Urban residential land in-road parking management method based on SWOT-AHP analysis method

Abstract

The invention relates to an urban residential road parking management method based on an SWOT-AHP analysis method. The method comprises the following steps: step 1, site selection is carried out on road parking spaces based on ArcGIS and SWOT-AHP; 2, the number of parking spaces in the urban residential road is planned; 3, calculating the charging rate of the parking spaces in the urban residential road; 4, taking differentiated fine measures according to different use conditions of the parking spaces; and step 5, periodically evaluating the management effect based on an SWOT-AHP analysis method, and dynamically perfecting the management system. According to the in-road parking management system for the urban residential land, the dynamic vitality of the site selection method for the urban residential land can be effectively improved, the site selection and quantity planning strategy can be widely applied to various scenes, and a brand-new and reliable solution thought is provided for self-dynamic maintenance and improvement of the in-road parking management system for the urban residential land.

Description

Technical Field

[0001] The invention belongs to the technical field of traffic engineering, and in particular relates to an on-street parking management method for urban residential land based on a SWOT-AHP analysis method. Background Art

[0002] On-street parking spaces for residential land refer to temporary or permanent parking spaces for motor vehicles set up on roads adjacent to residential land. The setting of on-street parking spaces for urban residential land can reduce the parking pressure of residents. The SWOT analysis method can comprehensively analyze the favorable and unfavorable factors inside and outside the system, and guide the development timing and improvement direction of the plan. AHP can quantitatively analyze the weight of the impact of various factors affecting the management system on the target, so as to guide the scientific focus of the plan improvement process. The above method has been applied to the reconstruction of the old city of Dalian, the standardization construction of smart cities in Zhejiang Province, and the land use analysis of Bharatpur City. Its importance has been fully demonstrated in the construction of smart cities today. In the field of urban intelligent transportation, the existing on-street parking management system for urban residential land is not sound and reasonable. There are problems such as unclear responsibilities, lack of scientific charging standards, lack of supervision of illegal behaviors, and arbitrary marking of parking spaces, which leads to the arbitrary setting of on-street parking spaces, widespread random parking of vehicles, illegal occupation of roads, green belts, and sidewalks, which seriously affects the road traffic capacity, interferes with dynamic and static traffic, and eventually becomes a high-incidence point for accidents. With the promotion of on-street parking spaces in urban residential land and the improvement of relevant laws and policies, the improvement of on-street parking management system in residential land has practical experience and legal basis. Therefore, it is particularly important to build a sound and efficient on-street parking management system for urban residential land based on the current situation of on-street parking in urban residential land and relevant legal basis.

[0003] Based on this, there is an urgent need for an on-street parking management method for urban residential land based on the SWOT-AHP analysis method. By analyzing the characteristics of on-street parking management in urban residential land, a self-improvement method for the on-street parking management system of urban residential land that is self-dynamically improved can be developed. Summary of the invention

[0004] In order to solve the problems of unreasonable parking space planning, lack of scientific parking fee standards, lack of supervision of illegal behaviors, rigid system response, etc. caused by the imperfect on-street parking management system of urban residential land, the present invention proposes an on-street parking management method for urban residential land based on SWOT-AHP analysis method.

[0005] The present invention relates to an on-street parking management method for urban residential land based on SWOT-AHP analysis method, comprising the following steps:

[0006] Step 1: Select the location of on-street parking spaces based on ArcGIS and SWOT-AHP;

[0007] Step 2: Plan the number of on-street parking spaces in urban residential areas;

[0008] Step 3, calculating the charging rate of on-street parking spaces in urban residential areas;

[0009] Step 4: Take differentiated penalty measures according to different usage of parking spaces;

[0010] Step 5: Periodically evaluate management effectiveness based on SWOT-AHP analysis and dynamically improve the management system.

[0011] Furthermore, step 1 includes the following steps:

[0012] Step 1-1: Import the residential road network data, population density distribution data, traffic condition data of roads adjacent to residential land, and existing parking space data of residential land into ArcGIS. After running the spatial analysis tool, comprehensively consider traffic accessibility, land use, and environmental constraints to view and analyze the parking potential of different locations.

[0013] Step 1-2: Classify parking spaces into concentrated and dispersed types according to their distribution, and classify parking spaces into short-distance parking spaces, long-distance parking spaces, and medium-distance parking spaces according to their walking accessibility;

[0014] Step 1-3: Use SWOT-AHP analysis method to analyze the effect of parking space location selection.

[0015] Furthermore, steps 1-3 include the following steps:

[0016] Determine the factors to be considered in the site selection decision, including residents' support for improving parking conditions, potential application of technological innovation, impact on dynamic traffic, policy support, and excessive occupation of parking spaces by external vehicles; construct a hierarchical model to decompose the decision-making problem into the target layer, i.e. the optimal site selection decision, the criterion layer, i.e. S, W, O, T, and the scheme layer, i.e. the specific site selection scheme; compare each element pairwise according to the analytic hierarchy process, assign values ​​to the elements according to their importance, and obtain the criterion layer AHP judgment matrix L as follows; use the 1-9 scale method to compare and score the criterion layer factors pairwise according to the degree of importance, and the scale from 1 to 9 indicates that the importance of the former is increasing in sequence compared with the latter between the two elements being compared;

[0017]

[0018] Among them, L represents the criterion-level AHP judgment matrix, S represents advantage, W represents disadvantage, O represents opportunity, and T represents threat;

[0019] Calculate the mth power of the weight vector of the elements in the judgment matrix L of the criterion layer to obtain an m-dimensional vector

[0020]

[0021] in, represents the element vector in the judgment matrix, k represents the number of rows of the element in the judgment matrix, j represents the number of columns of the element in the judgment matrix, a kj represents the element in row k and column j;

[0022] The element vector in the judgment matrix Normalize and get the weight vector ω of the kth row k :

[0023]

[0024] Among them, ω k represents the weight vector of the kth row, is the sum of the m-dimensional vectors of the j-column elements in the judgment matrix;

[0025] Solve for the largest eigenvalue:

[0026]

[0027] m is the number of columns in the judgment matrix, (Lω) k is the judgment matrix L and the weight vector ω k The kth element after multiplication, ω is the weight vector of the kth row of the judgment matrix L;

[0028] Calculate the consistency test index CI:

[0029]

[0030] Among them, CI is the consistency test index, λ max Represents the maximum eigenvalue of the judgment matrix L;

[0031] Calculate the random consistency index CR of the judgment matrix L:

[0032]

