A method for optimizing the time-sharing design of open space in community micro-renewal

Abstract

The present invention proposes a method for optimizing the time-sharing design of open space in community micro-renewal, comprising the steps of: dividing the area into a grid map composed of regular grid units according to the scope of the open space in the target community, and assigning attributes to all grid units according to the land use type; confirming the area occupied by a single entity; confirming the multi-purpose open space plot that can carry motor vehicles; establishing a multi-objective optimization model for the community multi-purpose open space; using the classic multi-objective genetic algorithm NSGA-II for rapid solution; selecting time periods for multiple different objectives, adjusting parameters according to actual experience and re-simulating, and selecting the time-sharing utilization period suitable for the target community and its Pareto optimal solution. The method of the present invention effectively improves the certainty of the planning effect, maximizes the utilization rate of the existing open space in the community, and is used to carry the growing parking demand and the needs of daily activities.

Description

Technical Field

[0001] The present invention relates to the field of urban planning and design, and in particular to an optimization of a design method for time-divided use of existing open spaces in supporting community micro-renewal. Background Art

[0002] Quantitative technology has made great progress in serving urban planning. Quantitative technology provides a basis for scientific evaluation in planning schemes. By performing scenario analysis and planning simulation on digital city models, the impact of planning can be evaluated and understood in advance, so that the results of the implementation of planning schemes are closer to the design goals. Usually, digital scenarios need to combine qualitative and quantitative methods to conduct thematic analysis and impact assessment in order to optimize the design benefits of planning elements (such as public facilities and green spaces).

[0003] Among the various common optimization methods, multi-objective optimization is an optimization method that considers the combined application of multiple factors, but it is usually difficult to find the absolute optimal solution to minimize all objectives. For multi-objective optimization, the commonly used method in the planning field is to form a multi-factor comprehensive evaluation result through some comprehensive calculation method (such as hierarchical analysis method, weighted average of utility function, etc.). The common practice is to assign corresponding weights to multiple objectives and convert the multi-objective problem into a single-objective calculation. This type of comprehensive evaluation is a single comprehensive result for the research scenario, and the calculated optimal result may shield the influence of some factors.

[0004] Although there are currently cases of multi-objective optimization in urban planning implementation evaluation and planning scheme simulation, there are currently few multi-objective optimization methods for site design resource utilization in community micro-renewal scenarios. Specifically, for the planning and design of open spaces in urban micro-renewal scenarios, the efficiency of the use of such spaces has not been fully considered. Therefore, accordingly, the planning optimization for the use of community open spaces should be fully estimated.

[0005] Specifically, the current planning and optimization of open spaces in community micro-renewal mainly considers spatial reconfiguration (increase, replacement, etc.), and often ignores the optimization of the use of existing sites in the time dimension. In the planning and design of community renewal entering the "stock" era, it is necessary to avoid large-scale spatial adjustments and a large amount of community transformation costs as a prerequisite, and explore the possibility of improving the efficiency of existing spatial resources from the time dimension. Some open spaces in the community, such as lawns, are often vacant for a long time during daily use or specific periods. The existing micro-renewal design lacks a detailed description and expression of the time-sharing utilization of such land. Therefore, based on the existing public space pattern within the community, it is of great significance to intensively utilize community public resources by quantitatively analyzing and optimizing to improve the time-sharing utilization rate of its open space. Summary of the invention

[0006] In view of the above-mentioned problems, the purpose of the present invention is to propose a multi-objective optimization method for the time-division use design of community open spaces, so as to solve the problems that it is difficult to quantitatively evaluate the use impact of open spaces in the time dimension and the time-division use configuration in the current micro-renewal design, so as to maximize the utilization rate of existing open spaces in the community to meet the growing parking needs and daily activities. Since the present invention belongs to a specific field problem, it is necessary to design a special optimization method that can fully evaluate the upper limit of community open space resource utilization based on site characteristics, accurately locate the planning and design that meets actual needs, and provide different selection sets with the most optimized benefits.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] Based on the multi-objective optimization method, an optimization model for the time-sharing use of community open spaces is constructed to find a set of solutions that are as close to the Pareto optimal domain as possible, so as to provide a configuration plan for time-sharing use.

