Site selection method for emergency shuttle bus parking points based on GIS and fuzzy comprehensive evaluation

Abstract

The present invention relates to a method for selecting a site for an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation, comprising the following steps: S1, setting a city's subway stations, alternative parking points, and urban road network in a network data set; S2, collecting first reference data; S3, collecting second reference data; S4, collecting third reference data; S5, using a fuzzy comprehensive evaluation method, using the reference data as a factor set, to evaluate site selection schemes with different numbers of parking points; S6, performing weighted summation on the site selection scheme evaluation sets for each number of parking points in the same time and space, and comparing to obtain the optimal site selection solution in the same time and space; S7, comparing the site selection of parking points based on the current built subway network and the future planned subway network, and obtaining site selection rules and constructive suggestions. The present invention can comprehensively consider factors such as safety, accessibility, and economy, and can compare parking point schemes at different stages of urban development, thus having universality and practicality.

Description

Technical Field

[0001] The present invention relates to a site selection technology for subway emergency bus parking points, and more specifically, to a site selection method for emergency shuttle bus parking points based on GIS and fuzzy comprehensive evaluation. Background Art

[0002] The location of subway emergency shuttle bus parking points, a crucial component of the subway's emergency shuttle system, plays a crucial role. In the event of an incident at a subway station, buses stored in these parking points must travel to the affected subway station within a relatively short period of time to provide emergency shuttle service. Failure to do so will cause significant delays in the daily lives of urban residents and severely reduce the quality of urban subway service. While extensive research exists on site selection, relatively little research has been conducted on the location of subway emergency shuttle bus parking points. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for selecting emergency shuttle bus parking points based on GIS and fuzzy comprehensive evaluation, which can comprehensively consider factors such as safety, accessibility and economy, and can compare parking point plans at different stages of urban development, and is universal and practical.

[0004] The technical solution adopted by the present invention to solve the technical problem is to construct a method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation, which includes the following steps:

[0005] S1. Set a city's subway stations, alternative parking spots, and urban road networks in the network dataset;

[0006] S2. Collect first reference data, where the first reference data includes subway station coverage of p parking points, and establish a maximum coverage-minimum impedance model for solution;

[0007] S3. Collect second reference data, where the second reference data includes the distance between a subway station with high passenger flow and its matching parking point;

[0008] S4. Collect third reference data, including parking point establishment costs such as vehicle purchase costs, site rental costs, and repair and maintenance costs, with the data source being market research;

[0009] S5. Using a fuzzy comprehensive evaluation method, with the first reference data, the second reference data, and the third reference data as a factor set, evaluate the site selection schemes with different numbers of parking spots;

[0010] S6. Perform weighted summation on the evaluation sets of site selection schemes for each number of parking spots in the same time and space, and compare them to obtain the optimal site selection solution in the same time and space;

[0011] S7. Compare the location of parking points based on the current subway network and the future planned subway network, and draw site selection rules and constructive suggestions.

[0012] According to the above scheme, in step S1, specifically: information on subway stations and alternative parking spots includes the names of subway stations and their geographical locations in the current year and the next three years; information on the longitude and latitude of bus parking lots and urban subway stations that can be used as parking spots is collected, including urban road network information such as national highways, provincial highways, and county roads; passenger flow information of urban subway stations in the past five years is collected, and the above information is stored in the network dataset.

[0013] According to the above scheme, step S2 specifically includes: maximizing coverage and minimizing impedance as the overall goal, selecting a site area as a set of finite points, and using distance as the impedance indicator. The distance here is in the form of actual road weighted distance, which is different from Euclidean distance. The model limits the number of parking points to p, and stipulates that each subway station can only establish a connection with one parking point. The model is formally expressed as follows:

[0014]

[0015] Among them, k is the subway station identifier, j is the alternative parking point identifier, K is the subway station set, J is the bus station set (i.e., the alternative parking point set), P is the number of parking points, d kj is the weighted distance between subway station k and parking point j,

[0016]

[0017]

[0018]

[0019] According to the above scheme, the constraints corresponding to the site selection model are as follows:

[0020] The first constraint is formulated as follows:

[0021]

[0022] It ensures that each subway station is connected to at most one parking spot;

[0023] The second constraint is formulated as follows:

[0024]

[0025] It ensures that only the bus stops selected as parking points in the alternative parking point set can establish a connection with the subway station;

[0026] The third constraint is formulated as follows:

[0027] ∑ j∈J y j =p,

[0028] The number of parking spots is guaranteed to be p;

[0029] The fourth constraint is formulated as follows:

[0030] ∑ k∈K z k ≤K,

[0031] This ensures that the total number of subway stations served by a unique bus stop is less than the total number of subway stations.

