A method for calculating and optimizing the balance between living and working in urban space

Abstract

This invention proposes a method for calculating and optimizing urban spatial job-housing balance based on mathematical programming. The method includes the following steps: First, collecting basic data related to urban and transportation travel; second, constructing an urban spatial job-housing balance optimization model; and finally, calculating the urban spatial job-housing balance and obtaining the optimal resource allocation result. This invention fully considers the shortcomings of existing engineering practices in calculating and optimizing job-housing balance, improves the accuracy of calculating the current job-housing balance, and considers measures that management departments should consider in future work to promote job-housing balance, thereby improving the decision-making efficiency of relevant urban and transportation engineering practices.

Description

Technical Field

[0001] This invention relates to a method for calculating and optimizing the urban spatial job-housing balance, belonging to the field of urban and transportation job-housing balance technology. Background Technology

[0002] With the continuous advancement of urbanization and the constraint of urban land area, the growth rate of transportation supply is increasingly unable to meet the ever-increasing demand for transportation. This severe imbalance between supply and demand is the root cause of traffic congestion. Against this backdrop, the concept of job-housing balance has gradually gained attention in urban and transportation engineering practices. Job-housing balance refers to effectively balancing the spatial relationship between residents' employment and residence through measures such as mixed land use during urban construction and functional layout, reducing long-distance commutes, lowering travel demand at the source, and thus alleviating traffic congestion. This is of great significance for fundamentally solving urban problems and improving the urban travel environment.

[0003] However, in current urban and transportation engineering practices, the concept of job-housing balance is difficult to implement. This is partly due to the lack of an effective method for calculating job-housing balance; the traditional job-housing ratio (the ratio of the number of jobs to the number of employed residents) is insufficient to accurately assess the actual situation. Furthermore, there is a lack of an optimization method to ensure job-housing balance, making it difficult for management departments to effectively promote its realization. Summary of the Invention

[0004] Technical Problem: In order to address the shortcomings and defects in current urban and transportation engineering practices, this invention proposes a method for calculating and optimizing urban spatial job-housing balance based on mathematical programming, which can effectively calculate the level of urban job-housing balance and support relevant departments' decision-making.

[0005] Technical Solution: To address the aforementioned technical problems, this invention proposes a method for calculating and optimizing the job-housing balance in urban spaces, comprising the following steps:

[0006] Step 1) Collect basic data related to urban life and transportation;

[0007] Step 2), construct an urban spatial work-housing balance optimization model;

[0008] Step 3) Calculate the urban spatial job-housing balance and obtain the optimal resource allocation result.

[0009] Furthermore, the basic urban and transportation-related data collected in step 1) includes: traffic community characteristic data, traffic community work-residence data, and traffic network structure attribute data, specifically including:

[0010] Step 1.1) Collect traffic zone feature data, including traffic zone boundaries, number of traffic zones, and centroid points of traffic zones;

[0011] Step 1.2) Collect work and residence data for the transportation community, including the number of working people and job positions for each type of work within the transportation community;

[0012] Step 1.3) Collect traffic network structure attribute data, including the adjacent structure of the road traffic network in the study area, the length and design speed of each road segment, and calculate the average effective path travel time between the centroids of traffic zones.

[0013] Furthermore, the effective paths between the centroids of the traffic zones mentioned in step 1.3) are calculated using any one of the following methods: the segment penalty function method, the segment elimination method, the K-shortest path algorithm, and the Monte Carlo simulation algorithm.

[0014] Furthermore, the specific formula for calculating the average effective path travel time between the centroids of the traffic zones is as follows:

[0015]

[0016] Among them, c ij Let u be the average effective path travel time between the centroids of traffic zones i and j. ij m is the number of valid paths between the centroids of traffic zone i and traffic zone j. r Let l be the number of road segments contained in the r-th valid path. rt and v rt Let be the length and design speed of the t-th road segment contained in the r-th valid path, respectively.