[0033] Among them, RI represents the average random consistency index, which is a predetermined value; when the order of the judgment matrix is ​​3, RI = 0.58; when the order of the judgment matrix is ​​4, RI = 0.89; when the order of the judgment matrix is ​​5, RI = 1.12; when the order of the judgment matrix is ​​6, RI = 1.26;

[0034] If CR≥0.1, it means that the data is unreliable and step 1 needs to be repeated to reconstruct the criterion layer AHP judgment matrix until CR<0.1 is met before proceeding to the next step; if CR<0.1, it means that the consistency test has passed;

[0035] The sub-criteria layer judgment matrix is ​​constructed as follows:

[0036]

[0037] in, Indicates the importance of sub-criterion S1 compared with itself, Sub-criteria S1 and S n The importance of comparison, Sub-criteria S n How important it is when compared to itself; Indicates the importance of sub-criterion W1 compared with itself, Subcriteria W1 and W n The importance of comparison, Subcriteria W n How important it is when compared to itself; Indicates the importance of sub-criterion O1 when compared with itself, Sub-criteria O1 and O n The importance of comparison, Sub-criteria O n How important it is when compared to itself; Indicates the importance of sub-criterion T1 when compared with itself, is the importance of the comparison between sub-criteria T1 and T2, Subcriteria T n The importance when compared with itself; the number of elements in each sub-criteria layer is the same, n is the number of elements in each sub-criteria layer;

[0038] The factors of each sub-criteria layer are compared pairwise, scored, weight vectors calculated, characteristic roots calculated, and consistency tests performed;

[0039] Multiply the weight of each criterion layer by the weight of the factors in the sub-criterion layer under each criterion layer to obtain the comprehensive weight of each sub-criterion layer factor;

[0040] The final weights of the S, W, O, and T layers are calculated according to the following formula:

[0041]

[0042] Among them, S z is the weight of each indicator under the total advantage index, W z is the weight of each indicator under the total disadvantage indicator, O zis the weight of each indicator under the total opportunity indicator, T z is the weight of each indicator under the total threat index; z is the number of indicators under the total advantage, total disadvantage, total opportunity, and total threat index, z = 1, 2, ..., n;

[0043] The four variables of advantage S, disadvantage W, opportunity O and threat T are used as semi-axes to form a coordinate system. S corresponds to the horizontal coordinate X1 and the vertical coordinate 0; W corresponds to the horizontal coordinate -X2 and the vertical coordinate 0; O corresponds to the horizontal coordinate 0 and the vertical coordinate Y1; T corresponds to the horizontal coordinate 0 and the vertical coordinate -Y2; locate in the coordinate system, connect the four points in sequence, and get the strategic quadrilateral; calculate the coordinates of the center of gravity M of the strategic quadrilateral as follows:

[0044]

[0045] In the formula, x represents the horizontal coordinate of the center of gravity M, y represents the vertical coordinate of the center of gravity M, and x j represents the sum of X1 and -X2, y j Represents the sum of Y1 and -Y2; total advantage strength X1, total disadvantage strength -X2, total opportunity strength Y1 and total threat strength -Y2;

[0046] The analysis results of SWOT-AHP show that there are four strategies: WO, WT, SO, and ST; WO is a reversal strategy, WT is a defensive strategy, SO is a pioneering strategy, and ST is a diversification strategy; the WO and WT strategies indicate that the next cycle will be relocated according to the actual threats in the order of step 1; the SO and ST strategies indicate that the system implementation effect is good and no readjustment is needed in the next cycle.

[0047] Furthermore, in step 2, the method for calculating the number of on-street parking spaces in urban residential land is as follows:

[0048]

[0049] N——the number of on-street parking spaces required for urban residential land, in units;

[0050] P——The annual number of motor vehicles in urban residential areas, in units of vehicles;

[0051] b——The number of existing parking spaces in urban residential areas in the target year, in units;

[0052] d——the utilization coefficient of on-street parking spaces in urban residential areas in the target year, that is, the proportion of parking needs of residents in the area that can be met by on-street parking spaces;

[0053] u——the area of ​​on-street parking spaces in urban residential areas, in square meters;

[0054] v——the usage intensity of on-street parking spaces in urban residential areas, i.e. the usage intensity per unit time and per unit area;

[0055] Substitute the above parameters into formula (16) to plan the number of on-street parking spaces.

[0056] Furthermore, the step 3 includes the following steps:

[0057] Step 3-1: Select factors influencing travel utility and investigate relevant travel data;

[0058] Step 3-2: Construct the travel utility function U based on the MNL model it and the probability function P it , the target rate is determined by numerical calculation and analysis;

[0059] Step 3-3: Develop a charging plan based on the progressive charging mechanism and the differentiated charging mechanism.

[0060] Furthermore, in step 3-1, the travel utility influencing factor X its It includes factors affecting traffic and factors affecting residents' travel; the factors affecting traffic include travel expenses and travel time; the factors affecting residents' travel include the presence or absence of private cars, age, type of work, monthly income and annual income; the corresponding travel data are investigated using the RP survey method to study residents' actual travel behavior, and the SP survey method to study residents' choice intentions; the use of parking spaces is evaluated based on the survey data obtained.

[0061] Furthermore, step 3-2 specifically includes the following steps:

[0062] Based on the MNL model, it is believed that the factors affecting travel utility conform to the price leverage mechanism. The relationship between the utility of traveler t choosing travel mode i and the corresponding expenditure is:

[0063] U it =βF i +A (17)

[0064] Where U it is the observable attribute in the travel utility function, β is the indicator sensitivity parameter, β<0, and A is other factors that affect the utility value;

[0065] Through data analysis, it is found that the utility function U it With random variable X its There is a linear relationship between them, and the following equation is obtained:

[0066]

[0067] Where, X its ASC is the factor affecting travel utility. it To determine the variable, β sis the weight parameter of the random item variable, and n is the number of random item variables;

[0068] After processing the data, the weight parameters of the above-mentioned deterministic variables and random variables were measured and calibrated by BIOGEME, and then the data variables with insignificant effects were detected and removed by t-test, and the significant variables and the corresponding calibration of each significant variable were obtained, and the deterministic variables and weight parameters were obtained;

[0069] According to the random utility theory, it is assumed that the principle of traveling residents is to choose the travel mode by maximizing random utility. Based on the MNL model, the theory of statistically analyzing and predicting the behavior and decision-making of travelers is called the multinomial logit theory. The probability of each transportation mode is:

[0070]