[0032] The fifth constraint is formulated as follows:

[0033] d kj ≤d max ,

[0034] It ensures that the weighted distance between subway station k and parking point j is less than the maximum distance specified between subway station k and parking point j.

[0035] The sixth constraint is formulated as follows:

[0036]

[0037] Use GIS to solve the model, determine parking point information, and calculate subway station coverage.

[0038] According to the above scheme, the steps for solving the model are:

[0039] S201, geographical location information of urban alternative facilities and demand points, and urban road network information are imported into ArcMap to construct a network dataset for backup;

[0040] S202. Use GIS network analysis to select sites;

[0041] S203: Export the site selection result data, including the correspondence between the parking points and each subway station and the weighted distance, and process the data to determine the parking point information and calculate the subway station coverage rate.

[0042] According to the above scheme, in step S3, specifically: collect the daily passenger flow information of urban subway stations in the past five years, count the top five subway stations every day, and list the subway stations that appear in the top five rankings more than 365 times in these five years as subway stations with large passenger flow, and obtain the information of subway stations with large passenger flow; according to the model solution results in step S2, obtain the distance from the subway stations with large passenger flow to the parking points for the site selection schemes with different numbers of parking points and calculate the average value.

[0043] According to the above solution, in step S4, specifically, in order to determine the construction cost of different numbers of parking spots, according to the functional relationship between the selling price of the product and the quantity of the product:

[0044] F(x)=[C+(A+Bx) -α ]px

[0045] Where F(x) is the total price of the product, which is the sum of the rental costs of several parking spots or the purchase costs of several buses, or the maintenance costs of buses; C is the relative sales cost coefficient relative to a single product; p is the unit price of the product, which is the cost of renting a parking spot or the purchase cost of a bus, or the maintenance costs of a bus; Cp is the sales cost relative to a single product; and x is the number of products.

[0046] Among them, the conditions satisfied by F(x) are:

[0047] (1) Relative unit price is strictly monotonically decreasing with respect to x, and

[0048] (2) The commodity profit function R(x) = F(x) - Cpx is strictly monotonically increasing with respect to x.

[0049] According to the above scheme, the method for determining the coefficients of the functional relationship is as follows: first, determine the range of α through the conditions satisfied by F(x), then investigate the rental costs of one to five different parking spots, bus purchase costs, and bus maintenance costs respectively, and perform linear fitting on the unknown terms in the functional relationship between the selling price of the commodity and the quantity of the commodity through the least squares method in Matlab software to determine the values ​​of the coefficients A, B, C, and α for different commodities, and then infer the price of the required quantity of commodities.

[0050] According to the above solution, step S5 specifically includes the following steps:

[0051] S501, determine the influencing factor set and its weight of the evaluation object in the fuzzy evaluation model,

[0052] The factor set is expressed as: U={u1,u2,…,u n};

[0053] The weight set is expressed as: A={a1,a2,…,a n},

[0054] in,

[0055] The coefficient of variation method is used to determine the weight set of the factor set. The specific steps are: eliminate the influence of different dimensions of each evaluation indicator, use the coefficient of variation of each indicator to measure the degree of difference of each indicator, and then calculate the weight of each indicator; the formula of each variation indicator is:

[0056]

[0057] Where Q i is the coefficient of variation of the i-th indicator, is the standard deviation of the i-th indicator, is the average of the i-th indicator;

[0058] The weight formula of each indicator is:

[0059]

[0060] S502: Determine the evaluation level, that is, determine the degree to which the parking spot belongs to each evaluation level;

[0061] Evaluation level V = {v1, v2, ..., v m}, the evaluation level set V={a,b,c,d} in the application case,

[0062] Among them, level a means that the site selection scheme with p parking points is very suitable for the site selection of emergency docking parking points under the subway failure, level b means that the site selection scheme with p parking points is suitable for the site selection of emergency docking parking points under the subway failure, level c means that the site selection scheme with p parking points is basically suitable for the site selection of emergency docking parking points under the subway failure, and level d means that the site selection scheme with p parking points is not suitable for the site selection of emergency docking parking points under the subway failure.

