a designated city into the accumulated time T of the corresponding user; judging whether the accumulated time T of the corresponding user is larger than or equal to an experience threshold value, if T is larger than or equal to the experience threshold value, the corresponding user is resident, otherwise, the corresponding user is not resident.
4. The life circle planning rationality assessment method based on people stream prediction according to claim 2, wherein in S13, the data preprocessing includes: clustering OD flows and calculating OD flow rates by using the land units;
the land unit OD stream clusters are: space summarizing individual space-time track data by using land units to generate land unit OD streams;
the OD flow rate calculation process comprises the following steps: calculating 24h track flow of OD flows of different land units, removing the land unit OD flows with the sum of the 24h track flow being lower than 20, and numbering j the rest land unit OD flows to obtain the track flow of the land unit OD flow with the number j in a period t within 24 hours; and constructing a space-time complex clustering algorithm, clustering individual space-time track data in the OD stream of the land-using unit by combining the traveling direction, and calculating the flow of various OD streams.
5. The life circle planning rationality assessment method based on people stream prediction according to claim 1, wherein the community people stream track prediction model constructing and predicting steps comprise:
s21, identifying a community outflow track and a community inflow track according to the nature of the starting point land; calculating track travel frequency, clustering the inflow track and the outflow track of the communities respectively, and performing vectorization processing by using a word embedding model to obtain an inflow track vector training set and an outflow track vector training set of each community;
s22, using the community inflow track vector training set, the community outflow track vector training set and the community land unit independent variable index as input variables, constructing a structural equation model, and extracting regression variable indexes among the variables;
s23, automatically generating an inflow track vector set and an outflow track vector set of each community in the target administrative region through the community track prediction model constructed in the S22.
6. The life circle planning rationality assessment method based on people stream prediction according to claim 5, wherein the regression variable index adopts a gradient descent algorithm to solve the goodness of fit, and if each goodness of fit index of the structural equation model meets the adaptation standard of the goodness of fit index, the finally obtained structural equation model is used as a community people stream track prediction model:
X1＝Λ1x+δ1 (1)
X2＝Λ2x+δ2 (2)
η＝βx+Γx 2 +...+ζ (3)
wherein, the formulas (1) and (2) are measurement models, the formula (3) is a structural model, X 1 For community outflow track flow, X 2 For community inflow track traffic, η is the dependent variable X 1 、X 2 Is a matrix set of (a); x is an argument; beta is the coefficient matrix of the dependent variable; Γ is the coefficient matrix of the argument; ζ is denoted as residual error.
7. The life circle planning rationality assessment method based on people stream prediction according to claim 1, wherein the specific step of simulating the community life circle comprises the following steps:
s31, marking the community inflow track vector set in S24 as V1 = { V i The community outflow track vector set is marked as v2= { V i According to the travel frequency and the distance interval d= (d) min ,d max ) Screening each prediction set, extracting each track end point in the community outflow track vector set, and forming a point set P 1 The method comprises the steps of carrying out a first treatment on the surface of the Extracting each track starting point in the community outflow track vector set to form a point set P 2 ；
S32, calculating a point set P in the geographic information platform by using a standard deviation elliptical tool 1 、P 2 And form simulated living circles C, S for each community.
8. The life circle planning rationality assessment method based on people stream prediction according to claim 7, wherein the standard deviation elliptical tool is calculated in the following manner:
wherein x and y are respectively point sets P 1 ，P 2 Is used for the purpose of determining the coordinates of (a),
represents the average center of the point set, and n is the total number of the point set points.
9. The life circle planning rationality evaluation method based on people stream prediction according to claim 1, wherein the step of evaluating the simulated life circle is:
s41, evaluating the rationality of life circle planning based on a compact index, wherein the calculation mode of the compact index is the ratio of the area of a community simulated life circle to the area of a minimum circumscribed circle;
s42, evaluating the rationality of life circle planning based on the deviation index; the deviation index is calculated in the following way: the ratio of the difference between the distances between the center of the minimum circumscribing circle of the community simulated life circle and the center of mass of the community to the average radius of the basic life circle;
basic life circle means: a walking range with community centroid as center and walking distance of 15min as radius.