[0071] Where P i represents the possibility of choosing transportation mode i, U i Represents the observable attributes in the travel utility function;

[0072] According to the expected perceived benefit theory, when the relevant costs increase, the expected perceived benefits will increase accordingly, but the rate of change will gradually decrease; at the same time, the probability of the travel mode decreases as the relevant costs increase. The derivative of the expected value with respect to the relevant costs has the following relationship with the probability of choosing the travel mode i:

[0073]

[0074] In the formula, Q it (F i ) is the expected benefit value of the residents traveling, P it F is the possibility of residents choosing travel mode, i Amount of money spent on travel expenses;

[0075] By integrating random utility theory and price leverage mechanism, and integrating both sides of equation (20), we can obtain the functional expression of expected perceived benefit on travel expenditure:

[0076]

[0077] According to the survey data obtained by the RP-SP survey method in step 3, the data of various factors affecting urban traffic roads and residents' travel and the various indicator parameters calibrated by BIOGEME and t-test are substituted into formula (21) to obtain the expected perceived benefit function of on-street parking. Then, the optimal rate is determined by numerical calculation and analysis. That is, by successively determining the slope values ​​of different points on the expected perceived benefit function graph, the corresponding expected perceived benefit values ​​and charging rates are calculated. The charging rate corresponding to the slope threshold is the optimal rate.

[0078] Furthermore, in step 3-3, based on the charging rate obtained in step 3-2, the progressive charging mechanism makes floating adjustments to the base rate according to different parking durations; the differentiated charging mechanism makes adjustments based on the base rate according to different time periods of the day and different locations to determine the optimal on-street parking charging plan.

[0079] Furthermore, in step 4, the differentiated penalty measures are: for residents who continue to use the parking space in the next quarter, the usage rate for the next cycle will be increased; for residents who no longer use the parking space in the next quarter, graded fines will be imposed based on any illegal parking behaviors that occur during use.

[0080] Furthermore, in step 5, the key factors that affect the implementation effect of the management system are determined, including intelligent management, policy support, irregular management, road resource development and utilization, and intensified parking conflicts. A hierarchical model is constructed, including the target layer, the criterion layer, and the scheme layer. The consistency test index and the coordinates of the center of gravity of the strategic quadrilateral are calculated using formulas (1)-(15), and then the strategy type is determined according to the distribution position of the coordinates of the center of gravity of the strategic quadrilateral. If the analysis result is WO or WT strategy, it indicates that the next cycle will be re-sited according to the actual threats in the order of step 1; SO or ST strategy indicates that the system does not need to be readjusted in the next cycle.

[0081] Beneficial Effects

[0082] The present invention mainly proposes an on-street parking management method for urban residential land based on the SWOT-AHP analysis method. By analyzing the on-street parking management characteristics of urban residential land, a self-improvement method for the on-street parking management system of urban residential land is formulated. The on-street parking management system for urban residential land proposed by the present invention can effectively improve the dynamic vitality of the site selection method for urban residential land. The site selection and quantity planning strategies can be widely used in various scenarios, providing a new and reliable solution for the self-dynamic maintenance and improvement of the on-street parking management system for residential land. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 Construct a flow chart for the management system of the present invention;

[0084] Figure 2 The flow chart of solving the on-road toll rate of the present invention;

[0085] Figure 3 This is the strategic quadrilateral diagram calculated by the SWOT-AHP analysis method in this invention. DETAILED DESCRIPTION

[0086] The following combination Figures 1 to 3 This embodiment will be described in detail.

[0087] The present invention relates to an on-street parking management method for urban residential land based on SWOT-AHP analysis method, comprising the following steps:

[0088] Step 1: Select the location of on-street parking spaces based on ArcGIS and SWOT-AHP.

[0089] Step 1-1: Import residential road network data, population density distribution data, traffic conditions of roads adjacent to residential land, and existing parking space data of residential land into ArcGIS. After running spatial analysis tools, comprehensively consider traffic accessibility, land use, and environmental constraints to view and analyze the parking potential of different locations.

[0090] See below Figure 1 To elaborate.

[0091] Step 1-2: Divide parking spaces into centralized and dispersed settings according to their distribution; divide parking spaces into short-distance parking spaces, long-distance parking spaces and medium-distance parking spaces according to their walking accessibility.

[0092] Step 1-3: Use SWOT-AHP analysis method to analyze the effect of parking space location selection.

[0093] Determine the factors to be considered in the site selection decision, including residents' support for improving parking conditions, potential application of technological innovation, impact on dynamic traffic, policy support, and excessive occupation of parking spaces by external vehicles; construct a hierarchical model to decompose the decision-making problem into the target layer, i.e. the optimal site selection decision, the criterion layer, i.e. S, W, O, T, and the scheme layer, i.e. the specific site selection scheme; compare each element pairwise according to the analytic hierarchy process, assign values ​​to the elements according to their importance, and obtain the criterion layer AHP judgment matrix as follows; use the 1-9 scale method to compare and score the criterion layer factors pairwise according to the degree of importance, and the scale from 1 to 9 indicates that the importance of the former is increasing in sequence compared with the latter between the two elements being compared;

[0094]

[0095] Among them, L represents the criterion-level AHP judgment matrix, S represents advantage, W represents disadvantage, O represents opportunity, and T represents threat;

[0096] Calculate the power of the weight vector of the elements in the judgment matrix L of the criterion layer to obtain an m-dimensional vector

[0097]

[0098] in, represents the element vector in the judgment matrix, k represents the number of rows of the element in the judgment matrix, j represents the number of columns of the element in the judgment matrix, a kj Represents the element in row k and column j;

[0099] The element vector in the judgment matrix Normalize and get the weight vector ω of the kth row k :

[0100]

[0101] Among them, ω k represents the weight vector of the kth row, is the sum of the m-dimensional vectors of the j-column elements in the judgment matrix; solve the maximum characteristic root:

[0102]

[0103] m is the number of columns in the judgment matrix, (Lω) k is the judgment matrix L and the weight vector ω k The kth element after multiplication, ω is the weight vector of the kth row of the judgment matrix L;

[0104] Calculate the consistency test index CI:

[0105]

[0106] Among them, CI is the consistency test index, λ max Represents the maximum eigenvalue of the judgment matrix L;

[0107] Calculate the random consistency index CR of the judgment matrix L:

[0108]

[0109] Among them, RI represents the average random consistency index, which is a predetermined value; when the order of the judgment matrix is ​​3, RI = 0.58; when the order of the judgment matrix is ​​4, RI = 0.89; when the order of the judgment matrix is ​​5, RI = 1.12; when the order of the judgment matrix is ​​6, RI = 1.26;