[0009] A method for optimizing the time-sharing use design of open space in community micro-renewal is characterized in that it comprises the following steps to achieve:

[0010] (a) In the target community, the area is divided into a grid map consisting of regular grid cells according to the scope of open space, and the attributes of all grid cells are assigned according to the land use type; in addition, the open space area M is divided into three parts according to the authority: M1, M2, and M3, where the M1 area can be used for motor vehicle parking, human activities, or non-motor vehicle parking; the M2 area is only for human activities or non-motor vehicle parking, and the M3 area represents the parking area for motor vehicle use only; the area and use of M3 are fixed, and the M1 and M2 areas are defined as multi-purpose open spaces;

[0011] (b) confirm the area occupied by a single entity;

[0012] (c) Identify multi-use open space parcels that can accommodate motor vehicles;

[0013] (d) Establish a multi-objective optimization model for community multi-use open spaces;

[0014] (e) Using the classic multi-objective genetic algorithm NSGA-II for fast solution, a set of Pareto optimal solutions S of the decision variable x in a given time period is calculated;

[0015] (f) Select multiple time periods with different objectives, and use NSGA-II to simulate different parking activity concentration periods and crowd activity concentration periods to calculate the Pareto optimal solution set; and according to the corresponding u solution sets S1,…S u and the target value vector PF1,…,PF u, determine whether to adjust the parameters and re-simulate; finally update the optimal solution of the decision variable x in different time periods, and use it as the maximum carrying capacity of the community open space for the above three types of uses for reference by planners.

[0016] Furthermore, in step (b), the following steps are specifically performed: selecting typical motor vehicle sizes, human body sizes, and non-motor vehicle sizes, rasterizing them according to the resolution of the community grid map, and calculating the number of grid cells occupied by these three types of entities; establishing buffer zones for these three types of entities according to the activity intervals required; assuming that motor vehicles need a certain interval distance d when parking m , the minimum interval d for a single person's activity p , and the minimum interval d between non-motor vehicles n , then according to d m、 d p and d n Buffer zones are constructed for the three types of entities respectively, and the areas occupied by the three types of entities are calculated using the number of grids including the entities and the buffer zones. Finally, the number of grids corresponding to the three types of entities is obtained, namely, wn1*hn1, wn2*hn2, and wn3*hn3. In addition, the areas occupied by motor vehicles, human bodies, and non-motor vehicles are recorded as r1, r2, and r3.

[0017] Furthermore, in the step (c), specifically: from the community multi-purpose open space plot set L = {l1, ...l t ,t>0,M1+M2=area(sum(l t ))}, according to the size of the motor vehicle wn1*hn1, these plots are confirmed one by one to determine whether the plot can support parking of motor vehicles, and the open space plot set L1={l1,...l k ,t≥k>0,M1=area(sum(l k ))}; thereby extracting the grid set corresponding to L1 and calculating its total area M1; accordingly, all plots in the set (L-L1) can support parking of non-motor vehicles or personnel activities, extracting their corresponding grid sets, and calculating the total area M2 corresponding to (L-L1).

[0018] Furthermore, in step (d), the specific steps are as follows: the time of each day is divided into multiple time periods; the decision variables x1, x2, and x3 are the number of motor vehicles, personnel, and non-motor vehicles that can be accommodated, respectively, and the vector x = {x1, x2, x3} T, is a solution to the optimization problem represented by ; in the time period from m to n, the parking area for motor vehicles is maximized (i.e., the parking area for human activities and non-motor vehicles is minimized); and from time point p to q, i.e., the time period for public activities, the area used for human activities is maximized (i.e., the parking area for motor vehicles and non-motor vehicles is minimized). The specific definition of the objective function and constraints is as follows:

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Wherein, equations (1)-(3) are objective functions, and equations (4)-(7) are constraints; r1, r2, and r3 correspond to the occupied areas of motor vehicles, human bodies, and non-motor vehicles, respectively, and can be regarded as constants (r1, r2, r3>0).

[0027] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0028] (1) This invention proposes an optimization method for the time-sharing use of open space in community micro-renewal design. Compared with other multi-objective optimization studies in the planning field, this invention can specifically deal with the optimal configuration of the time-sharing use of open space in the community, and provide a set of configuration selection sets with the best benefits for the micro-renewal design scheme, thereby effectively improving the certainty of the planning effect.

[0029] (2) The present invention is aimed at multi-purpose open spaces in the community, and fully considers and quantitatively analyzes the potential for use of different purposes (motor vehicles, non-motor vehicles, and crowd activities). Compared with the single calculation value of commonly used comprehensive calculation methods (such as hierarchical analysis method, utility function weighted average, etc.), the Pareto optimal solution set obtained by the present invention can not only intuitively reflect the number of different types of individuals that the community open space can carry, but also give the objective function values ​​of different solutions (i.e., Pareto optimal frontier) and compare them with the actual use results of the community open space.