[0017] Furthermore, in step 2), the urban spatial job-housing balance optimization model is as follows:

[0018]

[0019] Where n, m, and k represent the number of communities where trips occur, the number of communities where trips attract, and the number of different occupations in the study area, respectively; c ij Given traffic cell i to traffic cell j, the average effective path travel time is 0. ia D ja Let χ represent the number of working people in job type a in traffic community i and the number of job positions in job type a in traffic community j, respectively. a Given the total number of positions for the a-th type of work to be deployed; Let be the decision variable, representing the number of job positions of type a to be allocated to traffic community j, and q ijaLet H be the decision variable, representing the trip volume of the a-th job type from traffic zone i to traffic zone j; H represents the total trip volume. τ ij is a 0 / 1 decision variable; s is a given constant integer representing the number of traffic zone intervals to be optimized.

[0020] Furthermore, in step 3), the urban spatial job-housing balance is calculated and the optimal resource allocation result is obtained, specifically including:

[0021] Step 3.1) Calculate the average effective path travel time c between traffic zones. ij Number of working residents in the traffic community: O ia Number of job positions D ja Substituting into the aforementioned urban spatial job-housing balance optimization model, and setting n = m = N, s = 0, Where N represents the number of traffic zones in the study area, the optimal solution G of the model is obtained. min ;

[0022] Step 3.2) Calculate the current urban spatial job-housing balance. The specific calculation formula is as follows:

[0023]

[0024] Where P represents the urban spatial job-housing balance, and G... std This refers to the standard one-way commute time in the city.

[0025] Step 3.3) The average effective path travel time c between traffic zones is collected. ij Number of working residents in the traffic community: O ia Number of job positions D ja The total number of positions in job type a to be added (χ) a Substituting into the aforementioned urban spatial job-housing balance optimization model, and setting n = m = N and s = 1, we obtain the optimal solution of the model. Where N represents the number of traffic zones in the study area;

[0026] Step 3.4), by To achieve the goal of promoting a work-life balance, namely the optimal travel volume, we need to determine the optimal travel volume. The number of newly added job positions within the community was obtained from Traffic zones that should be prioritized for improving commuting efficiency.

[0027] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0028] The technical solution of this invention takes into account the shortcomings of existing engineering practices in calculating the effectiveness and optimization capabilities of job-housing balance, improves the accuracy of calculating the current job-housing balance, and considers measures that management departments should take in future work to promote job-housing balance, thereby improving the decision-making efficiency of relevant urban and transportation engineering practices. Attached Figure Description

[0029] Figure 1 This is a flowchart of the present invention;

[0030] Figure 2 The diagram shows the traffic zone division and centroid distribution for an example. Detailed Implementation

[0031] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0032] It should be noted that the term "traffic zone" as used in this invention is a general technical term and is known to those skilled in the art. In urban traffic engineering practice, the urban traffic network is divided into several functional sub-zones according to certain principles, and these functional sub-zones are called traffic zones.

[0033] This embodiment collected basic urban and transportation-related data within a newly developed area of ​​a city, including traffic zone boundaries, the number of traffic zones, the centroid of each traffic zone, the number of working people and jobs for each occupation within each traffic zone, the total number of jobs to be added, and the average effective travel time between the centroids of traffic zones. Some data are shown below. Figure 2 As shown in Tables 1, 2, 3 and 4.

[0034] Table 1 Number of Job Positions in Traffic Community

[0035]

[0036] Table 2. Number of working residents in the Jiaotong Community

[0037]

[0038]

[0039] Table 3 Number of positions to be added

[0040]

[0041] Table 4. Average effective path travel time (minutes) between the centroids of traffic zones.

[0042]

[0043] Based on the above data, and following the urban spatial job-housing balance optimization model constructed in step 2, when n = m = 50 and s = 0, G std G is obtained under the condition of 45. min =25.65, and the current job-housing balance in the region is calculated to be P=43%.

[0044] Substituting the collected data on average effective path travel time between traffic zones, the number of working people in each traffic zone, the number of jobs, and the total number of jobs to be added into the urban spatial job-housing balance optimization model, the optimal solution of the model is obtained under the conditions of n=m=50 and s=1. As shown in Table 5; simultaneously obtained This indicates that priority should be given to improving traffic efficiency between traffic zones 23 and 37, such as by adopting policies like customized bus services.