[0110] If CR≥0.1, it means that the data is unreliable and step 1 needs to be repeated to reconstruct the criterion layer AHP judgment matrix until CR<0.1 is met before proceeding to the next step; if CR<0.1, it means that the consistency test has passed;

[0111] The sub-criteria layer judgment matrix is ​​constructed as follows:

[0112]

[0113] in, Indicates the importance of sub-criterion S1 compared with itself, Sub-criteria S1 and S n The importance of comparison, Sub-criteria S n How important it is when compared to itself; Indicates the importance of sub-criterion W1 compared with itself, Subcriteria W1 and W n The importance of comparison, Subcriteria W n How important it is when compared to itself; Indicates the importance of sub-criterion O1 when compared with itself, Sub-criteria O1 and O n The importance of comparison, Sub-criteria O n How important it is when compared to itself; Indicates the importance of sub-criterion T1 when compared with itself, is the importance of the comparison between sub-criteria T1 and T2, Subcriteria T n The importance when compared with itself; the number of elements in each sub-criteria layer is the same, n is the number of elements in each sub-criteria layer;

[0114] The factors of each sub-criteria layer are compared pairwise, scored, weight vectors calculated, characteristic roots calculated, and consistency tests performed;

[0115] Multiply the weight of each criterion layer by the weight of the factors in the sub-criterion layer under each criterion layer to obtain the comprehensive weight of each sub-criterion layer factor;

[0116] The final weights of the criteria S, W, O, and T layers are calculated according to the following formula:

[0117]

[0118] Among them, S z is the weight of each indicator under the total advantage index, W z is the weight of each indicator under the total disadvantage indicator, O z is the weight of each indicator under the total opportunity indicator, T z is the weight of each indicator under the total threat index; z is the number of indicators under the total advantage, total disadvantage, total opportunity and total threat index, z = 1, 2, …, n.

[0119] The four variables of advantage S, disadvantage W, opportunity O and threat T are used as semi-axes to form a coordinate system. S corresponds to the horizontal coordinate X1 and the vertical coordinate 0; W corresponds to the horizontal coordinate -X2 and the vertical coordinate 0; O corresponds to the horizontal coordinate 0 and the vertical coordinate Y1; T corresponds to the horizontal coordinate 0 and the vertical coordinate -Y2; locate in the coordinate system, connect the four points in sequence, and get the strategic quadrilateral; calculate the coordinates of the center of gravity M of the strategic quadrilateral as follows:

[0120]

[0121] In the formula, x represents the horizontal coordinate of the center of gravity M, y represents the vertical coordinate of the center of gravity M, and x j represents the sum of X1 and -X2, y j Represents the sum of Y1 and -Y2; total advantage strength X1, total disadvantage strength -X2, total opportunity strength Y1 and total threat strength -Y2;

[0122] The analysis results of SWOT-AHP show that there are four strategies: WO, WT, SO, and ST; WO is a reversal strategy, WT is a defensive strategy, SO is a pioneering strategy, and ST is a diversification strategy; the WO and WT strategies indicate that the next cycle will be relocated according to the actual threats in the order of step 1; the SO and ST strategies indicate that the system implementation effect is good and no readjustment is needed in the next cycle.

[0123] Step 2: Plan the number of on-street parking spaces in urban residential areas.

[0124] After the site selection of the on-street parking lot is completed, a parking space quantity planning model is established according to the parking space demand. Based on the relevant parameters such as the number of motor vehicles in the urban residential land, the number of existing parking spaces, the on-street parking space utilization coefficient, the area occupied by the on-street parking spaces, and the intensity of on-street parking space utilization, the reasonable on-street parking space demand for urban residential land is determined and calculated according to the following formula:

[0125]

[0126] N——the number of on-street parking spaces required for urban residential land, in units;

[0127] P——The annual number of motor vehicles in urban residential areas, in units of vehicles;

[0128] b——The number of existing parking spaces in urban residential areas in the target year, in units;

[0129] d——the utilization coefficient of on-street parking spaces in urban residential areas in the target year, that is, the proportion of parking needs of residents in the area that can be met by on-street parking spaces;

[0130] s——the area of ​​on-street parking spaces in urban residential areas, in square meters;

[0131] v——the usage intensity of on-street parking spaces in urban residential areas, i.e. the usage intensity per unit time and per unit area;

[0132] Substitute the above parameters into formula (37) to realize the planning of the number of on-street parking spaces.

[0133] Step 3: Calculate the charging rate for on-street parking spaces in urban residential areas.

[0134] Step 3-1: Select factors affecting travel utility and investigate relevant travel data.

[0135] Factors affecting travel utility X its It includes factors affecting traffic and factors affecting residents' travel; the factors affecting traffic include travel expenses and travel time; the factors affecting residents' travel include the presence or absence of private cars, age, type of work, monthly income and annual income; the corresponding travel data are investigated using the RP survey method to study residents' actual travel behavior, and the SP survey method to study residents' choice intentions; the use of parking spaces is evaluated based on the survey data obtained.

[0136] Step 3-2: Construct the travel utility function U based on the MNL model it and the probability function P it , the target rate is determined by numerical calculation and analysis.

[0137] Based on the MNL model, it is believed that the factors affecting travel utility conform to the price leverage mechanism. The relationship between the utility of traveler t choosing travel mode i and the corresponding expenditure is:

[0138] U it =βF i +A (38)

[0139] Where U it is the observable attribute in the travel utility function, β is the indicator sensitivity parameter, β<0, and A is other factors that affect the utility value;

[0140] Through data analysis, it is found that the utility function U it With random variable X its There is a linear relationship between them, and the following equation is obtained:

[0141]

[0142] Where, X its ASC is the factor affecting travel utility. it To determine the variable, β s is the weight parameter of the random item variable, and n is the number of random item variables;

[0143] After processing the data, the weight parameters of the deterministic and random variables were measured and calibrated by BIOGEME, and then the data variables with insignificant effects were detected and removed by t-test, and the significant variables and the corresponding calibration of each significant variable were obtained, and the deterministic variables and weight parameters were obtained;

[0144] According to the random utility theory, it is assumed that the principle of traveling residents is to choose the travel mode by maximizing random utility. Based on the MNL model, the theory of statistically analyzing and predicting the behavior and decision-making of travelers is called the multinomial logit theory. The probability of each transportation mode is:

[0145]