[0063] S503, determine the membership function of each factor in the factor set with respect to each comment in the comment set and construct a comprehensive evaluation matrix R; perform single factor evaluation to obtain:

[0064] r i ={r i1 ,r i2 ,…,r im},

[0065] Among them, r i It represents the membership degree of each comment in the comment set to the i-th factor.

[0066] Construct a comprehensive evaluation matrix:

[0067]

[0068] Among them, r ij (1≤i≤n,1≤j≤m) means the jth evaluation level v j For example, the evaluation made by the expert group on the i-th factor;

[0069] Among them, the membership function is selected as follows:

[0070]

[0071] The Gaussian membership function is smooth and highly sensitive, and has strong adaptability to good and general evaluation indicators of influencing factors. Its expression is:

[0072]

[0073] For the excellent and poor membership functions of each factor index, semi-trapezoidal distribution is selected.

[0074] The specific expression of the large-scale semi-trapezoidal distribution is:

[0075]

[0076] The specific expression of the small size of the semi-trapezoidal distribution is:

[0077]

[0078] S504, conduct a comprehensive evaluation of different parking point location schemes to obtain the evaluation set

[0079] According to the above solution, step S6 specifically includes the following steps:

[0080] S601, for the weight set A on the factor set U = {a1, a2, ..., a n} Transform R into a fuzzy set on the comment set V: Calculate the evaluation set

[0081] S602: In order to make full use of the information brought by the comprehensive evaluation, the components of the evaluation vector are used to form weights, and the scores of each comment are weighted averaged to obtain the total evaluation score of the evaluation event. The details are as follows:

[0082] B={b1,b2,…,b m},

[0083] make

[0084] Where k is a selected positive real number, and b kj It is b j kth power, j=1,2,…,m; so δ1,…,δ m Constitute a set of weights, and then for each review v j Give a score of c j The comprehensive evaluation is B={b1,b2,…,b m The total score of} is: The site selection schemes with different numbers of parking spots p are compared in terms of their total scores to obtain the optimal scheme.

[0085] The implementation of the emergency shuttle bus parking point site selection method based on GIS and fuzzy comprehensive evaluation of the present invention has the following beneficial effects:

[0086] 1. This invention quantifies uncertain factors in site selection and displays them on intuitive maps, thus reducing the influence of subjective factors on site selection results and providing a scientific basis for site selection decisions.

[0087] 2. The present invention takes population and economic benefits into consideration, and the site selection is closer to reality and more reliable;

[0088] 3. The present invention is compared with the site selection plan under future development, and the site selection result is more long-term. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0090] Figure 1 This is a flow chart of the method for selecting a site for an emergency shuttle bus stop based on GIS and fuzzy comprehensive evaluation;

[0091] Figure 2 Select a site plan for a subway emergency docking station in a certain city in 2022;

[0092] Figure 3 Select a site plan for a subway emergency docking station in a certain city in 2025;

[0093] Figure 4 Select a site plan for two subway emergency docking stations in a city in 2022;

[0094] Figure 5 Select a site plan for two subway emergency docking stations in a city by 2025;

[0095] Figure 6 Select a site plan for three subway emergency docking stations in a city in 2022;

[0096] Figure 7Select a site plan for three subway emergency docking stations in a city by 2025;

[0097] Figure 8 Select a site plan for four subway emergency docking stations in a city in 2022;

[0098] Figure 9 Select a site plan for four subway emergency docking stations in a city by 2025;

[0099] Figure 10 Select a site plan for five subway emergency docking stations in a city in 2022;

[0100] Figure 11 Select a site plan for five subway emergency docking stations in a city by 2025;

[0101] Figure 12 Select a site plan for six subway emergency docking stations in a city in 2022;

[0102] Figure 13 Select a site plan for six subway emergency docking stations in a city by 2025. DETAILED DESCRIPTION

[0103] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0104] like Figure 1-13 As shown, the method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation of the present invention includes the following steps:

[0105] S1. Set up a city’s subway stations, alternative parking spots, and urban road networks in the network dataset.