[0030] (3) Automatic optimization calculation is realized. In the process of solving the decision variable x, for multiple objectives subject to time-sharing constraints, the explanation of each Pareto optimal solution and the visualization of the results are provided, so that automatic simulations of different time periods can be set to evaluate the impact of each objective involved in the design, so as to obtain sufficient (approximate) Pareto optimal solutions for design scheme selection. It provides planners with the basic results of quantitative analysis of the site, and promotes the comprehensive consideration of social, economic and human factors in the next step of design to determine the final time-sharing utilization plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process of the present invention.

[0032] Figure 2 This is a schematic diagram of confirming a community multi-purpose open space plot that can accommodate motor vehicles according to an embodiment of the present invention.

[0033] Figure 3 It is a schematic diagram of the floor space corresponding to the individual units of motor vehicles, people and non-motor vehicles in the present invention, as well as the floor space considering the spacing range.

[0034] Figure 4 Schematic diagram of the Pareto optimal frontier calculated based on the NSGA-II algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] Based on the multi-objective optimization method, an optimization model for the time-sharing use of community open spaces is constructed to find a set of solutions as close as possible to the Pareto optimal domain to provide a configuration plan for time-sharing use. This includes the following steps:

[0036] (a) In the target community, according to the scope of the open space, the area is divided into a grid map consisting of 0.5m*0.5m regular grid cells, and the attributes of all grid cells are assigned according to the land use type. In addition, the open space area M is divided into three parts according to the authority: M1, M2, and M3, where the M1 area can be used for motor vehicle parking, personnel activities or non-motor vehicle parking; the M2 area is only for personnel activities or non-motor vehicle parking, and the M3 area represents the parking area for motor vehicle use only. Since the area and purpose of M3 are fixed (such as a parking lot), its use is not optimized in the present invention. The M1 and M2 areas are defined as multi-purpose open spaces.

[0037] (b) Determine the area occupied by a single subject. Select typical motor vehicle sizes, human body sizes, and non-motor vehicle sizes, rasterize them according to the resolution of the community grid map (0.5m*0.5m), and calculate the number of grid cells occupied by these three types of subjects. According to the activity intervals required by these three types of subjects, buffer zones are established for them respectively. Assume that motor vehicles need a certain interval distance d when parking.m , the minimum interval d for a single person's activity p , and the minimum interval d between non-motor vehicles n , then according to d m、 d p and d n Buffer zones are constructed for the three types of entities respectively, and the areas occupied by the three types of entities are calculated by the number of grids containing the entities and buffer zones, and finally the number of grids corresponding to the three types of entities is obtained, namely, wn1*hn1, wn2*hn2, and wn3*hn3. In addition, the areas occupied by motor vehicles, human bodies, and non-motor vehicles are recorded as r1, r2, and r3.

[0038] (c) Identify multi-purpose open space plots that can accommodate motor vehicles. From the community multi-purpose open space plot set L = {l1,...l t ,t>0,M1+M2=area(sum(l t ))}, according to the size of the motor vehicle wn1*hn1, these plots are confirmed one by one to determine whether the plot can support parking of motor vehicles, and the open space plot set L1={l1,...l k ,t≥k>0,M1=area(sum(l k ))}; thus, the grid set corresponding to L1 is extracted, and its total area M1 is calculated. Accordingly, all plots in the set (L-L1) can support parking of non-motor vehicles or human activities, and the corresponding grid set is extracted, and the total area M2 corresponding to (L-L1) is calculated.

[0039] (d) Establish a multi-objective optimization model for community multi-purpose open spaces. Divide each day into 24 periods, namely {0:00-1:00, 1:00-2:00, ... 23:00-24:00}. The decision variables x1, x2, and x3 are the number of motor vehicles, people, and non-motor vehicles that can be accommodated, respectively. Use the vector x = {x1, x2, x3} T It represents a solution to the optimization problem represented in the present invention. In the time period m to n (e.g. 1:00 a.m. to 6:00 a.m.), the parking area for motor vehicles is maximized (i.e., the parking area for human activities and non-motor vehicles is minimized); and from time point p to q, i.e., the time period for public activities, the area used for human activities is maximized (i.e., the parking area for motor vehicles and non-motor vehicles is minimized). The specific definition of the objective function and constraints is as follows:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Wherein, equations (1)-(3) are objective functions, and equations (4)-(7) are constraints; r1, r2, and r3 correspond to the occupied areas of motor vehicles, human bodies, and non-motor vehicles, respectively, and can be regarded as constants (r1, r2, r3>0).