[0146] Where P i represents the possibility of choosing transportation mode i, U i Represents the observable attributes in the travel utility function;

[0147] According to the expected perceived benefit theory, when the relevant costs increase, the expected perceived benefits will increase accordingly, but the rate of change will gradually decrease; at the same time, the probability of the travel mode decreases as the relevant costs increase. The derivative of the expected value with respect to the relevant costs has the following relationship with the probability of choosing the travel mode i:

[0148]

[0149] In the formula, Q it (F i ) is the expected benefit value of the residents traveling, P it F is the possibility of residents choosing travel mode, i Amount of money spent on travel expenses;

[0150] By integrating random utility theory and price leverage mechanism, and integrating both sides of equation (41), we can obtain the functional expression of expected perceived benefit on travel expenditure:

[0151]

[0152] According to the survey data obtained by the RP-SP survey method in step 3, the data of various factors affecting urban traffic roads and residents' travel and the various indicator parameters calibrated by BIOGEME and t-test are substituted into formula (42) to obtain the expected perceived benefit function of on-street parking. Then, the optimal rate is determined by numerical calculation and analysis. That is, by successively determining the slope values ​​of different points on the expected perceived benefit function graph, the corresponding expected perceived benefit values ​​and charging rates are calculated. The charging rate corresponding to the slope threshold is the optimal rate.

[0153] Step 3-3: Develop a charging plan based on the progressive charging mechanism and the differentiated charging mechanism.

[0154] Based on the charging rate obtained in step 3-2, the progressive charging mechanism makes floating adjustments to the base rate according to different parking durations; the differentiated charging mechanism makes adjustments based on the base rate according to different time periods and locations of the day to determine the optimal on-street parking charging plan.

[0155] Step 4: Take differentiated penalty measures based on the different usage of parking spaces.

[0156] In order to maintain the stability of parking order, differentiated fines are adopted. For residents who continue to use parking spaces in the next quarter, the rate for the next use cycle will be increased. For residents and non-residents who no longer use the parking spaces, graded fines will be imposed according to the degree of harm caused by illegal parking.

[0157] Step 5: Periodically evaluate management effectiveness based on SWOT-AHP analysis and dynamically improve the management system.

[0158] After the management system has been implemented for a period of time, its implementation effect is analyzed and the key factors affecting the implementation effect of the management system are determined, including intelligent management, policy support, irregular management, road resource development and utilization, and intensified parking conflicts. A SWOT hierarchical structure model is constructed, including the target layer, the criterion layer, and the program layer. Then, the consistency test index and the coordinates of the center of gravity of the strategic quadrilateral are calculated using formulas (22)-(36). The strategic type is determined based on the distribution of the coordinates of the center of gravity of the strategic quadrilateral. If the analysis result is WO or WT strategy, it indicates that the next cycle will be re-sited according to the actual threats in the order of step 1. SO or ST strategy indicates that the system does not need to be readjusted in the next cycle.

[0159] Example

[0160] Step 1: Select the location of on-street parking spaces based on ArcGIS and SWOT-AHP.

[0161] Import residential road network data, population density distribution data, traffic conditions of roads adjacent to residential land, and existing parking space data of residential land into ArcGIS, and run spatial analysis tools to view and analyze the parking space location distribution area provided. Select centralized or decentralized settings based on the analysis results; set short-distance parking spaces, long-distance parking spaces, and medium-distance parking spaces.

[0162] In order to scientifically evaluate the rationality of the site selection effect, a SWOT analysis hierarchy consisting of the target layer, criterion layer and program layer is constructed. The target layer is the best site selection plan, the criterion layer is the internal advantages S, internal disadvantages W, external opportunities O and external threats T; the sub-criterion layer is the various factors under the criterion layer, including intelligent management, multi-subject coordinated participation, and policy support under the criterion layer S, insufficient parking spaces, irregular management, and aging facilities under the criterion layer W, new parking space planning, intelligent upgrading, and road resource development and utilization under the criterion layer O, and increased traffic pressure on residential roads, intensified parking conflicts, and difficult law enforcement under the criterion layer T; the program layer is the four strategic measures of SO, ST, WO, and WT.

[0163] Then construct the criterion layer AHP judgment matrix L and the sub-criterion layer judgment matrix as follows. Use the 1-9 scale method to compare and score the criterion layer factors according to the degree of importance. The scale 1-9 indicates that the importance of the former is increasing compared with the latter between the two elements being compared.

[0164] Then, according to formulas (2)-(15), the results are shown in Tables 1-6. Table 1 is the criterion layer judgment matrix for site selection effect evaluation, Tables 2-5 are the sub-criterion layer judgment matrices, and Table 6 is the comprehensive weight of the influencing effect.

[0165] Table 1 Criteria layer L judgment matrix

[0166]

[0167] Table 2 Judgment matrix of sub-criteria layer B1

[0168]

[0169] Table 3 Sub-criteria layer B2 judgment matrix

[0170]

[0171] Table 4 Sub-criteria layer B3 judgment matrix

[0172]

[0173] Table 5 Sub-criteria layer B4 judgment matrix

[0174]

[0175] Table 6 Results of weights of indicators affecting the effect of on-street location selection for residential land

[0176]

[0177] According to formulas (32)-(35), the comprehensive weights of each criterion layer are: the comprehensive weight of criterion layer S is 0.21188, the comprehensive weight of criterion layer W is 0.24684, the judgment matrix of criterion layer O is 0.25022, and the comprehensive weight of criterion layer T is 0.29023. Then establish a plane rectangular coordinate system, in which the positive direction of the X axis is O and the negative direction is T; the positive direction of the Y axis is S and the negative direction of the Y axis is W. Then according to formula (36), the coordinates of the center of gravity are (-0.04001, -0.03496), which is located in the WT quadrant. Then step 1 should be repeated to readjust the site selection plan, such as re-planning the quantity, changing the site selection location, etc., to achieve the optimization of the site selection effect.

[0178] Step 2: Plan the number of on-street parking spaces in urban residential areas.

[0179] After investigation, the number of motor vehicles in a certain city's residential land is 1996, the number of existing parking spaces is 1486, the on-street parking space utilization coefficient is 1.2, the on-street parking space area is 7712 square meters, and the on-street parking space utilization intensity is 1.3 to determine the reasonable number of on-street parking spaces required for urban residential land, and calculate it according to the following formula:

[0180]

[0181] Substituting the parameters into the formula, we get:

[0182]

[0183] Step 3: Calculate the charging rate for on-street parking spaces in urban residential areas.