[0106] The subway station information includes the names and geographical locations of the current year (2022) and the planned stations in 2025. The data source is map information point picking. The alternative parking point information comes from the city’s bus company. There is no clear parking capacity for the construction site. Based on the scale and capacity of the city’s existing stations, it is assumed that the planned station area in the future is 20,000 m 2 The above stations can accommodate more than 100 buses and serve as alternative parking points. The urban road network data comes from Bigemap.

[0107] S2. Collect first reference data, which includes the subway station coverage rate of p parking points, and establish a maximum coverage-minimum impedance model for solution.

[0108] The steps to establish the maximum coverage-minimum impedance model are as follows:

[0109] S201, with maximizing coverage and minimizing impedance as the overall goal, the site selection area is a set of finite points, and the impedance indicator is distance, which is in the form of actual road weighted distance, as opposed to Euclidean distance. This model limits the number of parking points to p, and stipulates that each subway station can only be connected to one parking point. The model is formally expressed as:

[0110]

[0111] Among them, k is the subway station identifier, j is the alternative parking point identifier, K is the subway station set, J is the bus station set (i.e., the alternative parking point set), P is the number of parking points, d kj is the weighted distance between subway station k and parking point j,

[0112]

[0113]

[0114]

[0115] The constraints corresponding to the site selection model are as follows:

[0116] The first constraint is formulated as:

[0117]

[0118] This ensures that each subway station is connected to at most one parking spot.

[0119] The second constraint is formulated as:

[0120]

[0121] It is ensured that only the bus stops selected as parking points in the alternative parking point set can establish a connection with the subway station.

[0122] The third constraint is formulated as:

[0123] ∑ j∈J y j =p,

[0124] It is guaranteed that the number of parking points is p.

[0125] The fourth constraint is formulated as follows:

[0126] ∑ k∈K z k ≤K,

[0127] This ensures that the total number of subway stations served by a unique bus stop is less than the total number of subway stations.

[0128] The fifth constraint is formulated as:

[0129] d kj ≤d max ,

[0130] It ensures that the weighted distance between subway station k and parking point j is less than the maximum distance specified between subway station k and parking point j.

[0131] The sixth constraint is formulated as follows:

[0132]

[0133] The model solution steps are as follows: geographical location information of urban alternative facilities and demand points, and urban road network information are imported into ArcMap to build a network dataset for backup. GIS network analysis is used for site selection. Figure 2-13 Select parking locations for different years. Export the location selection results, including the correspondence between parking locations and subway stations and weighted distances, and process the data to determine parking location information and calculate subway station coverage.

[0134] S3. Collect second reference data, where the second reference data includes the distance between a subway station with large passenger flow and its matching parking point.

[0135] Collect daily passenger flow data for urban subway stations over the past five years and compile a statistical analysis of the top five stations each day. Count any stations that appeared in the top five rankings more than 365 times over the past five years as high-passenger flow stations, thus obtaining this information. Based on the model solution from step 2, calculate the distances from high-passenger flow stations to the parking points for each site selection scenario with different numbers of parking points and calculate the average value.

[0136] S4. Collect third reference data. The third reference data includes parking point establishment costs such as vehicle purchase costs, site rental costs, and maintenance costs. The data source is market research. In order to determine the construction costs of different numbers of parking points, according to the functional relationship between product price and product quantity:

[0137] F(x)=[C+(A+Bx) -α ]px

[0138] Where F(x) is the total price of the product, which is the sum of the rental costs of several parking spots or the purchase costs of several buses, or the maintenance costs of buses; C is the relative sales cost coefficient relative to a single product; p is the unit price of the product, which is the cost of renting a parking spot or the purchase cost of a bus, or the maintenance costs of a bus; Cp is the sales cost relative to a single product; and x is the number of products.