[0048] (e) Use the classic multi-objective genetic algorithm NSGA-II for fast solution. This method can optimize and solve multi-objective problems, and its calculation speed is fast, and it stops after reaching the specified iteration condition. The present invention uses the NSGA-II algorithm to calculate a set of (approximate) Pareto optimal solutions S for the decision variable x in a given time period. This means that the solution set is non-dominated, that is, there is no solution s in the solution set that is better than any other solution s' in all objectives (Formulas (1)-(3)). Therefore, for different uses such as parking and public activities for crowds, planners must make trade-offs in the time-sharing use of open public spaces and select the Pareto optimal solution suitable for the target community.

[0049] (f) Select multiple time periods with different objectives (time periods m to n, p to q in equations (2) and (3)), and use NSGA-II to simulate different parking activity concentration periods and crowd activity concentration periods to calculate the Pareto optimal solution set. And according to the corresponding u solution sets S1,…S u and the target value vector PF1,…,PF u (the target calculated values ​​corresponding to equations (1)-(3)), determine whether to adjust the parameters m, n, p and q and re-simulate. Finally, update the optimal solution of the decision variable x in the 24 time periods and use it as the maximum carrying capacity of the community open space for the above three types of uses for reference by planners.

[0050] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0051] The present invention is further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto.

[0052] like Figure 1 As shown in the figure, the method of optimizing the time-sharing use of open space in community micro-renewal design includes the following steps:

[0053] Step 1: Based on the map of the target community, divide the area into a grid map with a resolution of 0.5m*0.5m, and assign attributes to all grid cells according to the land use type.

[0054] Step 2: Divide the open space area into three parts according to the given access rights: the first category is for motor vehicle parking, human activities or non-motor vehicle parking; the second category area is only for human activities or non-motor vehicle parking, and the third category area represents the area for motor vehicle parking only. Count the number of grid cells occupied by the first and second categories of areas (multi-purpose spaces) and calculate their areas M1 and M2 respectively ( Figure 2 ).

[0055] Step 3: Calculate the area occupied by a single subject. Select typical motor vehicle models, human models, and non-motor vehicle models, rasterize them at a given resolution (0.5m*0.5m), and establish buffer zones for each of the three types of subjects according to the required activity intervals ( Figure 3 ) Calculate the occupied areas r1, r2, and r3 corresponding to motor vehicles, human bodies, non-motor vehicles, and their buffer zones respectively.

[0056] Step 4: Determine the multi-purpose open space plots that can accommodate motor vehicles based on the actual area. Suppose all the multi-purpose open space plots in the community L = {l1,…,l t ,t>0}, according to the length and width of the motor vehicle area wn1 and hn1, and the area r1, determine the plot l one by one t Whether it can support parking (at least 1) motor vehicle, extract the open space plot set L1 that can be used for motor vehicle / non-motor vehicle parking / personnel activities. Count the grid set corresponding to L1 and calculate its total area M1 ( Figure 2 ).

[0057] Step 5: The remaining open space plot set L2 = (L-L1) can support parking of non-motor vehicles / crowd activities. Count the grid sets corresponding to all plots in L2 and calculate the total area M2 corresponding to L2 ( Figure 2 ).

[0058] Step 6: Construct a multi-objective optimization model for community multi-purpose open space. The decision variables x1, x2, and x3 represent the number of motor vehicles / people / non-motor vehicles that can be accommodated, respectively, and the vector x = {x1, x2, x3} TRepresents. Set optimization goal 1 to minimize the total idle area in 24 time periods, optimization goal 2 to minimize the area of ​​personnel activities and non-motor vehicle parking, and optimization goal 3 to minimize the parking area of ​​motor vehicles and non-motor vehicles. Set optimization parameters m, n, p, q, where m and n are the start time and end time of goal 2; and p and q are the start time and end time of goal 3. At the same time, set the constraints of x to complete the construction of the optimization model.

[0059] Step 7: For the multi-objective optimization model in the invention, the classic multi-objective genetic algorithm NSGA-II is used to solve. The calculation result is the (approximate) Pareto optimal solution set S of the decision variable x in a given time period. In order to quickly obtain the solution, the optimal iteration condition can be specified (such as stopping the calculation after iterating g generations). The user can adjust the iteration condition according to the needs and calculation results to obtain the appropriate optimal solution and Pareto optimal frontier (i.e., the objective function value, see Figure 4 ).