[0184] Figure 2 The present invention is a flow chart for solving the on-road toll rate.

[0185] Select the factors affecting travel utility and investigate the corresponding travel data. According to the travel situation of residents in a certain city, the travel modes are divided into three paid travel modes: private cars (i=1), taxis (i=2) and public transportation (i=3).

[0186] On the influencing factor X in random utility theory its The selection is as follows:

[0187] (1) Factors influencing transportation (i.e., factors influencing the choice of limbs): travel expenses and travel time;

[0188] (2) Factors affecting residents’ travel: whether they own a private car, age, job type, and monthly (annual) income.

[0189] The RP survey method focuses on the actual travel behavior of residents, while the SP survey method focuses on their choice intention under specific conditions. Then, based on the MNL model, the travel utility function U is constructed. it and the probability function P it , the target rate is determined by numerical calculation and analysis.

[0190] Assuming that each influencing factor conforms to the price lever mechanism, the relationship between the utility of traveler t choosing travel mode i and the corresponding expenditure is:

[0191] U it =βF i +A (45)

[0192] Where U it is the observable attribute in the travel utility function, β is the indicator sensitivity parameter (β<0), and A is other factors affecting the utility value.

[0193] Through data analysis, it is found that the utility function U it With random variable X its There is a linear relationship between them, namely:

[0194]

[0195] Where, X its is a random variable; ASC it is the determinant variable; s is the weight parameter of the random item variable; n is the number of random item variables.

[0196] After processing the data, the weight parameters of the deterministic and random variables of the MNL model were measured and calibrated by BIOGEME, and then the t-test was used to detect and remove the data variables with insignificant effects, and the significant variables and the corresponding calibration of each significant variable were obtained. The indicator data such as deterministic variables and weight parameters were obtained as shown in Table 7.

[0197] Table 7 Parameter calibration and corresponding index data calculation

[0198] Name Value t-test Name Value t-test <![CDATA[ASC1]]> 1.05 1.13 <![CDATA[β4]]> 2.68 5.74 <![CDATA[ASC2]]> -0.461 -0.95 <![CDATA[β5]]> -0.402 -1.98 <![CDATA[β1]]> -0.298 -3.54 <![CDATA[β6]]> -0.298 -2.02 <![CDATA[β2]]> -0.135 -3.29 <![CDATA[β7]]> 0.33 1.97 <![CDATA[β3]]> -11.4 -3.27 <![CDATA[β8]]> 0.468 2.04

[0199] For residents who travel, they think that the travel modes are private cars (i=1), taxis (i=2) and public transportation (i=3). The travel utilities of the three travel modes are:

[0200] U 1t =ASC 1t +β1*COST1+β3*TIME1+β4*CAR+β5*AGE+β6*WORK+β7*INCOME1 (47)

[0201]

[0202] U 3t =β2*COST3+β3*TIME3 (49)

[0203] According to the random utility theory, it is assumed that the principle of traveling residents is to choose the travel mode by maximizing random utility. Based on the MNL model, the theory of statistically analyzing and predicting the behavior and decision-making of travelers is called the multinomial logit theory. The probabilities of the three transportation modes are:

[0204]

[0205]

[0206] In the expected perceived benefit theory, the derivative of the expected value with respect to the relevant cost has the following relationship with the probability of choosing transportation mode i:

[0207]

[0208] In the formula, Q it (F i ) is the expected benefit value of the residents traveling, P it F is the possibility of residents choosing travel mode, i The amount of money spent on travel expenses.

[0209] By integrating random utility theory and price leverage mechanism, and integrating equation (58), we can obtain the functional expression of expected perceived benefit on travel expenditure:

[0210]

[0211] Substituting the traffic road and travel data of a certain urban area and the above parameters into equation (59), the expected perceived benefit function of on-street parking is obtained as:

[0212]

[0213] The numerical calculation analysis method calculates the corresponding expected perceived benefit value and charging rate by changing the slope of the expected perceived benefit function graph in turn. As the slope decreases, the expected perceived benefit value increases, the rate increases, but the marginal utility of the rate decreases. When the slope is set to 0.010, the rate increase exceeds 0.5 yuan / hour, indicating that further raising the rate will have limited benefits and may reduce residents' satisfaction. Therefore, the rate of 9.97 yuan / hour corresponding to the slope of 0.010 is set as the optimal rate.

[0214] Then implement progressive charging mechanism and differentiated charging mechanism to determine the optimal charging plan.

[0215] According to the different parking durations, a progressive charging mechanism is adopted, that is, different charging rates are adopted for different parking durations, and the longer the parking duration, the higher the charge; according to the different time periods and locations of the day, a differentiated charging mechanism is adopted, that is, different charging price mechanisms are implemented for differentiated factors such as different times and locations. Based on the above cost thresholds, progressive charging and differentiated charging are adopted to determine the final time and space differentiated charging strategies, see Table 8.

[0216] Table 8: Time-space differentiated roadside parking fee scheme

[0217]

[0218] Step 4: Take differentiated penalty measures based on the different usage of parking spaces.

[0219] For residents who continue to use the parking spaces in the next quarter, the rate for the next use cycle will be increased. For residents who no longer use the spaces, graded fines will be imposed based on the degree of harm caused by illegal parking.

[0220] Step 5: Periodically evaluate management effectiveness based on SWOT-AHP analysis and dynamically improve the management system.

[0221] After the management system has been implemented for a period of time, a SWOT hierarchical model is constructed, including the target layer, the criterion layer, and the program layer; then, the consistency test index and the coordinates of the center of gravity of the strategic quadrilateral are calculated using formulas (1)-(15), and then the strategic type is determined according to the distribution position of the coordinates of the center of gravity of the strategic quadrilateral. The calculation results are shown in Tables 9-14. Table 9 is the judgment matrix of the criterion layer of the implementation effect of the management system, Tables 10-13 are the judgment matrices of the sub-criterion layer, and Table 14 is the calculation result of the comprehensive weight.