[0139] The conditions that F(x) should satisfy are:

[0140] (1) Relative unit price is strictly monotonically decreasing with respect to x, and

[0141] (2) The commodity profit function R(x) = F(x) - Cpx is strictly monotonically increasing with respect to x.

[0142] The method for determining the coefficients of the functional relationship is as follows: first, determine the range of α through the conditions satisfied by F(x), then investigate the rental costs of one to five different parking spots, bus purchase costs, and bus maintenance costs respectively, and perform linear fitting on the unknown terms in the functional relationship between the sales price and the quantity of the above-mentioned goods through the least squares method in Matlab software to determine the values ​​of the coefficients A, B, C, and α for different goods, and then infer the price of the required quantity of goods.

[0143] S5. Using the fuzzy comprehensive evaluation method, the above reference data is used as a factor set to evaluate the site selection schemes with different numbers of parking spots, including the following steps:

[0144] S501. Determine the set of influencing factors and their weights of the evaluation object in the fuzzy evaluation model.

[0145] The factor set is expressed as:

[0146] U={u1,u2,…,u n};

[0147] The weight set is expressed as:

[0148] A={a1,a2,…,a n},

[0149] in,

[0150] The coefficient of variation method is used to determine the weight set of the factor set. The specific steps are to first eliminate the influence of different dimensions of each evaluation indicator, use the coefficient of variation of each indicator to measure the degree of difference of each indicator, and then calculate the weight of each indicator.

[0151] The formulas for each variation index are:

[0152]

[0153] Where Q i is the coefficient of variation of the i-th indicator, is the standard deviation of the i-th indicator; is the average of the i-th indicator.

[0154] The weight formula of each indicator is:

[0155]

[0156] S502, determine the evaluation level, that is, determine the degree to which the parking point belongs to each evaluation level. Evaluation level V = {v1, v2, ..., v m}, the evaluation level set V = {a, b, c, d} in the application case of the present invention, wherein: level a indicates that the site selection scheme with p parking points is very suitable for the site selection of emergency docking parking points under the subway failure; level b indicates that the site selection scheme with p parking points is suitable for the site selection of emergency docking parking points under the subway failure; level c indicates that the site selection scheme with p parking points is basically suitable for the site selection of emergency docking parking points under the subway failure; level d indicates that the site selection scheme with p parking points is not suitable for the site selection of emergency docking parking points under the subway failure.

[0157] S503: Determine the membership function of each factor in the factor set with respect to each comment in the comment set and construct a comprehensive evaluation matrix R.

[0158] The single factor evaluation results are:

[0159] r i ={r i1 ,r i2 ,…,r im},

[0160] r i It represents the membership degree of each comment in the comment set to the i-th factor.

[0161] Construct a comprehensive evaluation matrix:

[0162] Among them, r ij (1≤i≤n,1≤j≤m) means the jth evaluation level v j For example, the expert group’s evaluation of the i-th factor.

[0163] Among them, the membership function is selected as follows:

[0164]

[0165] The Gaussian membership function is smooth and highly sensitive, and has strong adaptability to the influencing factors of the present invention and general evaluation indicators. Its expression is:

[0166]

[0167] The present invention selects a semi-trapezoidal distribution for the membership function of the excellent and poor indicators of each factor.

[0168] The specific expression of the large-scale semi-trapezoidal distribution is:

[0169]

[0170] The specific expression of the small size of the semi-trapezoidal distribution is:

[0171]

[0172] Finally, a comprehensive evaluation of different parking point location schemes is conducted to obtain the evaluation set

[0173] S6. Perform weighted summation on the evaluation sets of the location selection schemes for each number of parking spots in the same time and space, and compare them to obtain the optimal location selection solution for the same time and space. The specific steps are as follows:

[0174] S601, for the weight set A on the factor set U = (a1, a2, ..., a n} Transform R into a fuzzy set on the comment set V: Calculate the evaluation set

[0175] S602. In order to make full use of the information brought by the comprehensive evaluation, this paper uses the components of the evaluation vector to form weights and performs weighted average on the scores of each comment to obtain the total evaluation score of the evaluation event. The details are as follows:

[0176] B={b1,b2,…,b m},

[0177] make

[0178] Where k is a selected positive real number. And b k j It is b j kth power, j=1,2,…,m. So δ1,…,δ m Constitute a set of weights, and then for each review v j Give a score of c j The comprehensive evaluation is B={b1,b2,…,b m The total score of} is:

[0179]

[0180] The site selection schemes with different numbers of parking spots p are compared in terms of their total scores to obtain the optimal scheme.