10. Life circle planning rationality evaluation system based on people stream prediction, characterized by comprising:
and a data acquisition module: collecting multi-source data and constructing a community space-time database;
the prediction model building module: constructing a community people stream track prediction model based on a community space-time database, and predicting a community inflow track vector set and a community outflow track vector set;
life circle simulation module: simulating a community life circle based on the community inflow track vector set and the community outflow track vector set;
and an evaluation module: performing planning rationality evaluation on the simulated life circle;
and a result output module: and outputting an evaluation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310422154.9A CN116307927A (en) | 2023-04-19 | 2023-04-19 | Life circle planning rationality evaluation method and system based on people stream prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310422154.9A CN116307927A (en) | 2023-04-19 | 2023-04-19 | Life circle planning rationality evaluation method and system based on people stream prediction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116307927A true CN116307927A (en) | 2023-06-23 |
Family
ID=86785177
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310422154.9A Pending CN116307927A (en) | 2023-04-19 | 2023-04-19 | Life circle planning rationality evaluation method and system based on people stream prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116307927A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118134111A (en) * | 2024-05-06 | 2024-06-04 | 清华大学 | Community life circle planning current situation evaluation method based on mobile phone signaling data |
-
2023
- 2023-04-19 CN CN202310422154.9A patent/CN116307927A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118134111A (en) * | 2024-05-06 | 2024-06-04 | 清华大学 | Community life circle planning current situation evaluation method based on mobile phone signaling data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446470B (en) | Medical facility accessibility analysis method based on vehicle trajectory data and population distribution | |
| CN110889562B (en) | Dynamic city model system based on scenario planning | |
| CN109978224B (en) | Method for analyzing and acquiring traffic trip rates of buildings with different properties | |
| CN110176141B (en) | Traffic cell division method and system based on POI and traffic characteristics | |
| US20220270192A1 (en) | Computerized-system and computerized-method to calculate an economic feasibility analysis for an urban planning model | |
| CN108597227A (en) | Road traffic flow forecasting method under freeway toll station | |
| CN106488405A (en) | A kind of position predicting method merging individuality and neighbour's movement law | |
| CN108961758A (en) | A kind of crossing broadening lane detection method promoting decision tree based on gradient | |
| CN110413855B (en) | Region entrance and exit dynamic extraction method based on taxi boarding point | |
| CN102646164A (en) | Land use change modeling method and system implemented in combination with spatial filtering | |
| CN116307927A (en) | Life circle planning rationality evaluation method and system based on people stream prediction | |
| CN113505999A (en) | Index calculation method applied to urban space quality assessment | |
| CN114662774A (en) | City block vitality prediction method, storage medium and terminal | |
| CN114819589A (en) | Urban space high-quality utilization determination method, system, computer equipment and terminal | |
| Yu et al. | Passenger flow prediction for new line using region dividing and fuzzy boundary processing | |
| Chen et al. | An analysis of movement patterns between zones using taxi GPS data | |
| CN113342873B (en) | Population analysis unit division method based on city morphology and convergence mode | |
| CN111738527B (en) | Urban traffic cell division method based on hot spot detection model | |
| CN105654722A (en) | Road programming method based on speeds | |
| CN116957280A (en) | Mountain village and town community life circle public service facility configuration method based on supply and demand balance | |
| CN116167254A (en) | Multidimensional city simulation deduction method and system based on city big data | |
| AU2021104004A4 (en) | Double-layer Domain-oriented Intelligent Optimal Allocation Method for Rural Residential Areas | |
| CN113554221B (en) | Method for simulating and predicting town development boundary under view angle of' flow space | |
| CN116258612A (en) | Urban land utilization distribution evaluation method based on knowledge embedding model | |
| Wang et al. | Hierarchical stroke mesh: A new progressive matching method for detecting multi-scale road network changes using OpenStreetMap |
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 |