[0060] Step 8: Adjust the parameters according to actual experience, simulate and compare the optimization results of different parameter values ​​(i.e. time periods mn and pq) for target 2 and target 3. Repeat step 7 to obtain the Pareto optimal solution sets calculated for the parking activity concentration time periods and crowd activity concentration time periods corresponding to different parameter values. According to the Pareto optimal solution sets S1,…S corresponding to different parameters u and Pareto optimal frontier PF1,…,PF u , compare the carrying capacity of these three types of uses in 24 time periods (x1, x2, x3), the utilization efficiency of community open spaces (targets 1-3), etc., to provide reference for planners; and confirm whether to adjust the parameters and simulate again until a choice is made in the time-sharing use of open public spaces, and select the time-sharing utilization period suitable for the target community and its Pareto optimal solution.

Claims

1. A method for optimizing the time-sharing use design of open space in community micro-renewal, characterized in that: It includes the following steps to achieve: (a) In the target community, according to the scope of open space, the area is divided into a grid map composed of regular grid cells, and the attributes of all grid cells are assigned according to the land use type; the open space area M is divided into three parts according to the authority: M1, M2, and M3, where the purpose of the M1 area is for motor vehicle parking, human activities, or non-motor vehicle parking, the purpose of the M2 area is only for human activities or non-motor vehicle parking, and the purpose of the M3 area is only for motor vehicle use; the area and purpose of M3 are fixed, and the M1 and M2 areas are defined as multi-purpose open spaces; (b) confirm the area occupied by a single entity; (c) Identify multi-use open space parcels that can accommodate motor vehicles; (d) Establish a multi-objective optimization model for community multi-use open spaces; (e) Using the classic multi-objective genetic algorithm NSGA-II for fast solution, a set of Pareto optimal solutions S of the decision variable x in a given time period is calculated; (f) Select multiple time periods with different objectives, and use NSGA-II to simulate different parking activity concentration periods and crowd activity concentration periods to calculate the Pareto optimal solution set; and according to the corresponding u solution sets S1,…S u and the target value vector PF1,…,PF u , determine whether to adjust the parameters m, n, p and q and re-simulate; finally update the optimal solution of the decision variable x in different time periods and use it as the maximum carrying capacity of the community open space for the above three types of uses for reference by planners; In the step (b), specifically: select typical motor vehicle sizes, human body sizes and non-motor vehicle sizes, rasterize them according to the resolution of the community grid map, and calculate the number of grid cells occupied by these three types of subjects; establish buffer zones for these three types of subjects according to the activity intervals required; assume that a certain interval distance d is required for parking motor vehicles m , the minimum interval d for a single person's activity p , and the minimum interval d between non-motor vehicles n , then according to d m、 d p and d n Buffer zones are constructed for the three types of subjects respectively, and the areas occupied by the three types of subjects are calculated by the number of grids containing the subjects and the buffer zones, and finally the number of grids corresponding to the three types of subjects is obtained, namely, wn1*hn1, wn2*hn2, and wn3*hn3; in addition, the areas occupied by motor vehicles, human bodies, and non-motor vehicles are recorded as r1, r2, and r3; In the step (d), specifically: divide the time of each day into multiple time periods; the decision variables x1, x2, x3 are the number of motor vehicles, personnel and non-motor vehicles that can be accommodated, respectively, and the vector x = {x1, x2, x3} T It is a solution to the optimization problem represented by ; in the time period m to n, the parking area of ​​motor vehicles is maximized; and from time point p to q, the area used for human activities is maximized; the specific definition of the objective function and constraints are as follows: Wherein, equations (1)-(3) are objective functions, and equations (4)-(7) are constraints; r1, r2, and r3 correspond to the occupied areas of motor vehicles, human bodies, and non-motor vehicles, respectively, and are regarded as constants, where r1, r2, and r3>0.

2. The method for optimizing the time-sharing use design of open space in community micro-renewal as claimed in claim 1, characterized in that: In the step (c), specifically: from the community multi-purpose open space plot set L = {l1, ...l t ,t>0,M1+M2=area(sum(l t ))}, according to the size of the motor vehicle wn1*hn1, these plots are confirmed one by one to determine whether the plot can support parking of motor vehicles, and the open space plot set L1={l1,...l k ,t≥k>0,M1=area(sum(l k ))}; thereby extracting the grid set corresponding to L1 and calculating its total area M1; accordingly, all plots in the set (L-L1) support parking of non-motor vehicles or personnel activities, extracting their corresponding grid sets, and calculating the total area M2 corresponding to (L-L1).

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