[0222] Table 9 Criteria layer judgment matrix

[0223]

[0224] Table 10 S sub-criteria layer judgment matrix

[0225]

[0226] Table 11. Judgment matrix of sub-criteria layer of W

[0227]

[0228] Table 12 Sub-criteria layer judgment matrix of O

[0229]

[0230] Table 13 T's sub-criteria layer judgment matrix

[0231]

[0232] Table 14 Results of weights of influencing indicators of on-street parking management system for residential land

[0233]

[0234] According to the above calculation results, draw Figure 3 The quadrilateral strategic map of the residential land is shown in Figure 1. In this strategic quadrilateral, the center of gravity coordinates are located in the WT conservative strategic quadrant, that is, the on-street parking management mode of the residential land tends to be WT conservative strategy. Specifically, in terms of parking site selection, facing the internal disadvantage of insufficient management resources, the next management cycle should give priority to the use of existing road space, select wide and light-traffic branch roads, avoid areas with frequent resident activities, and reduce conflicts and hidden dangers; in terms of parking space quantity planning, due to limited funds and land resources and rapid growth in parking demand, in the next management cycle, priority should be given to adding a small number of parking spaces in areas with urgent needs and advancing on demand; in terms of parking space charging rates, management costs and residents' acceptance should be considered, cost-oriented pricing should be adopted, and after calculating management costs, charging standards should be determined according to parking turnover rate and utilization rate, and regular evaluation and adjustment should be made to ensure normal management and residents' acceptance.

[0235] The above contents of the present invention are only preferred embodiments of the present invention and are not intended to limit the implementation scheme of the present invention. A person skilled in the art can easily make corresponding changes or modifications based on the main concept and spirit of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope required by the claims.

Claims

1. A method for managing on-street parking in urban residential areas based on SWOT-AHP analysis, characterized in that: The steps include: Step 1: Select the location of on-street parking spaces based on ArcGIS and SWOT-AHP; Step 2: Plan the number of on-street parking spaces in urban residential areas; Step 3, calculating the charging rate of on-street parking spaces in urban residential areas; Step 4: Take differentiated penalty measures according to different usage of parking spaces; Step 5: Periodically evaluate management effectiveness based on SWOT-AHP analysis and dynamically improve the management system.

2. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 1 is characterized in that: Step 1 includes the following steps: Step 1-1: Import the residential road network data, population density distribution data, traffic condition data of roads adjacent to residential land, and existing parking space data of residential land into ArcGIS. After running the spatial analysis tool, comprehensively consider traffic accessibility, land use, and environmental constraints to view and analyze the parking potential of different locations. Step 1-2: Classify parking spaces into concentrated and dispersed types according to their distribution, and classify parking spaces into short-distance parking spaces, long-distance parking spaces, and medium-distance parking spaces according to their walking accessibility; Step 1-3: Use SWOT-AHP analysis method to analyze the effect of parking space location selection.

3. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 2 is characterized in that: Steps 1-3 include the following steps: Determine the factors to be considered in the site selection decision, including residents' support for improving parking conditions, potential application of technological innovation, impact on dynamic traffic, policy support, and excessive occupation of parking spaces by external vehicles; construct a hierarchical model to decompose the decision-making problem into the target layer, i.e. the optimal site selection decision, the criterion layer, i.e. S, W, O, T, and the scheme layer, i.e. the specific site selection scheme; compare each element pairwise according to the analytic hierarchy process, assign values ​​to the elements according to their importance, and obtain the criterion layer AHP judgment matrix L as follows; use the 1-9 scale method to compare and score the criterion layer factors pairwise according to the degree of importance, and the scale from 1 to 9 indicates that the importance of the former is increasing in sequence compared with the latter between the two elements being compared; Among them, L represents the criterion layer AHP judgment matrix, S represents advantage, W represents disadvantage, O represents opportunity, T stands for threat; Calculate the mth power of the weight vector of the elements in the judgment matrix L of the criterion layer to obtain an m-dimensional vector in, represents the element vector in the judgment matrix, k represents the number of rows of the element in the judgment matrix, j represents the number of columns of the element in the judgment matrix, a kj represents the element in row k and column j; The element vector in the judgment matrix Normalize and get the weight vector ω of the kth row k : Among them, ω k represents the weight vector of the kth row, is the sum of the m-dimensional vectors of the j-column elements in the judgment matrix; Solve for the largest eigenvalue: m is the number of columns in the judgment matrix, (Lω) k is the judgment matrix L and the weight vector ω k The kth element after multiplication, ω is the weight vector of the kth row of the judgment matrix L; Calculate the consistency test index CI: Among them, CI is the consistency test index, λ max Represents the maximum eigenvalue of the judgment matrix L; Calculate the random consistency index CR of the judgment matrix L: Among them, RI represents the average random consistency index, which is a predetermined value; when the order of the judgment matrix is ​​3, RI = 0.58; when the order of the judgment matrix is ​​4, RI = 0.89; when the order of the judgment matrix is ​​5, RI = 1.12; when the order of the judgment matrix is ​​6, RI = 1.26; If CR≥0.1, it means that the data is unreliable and step 1 needs to be repeated to reconstruct the criterion layer AHP judgment matrix until CR<0.1 is met before proceeding to the next step; if CR<0.1, it means that the consistency test has passed; The sub-criteria layer judgment matrix is ​​constructed as follows: in, Indicates the importance of sub-criterion S1 compared with itself, Sub-criteria S1 and S n The importance of comparison, Sub-criteria S n How important it is when compared to itself; Indicates the importance of sub-criterion W1 compared with itself, Subcriteria W1 and W n The importance of comparison, Subcriteria W n How important it is when compared to itself; Indicates the importance of sub-criterion O1 when compared with itself, Sub-criteria O1 and O n The importance of comparison, Sub-criteria O n How important it is when compared to itself; Indicates the importance of sub-criterion T1 when compared with itself, is the importance of the comparison between sub-criteria T1 and T2, Subcriteria T n The importance when compared with itself; the number of elements in each sub-criteria layer is the same, n is the number of elements in each sub-criteria layer; The factors of each sub-criteria layer are compared pairwise, scored, weight vectors calculated, characteristic roots calculated, and consistency tests performed; Multiply the weight of each criterion layer by the weight of the factors in the sub-criterion layer under each criterion layer to obtain the comprehensive weight of each sub-criterion layer factor; The final weights of the criteria S, W, O, and T layers are calculated according to the following formula: Among them, S z is the weight of each indicator under the total advantage index, W z is the weight of each indicator under the total disadvantage index, O z is the weight of each indicator under the total opportunity indicator, T z is the weight of each indicator under the total threat index; z is the number of indicators under the total advantage, total disadvantage, total opportunity, and total threat index, z = 1, 2, ..., n; The four variables of advantage S, disadvantage W, opportunity O and threat T are used as semi-axes to form a coordinate system. S corresponds to the horizontal coordinate X1 and the vertical coordinate 0; W corresponds to the horizontal coordinate -X2 and the vertical coordinate 0; O corresponds to the horizontal coordinate 0 and the vertical coordinate Y1; T corresponds to the horizontal coordinate 0 and the vertical coordinate -Y2; locate in the coordinate system, connect the four points in sequence, and get the strategic quadrilateral; calculate the coordinates of the center of gravity M of the strategic quadrilateral as follows: In the formula, x represents the horizontal coordinate of the center of gravity M, y represents the vertical coordinate of the center of gravity M, and x j represents the sum of X1 and -X2, y j Represents the sum of Y1 and -Y2; total advantage strength X1, total disadvantage strength -X2, total opportunity strength Y1 and total threat strength -Y2; The analysis results of SWOT-AHP show that there are four strategies: WO, WT, SO, and ST; WO is a reversal strategy, WT is a defensive strategy, SO is a pioneering strategy, and ST is a diversification strategy; the WO and WT strategies indicate that the next cycle will be relocated according to the actual threats in the order of step 1; the SO and ST strategies indicate that the system implementation effect is good and no readjustment is needed in the next cycle.

4. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 1, characterized in that: In step 2, the number of on-street parking spaces in urban residential areas is calculated as follows: N——the number of on-street parking spaces required for urban residential land, in units; P——The annual number of motor vehicles in urban residential areas, in units of vehicles; b——The number of existing parking spaces in urban residential areas in the target year, in units; d——the utilization coefficient of on-street parking spaces in urban residential areas in the target year, that is, the proportion of parking needs of residents in the area that can be met by on-street parking spaces; u——the area of ​​on-street parking spaces in urban residential areas, in square meters; v——the usage intensity of on-street parking spaces in urban residential areas, i.e. the usage intensity per unit time and per unit area; Substitute the above parameters into formula (16) to plan the number of on-street parking spaces.

5. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 1, characterized in that: The step 3 includes the following steps: Step 3-1: Select factors influencing travel utility and investigate relevant travel data; Step 3-2: Construct the travel utility function U based on the MNL model it and the probability function P it , the target rate is determined by numerical calculation and analysis; Step 3-3: Develop a charging plan based on the progressive charging mechanism and the differentiated charging mechanism.

6. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 5 is characterized in that: In step 3-1, the travel utility influencing factor X its It includes factors affecting traffic and factors affecting residents' travel; the factors affecting traffic include travel expenses and travel time; the factors affecting residents' travel include the presence or absence of private cars, age, type of work, monthly income and annual income; the corresponding travel data are investigated using the RP survey method to study residents' actual travel behavior, and the SP survey method to study residents' choice intentions; the use of parking spaces is evaluated based on the survey data obtained.

7. The method for managing on-street parking in urban residential areas based on SWOT-AHP analysis according to claim 5, characterized in that: Step 3-2 specifically includes the following steps: Based on the MNL model, it is believed that the factors affecting travel utility conform to the price leverage mechanism. The relationship between the utility of traveler t choosing travel mode i and the corresponding expenditure is: U it =βF i +A (17) Where U it is the observable attribute in the travel utility function, β is the indicator sensitivity parameter, β<0, and A is other factors that affect the utility value; Through data analysis, it is found that the utility function U it With random variable X its There is a linear relationship between them, and the following equation is obtained: Where, X its ASC is the factor affecting travel utility. it To determine the variable, β s is the weight parameter of the random item variable, and n is the number of random item variables; After processing the data, the weight parameters of the above-mentioned deterministic variables and random variables were measured and calibrated by BIOGEME, and then the data variables with insignificant effects were detected and removed by t-test, and the significant variables and the corresponding calibration of each significant variable were obtained, and the deterministic variables and weight parameters were obtained; According to the random utility theory, it is assumed that the principle of traveling residents is to choose the travel mode by maximizing random utility. Based on the MNL model, the theory of statistically analyzing and predicting the behavior and decision-making of travelers is called the multinomial logit theory. The probability of each transportation mode is: Where P i represents the possibility of choosing transportation mode i, U i Represents the observable attributes in the travel utility function; According to the expected perceived benefit theory, when the relevant costs increase, the expected perceived benefits will increase accordingly, but the rate of change will gradually decrease; at the same time, the probability of the travel mode decreases as the relevant costs increase. The derivative of the expected value with respect to the relevant costs has the following relationship with the probability of choosing the travel mode i: In the formula, Q it (F i ) is the expected benefit value of the residents traveling, P it F is the possibility of residents choosing travel mode, i Amount of money spent on travel expenses; By integrating random utility theory and price leverage mechanism, and integrating both sides of equation (20), we can obtain the functional expression of expected perceived benefit on travel expenditure: According to the survey data obtained by the RP-SP survey method in step 3, the data of various factors affecting urban traffic roads and residents' travel and the various indicator parameters calibrated by BIOGEME and t-test are substituted into formula (21) to obtain the expected perceived benefit function of on-street parking. Then, the optimal rate is determined by numerical calculation and analysis. That is, by successively determining the slope values ​​of different points on the expected perceived benefit function graph, the corresponding expected perceived benefit values ​​and charging rates are calculated. The charging rate corresponding to the slope threshold is the optimal rate.

8. The method for managing on-street parking in urban residential areas based on SWOT-AHP analysis according to claim 7, characterized in that: In step 3-3, based on the charging rate obtained in step 3-2, the progressive charging mechanism makes floating adjustments to the base rate according to different parking durations; the differentiated charging mechanism makes adjustments based on the base rate according to different time periods and locations of the day to determine the optimal on-street parking charging plan.

9. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 1, characterized in that: In step 4, the differentiated penalty measures are: for residents who continue to use the parking space in the next quarter, the usage rate for the next cycle will be increased; for residents who no longer use the parking space in the next quarter, graded fines will be imposed based on any illegal parking behaviors that occur during use.

10. The urban residential on-street parking management method based on SWOT-AHP analysis method according to claim 3, characterized in that: In step 5, the key factors that affect the implementation effect of the management system are determined, including intelligent management, policy support, non-standard management, road resource development and utilization, and intensified parking conflicts; a hierarchical model is constructed, including the target layer, the criterion layer, and the plan layer; the consistency test index and the coordinates of the center of gravity of the strategic quadrilateral are calculated using formulas (1)-(15), and then the strategy type is determined according to the distribution position of the coordinates of the center of gravity of the strategic quadrilateral. If the analysis result is WO or WT strategy, it indicates that the next cycle will re-select the site according to the actual threats in the order of step 1; The SO and ST strategies indicate that the system does not need to be readjusted in the next cycle.

Images