[0181] S7. Compare the location of parking points based on the current subway network and the future planned subway network (2025), and draw site selection rules and constructive suggestions.

[0182] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A method for selecting emergency shuttle bus parking points based on GIS and fuzzy comprehensive evaluation, characterized in that: The following steps are involved: S1. Set a city's subway stations, alternative parking spots, and urban road networks in the network dataset; S2. Collect first reference data, where the first reference data includes subway station coverage of p parking points, and establish a maximum coverage-minimum impedance model for solution; Step S2 specifically includes: maximizing coverage and minimizing impedance as the overall goal, selecting a site area as a set of finite points, and using distance as the impedance indicator. The distance here is in the form of actual road weighted distance, which is different from Euclidean distance. The model limits the number of parking spots to p, stipulating that each subway station can only be connected to one parking spot. The model is formally expressed as follows: ， ， Among them, k is the subway station identifier, j is the alternative parking point identifier, K is the subway station set, J is the bus station set (i.e., the alternative parking point set), P is the number of parking points, is the weighted distance between subway station k and parking point j, ， ， ； The model solving steps are: S201, geographical location information of urban alternative facilities and demand points, and urban road network information are imported into ArcMap to construct a network dataset for backup; S202. Use GIS network analysis to select sites; S203: Exporting the site selection result data, including the correspondence between parking spots and each subway station and the weighted distance, and processing the data to determine the parking spot information and calculate the subway station coverage rate; S3. Collect second reference data, where the second reference data includes the distance between a subway station with high passenger flow and its matching parking point; S4. Collect third reference data, including parking spot vehicle purchase costs, site rental costs, maintenance costs, and parking spot establishment costs, the data source being market research; S5. Using a fuzzy comprehensive evaluation method, with the first reference data, the second reference data, and the third reference data as a factor set, evaluate the site selection schemes with different numbers of parking spots; S6. Perform weighted summation on the evaluation sets of site selection schemes for each number of parking spots in the same time and space, and compare them to obtain the optimal site selection solution in the same time and space; S7. Compare the location of parking points based on the current subway network and the future planned subway network, and draw site selection rules and constructive suggestions.

2. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: In step S1, the information of subway stations and alternative parking spots includes the names and geographical locations of subway stations planned for the current year and the next three years; the collected longitude and latitude of bus parking lots and urban subway stations that can be used as parking spots; and the urban road network information including national highways, provincial highways, and county roads. Collect passenger flow information of urban subway stations in the past five years and store the above information in a network dataset.

3. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: The constraints corresponding to the model are as follows: The first constraint is formulated as follows: ， It ensures that each subway station is connected to at most one parking spot; The second constraint is formulated as follows: ， It ensures that only the bus stops selected as parking points in the alternative parking point set can establish a connection with the subway station; The third constraint is formulated as follows: ， The number of parking spots is guaranteed to be p; The fourth constraint is formulated as follows: ， Ensure that the total number of subway stations served by a unique bus stop is less than the total number of subway stations; The fifth constraint is formulated as follows: ， It ensures that the weighted distance between subway station k and parking point j is less than the maximum distance between subway station k and parking point j; The sixth constraint is formulated as follows: Use GIS to solve the model, determine parking point information, and calculate subway station coverage.

4. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: In the step S3, specifically, the daily passenger flow information of urban subway stations in the past five years is collected, and the top five subway stations ranked each day are counted. In these five years, subway stations that appear in the top five rankings more than 365 times are classified as subway stations with large passenger flow, and the information of subway stations with large passenger flow is obtained; based on the model solution results in step S2, the distances from the subway stations with large passenger flow to the parking points of the site selection schemes with different numbers of parking points are obtained and the average value is calculated.

5. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: In step S4, specifically, in order to determine the construction costs of different numbers of parking spots, according to the functional relationship between the selling price of the product and the quantity of the product: in, The total price of the product is the sum of the rental fees of several parking spots or the purchase fees of several buses, or the maintenance fees of buses. is the relative sales cost coefficient for a single commodity, The unit price of the product, i.e. the cost of renting a parking spot or the cost of purchasing a bus, or the cost of repairing and maintaining a bus, is the sales cost relative to a single product, is the quantity of goods; in, The conditions to be met are: (1) Relative unit price about is strictly monotonically decreasing, and , (2) Commodity profit function about Strictly monotonically increasing.

6. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 5 is characterized in that: The method for determining the coefficients of the functional relationship is as follows: First, Satisfied conditions confirmed Then, we investigate the rental costs of one to five different parking spots, the purchase costs of buses, and the maintenance costs of buses. We use the least squares method in Matlab software to perform linear fitting on the unknown terms in the functional relationship between the price of the commodity and the quantity of the commodity, and determine the coefficients for different commodities. The value of , and then infer the price of the required quantity of goods.

7. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: The step S5 specifically includes the following steps: S501, determine the influencing factor set and its weight of the evaluation object in the fuzzy evaluation model, The factor set is expressed as: ; The weight set is expressed as: , in, ; The coefficient of variation method is used to determine the weight set of the factor set. The specific steps are: eliminate the influence of different dimensions of each evaluation indicator, use the coefficient of variation of each indicator to measure the degree of difference of each indicator, and then calculate the weight of each indicator; the formula of each variation indicator is: ； Where, It is The coefficient of variation of the indicator, For the The standard deviation of the indicator, It is The average of the indicators; The weight formula of each indicator is: ； S502: Determine the evaluation level, that is, determine the degree to which the parking spot belongs to each evaluation level; Assessment level , the evaluation level set in the application case , Among them, level a means that the site selection scheme with p parking points is very suitable for the site selection of emergency docking parking points under subway failure, level b means that the site selection scheme with p parking points is suitable for the site selection of emergency docking parking points under the subway failure, level c means that the site selection scheme with p parking points is basically suitable for the site selection of emergency docking parking points under the subway failure, and level d means that the site selection scheme with p parking points is not suitable for the site selection of emergency docking parking points under the subway failure. S503, determine the membership function of each factor in the factor set with respect to each comment in the comment set and construct a comprehensive evaluation matrix R; perform single factor evaluation to obtain: ， in, represents the membership of the i-th factor with respect to each comment in the comment set; Construct a comprehensive evaluation matrix: ， Among them, r ij (1≤i≤n,1≤j≤m) means the jth evaluation level v j For example, the evaluation made by the expert group on the i-th factor; Among them, the membership function is selected as follows: The Gaussian membership function is smooth and highly sensitive, and has strong adaptability to good and general evaluation indicators of influencing factors. Its expression is: ， For the excellent and poor membership functions of each factor index, semi-trapezoidal distribution is selected. The specific expression of the large-scale semi-trapezoidal distribution is: ， The specific expression of the small size of the semi-trapezoidal distribution is: ; S504, conduct a comprehensive evaluation of different parking point location schemes to obtain the evaluation set .

8. The method for selecting an emergency shuttle bus parking point based on GIS and fuzzy comprehensive evaluation according to claim 1 is characterized in that: The step S6 specifically includes the following steps: S601, for the weight set on the factor set U Transform R into a fuzzy set on the review set V: Calculate the evaluation set ; S602: In order to make full use of the information brought by the comprehensive evaluation, the components of the evaluation vector are used to form weights, and the scores of each comment are weighted averaged to obtain the total evaluation score of the evaluation event. Specifically, ， make , in, is a chosen positive real number, and yes of Power, ;then Construct a set of weights and then assign each review Give a score Such a comprehensive evaluation The total score is: , the comprehensive evaluation scores of the site selection plans with different numbers of parking points p are compared to obtain the optimal plan.

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