[0045] Table 5 Optimal Job Placement Plan for Traffic Communities

[0046]

[0047]

[0048] It should be understood that the above embodiments are only for illustrating the technical ideas of the present invention. For those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating and optimizing the urban spatial job-housing balance, characterized in that, Includes the following steps: Step 1) Collect basic data related to urban transportation; Step 2), construct an urban spatial work-housing balance optimization model; Step 3) Calculate the urban spatial job-housing balance and obtain the optimal resource allocation result; The basic urban and transportation-related data collected in step 1) includes: traffic community characteristic data, traffic community work-residence data, and traffic network structure attribute data, specifically including: Step 1.1) Collect traffic zone feature data, including traffic zone boundaries, number of traffic zones, and centroid points of traffic zones; Step 1.2) Collect work and residence data for the transportation community, including the number of working people and job positions for each type of work within the transportation community; Step 1.3) Collect traffic network structure attribute data, including the adjacency structure of the road traffic network in the study area, the length and design speed of each road segment, and calculate the average effective path travel time between the centroids of traffic zones. The specific formula for calculating the average effective path travel time between the centroids of the traffic zones is as follows: Among them, c ij Let u be the average effective path travel time between the centroids of traffic zones i and j. ij m is the number of valid paths between the centroids of traffic zone i and traffic zone j. r Let l be the number of road segments contained in the r-th valid path. rt and v rt Let be the length and design speed of the t-th road segment contained in the r-th valid path, respectively; In step 2), the urban spatial job-housing balance optimization model is as follows: Where n, m, and k represent the number of communities where trips occur, the number of communities where trips attract, and the number of occupations in the study area, respectively; c ij Given traffic cell i to traffic cell j, the average effective path travel time is 0. ia D ja Let χ represent the number of working people in occupation a in traffic zone i and the number of job positions in occupation a in traffic zone j, respectively. a Given the total number of positions for job type a to be filled; Let be the decision variable, representing the number of job positions of type a to be allocated to traffic community j, and q ija Let be the decision variable, representing the trip volume of job type a from traffic zone i to traffic zone j; H represents the total trip volume. τ ij is a 0 / 1 decision variable; s is a given constant integer representing the number of traffic zone intervals to be optimized.

2. The method for calculating and optimizing the urban spatial job-housing balance according to claim 1, characterized in that, The effective paths between the centroids of the traffic zones mentioned in step 1.3) are calculated using either the K-shortest path algorithm or the Monte Carlo simulation algorithm.

3. The method for calculating and optimizing the urban spatial job-housing balance according to claim 1, characterized in that, Step 3) calculates the urban spatial job-housing balance and obtains the optimal resource allocation result, specifically including: Step 3.1) Calculate the average effective path travel time c between traffic zones. ij Number of working residents in the traffic community: O ia Number of job positions D ja Substituting into the aforementioned urban spatial job-housing balance optimization model, and letting n = m = N, s = 0, χ a =0, Where N represents the number of traffic zones in the study area, the optimal solution G of the model is obtained. min ; Step 3.2) Calculate the current urban spatial job-housing balance. The specific calculation formula is as follows: Where P represents the urban spatial job-housing balance, and G... std This refers to the standard one-way commute time in the city. Step 3.3) The average effective path travel time c between traffic zones is collected. ij Number of working residents in the traffic community: O ia Number of job positions D ja The total number of job positions in job type A that are yet to be filled (χ) a Substituting into the aforementioned urban spatial job-housing balance optimization model, and setting n = m = N and s = 1, we obtain the optimal solution of the model. Where N represents the number of traffic zones in the study area; Step 3.4), by To achieve the goal of promoting a work-life balance, namely the optimal travel volume, we need to determine the optimal travel volume. The number of newly added job positions within the community was obtained from Traffic zones that should be prioritized for improving commuting efficiency.

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