A method for urban public parking lot planning and site selection based on OD and POI models

Abstract

The present invention proposes a method for planning and site selection of urban public parking lots by integrating OD and POI models, including dividing a target area into multiple OD traffic areas, establishing an OD parking space demand prediction model according to the traffic area motor vehicle travel attraction data, obtaining the public parking space demand of each traffic area and assigning it to the OD traffic area surface vector attribute table, obtaining multiple types of parking influencing factor POI data affecting parking results, establishing a site selection analysis POI model, obtaining a parking site selection grid map of the target area, obtaining a parking site selection grid map of each traffic area, obtaining a site selection grid map of an urban public parking lot integrating parking space demand, obtaining a grid map of the maximum value area of ​​the required parking space in the target area, taking the area as a research area, obtaining the number of parking spaces to be built, obtaining the location of regional vacant spaces, and conducting simulation evaluation in combination with the number of parking spaces. The fusion model fully considers various factors affecting parking demand, realizes site selection from macro to micro, and can meet the systematic needs of special planning.

Description

Technical Field

[0001] The present invention relates to the technical field of GIS site selection, and in particular to a method for planning and site selection of urban public parking lots that integrates OD and POI models. Background Art

[0002] With the development of urban economy, the number of motor vehicles continues to increase. The contradiction between the continuous growth of motor vehicle ownership and the limited number of parking spaces has become a major problem in the development of urban transportation. The problem of difficulty in parking has become one of the main causes of urban traffic congestion, restricting the sustainable development of cities.

[0003] Parking lots are buffer zones between static and dynamic traffic, and are an important part of improving traffic efficiency and traffic conditions. Parking lot site selection without a systematic overall plan is difficult to meet parking needs. Not only can it not solve the parking problem, but inappropriate site selection may also have a significant impact on dynamic traffic. Existing site selection analysis models do not consider influencing factors comprehensively enough. They often use local shortest path and minimum cost to analyze parking site selection points, which cannot make up for the demand gap well and it is difficult to deal with the site selection problem of urban public parking lots from a planning and system level.

[0004] In summary, the current urban parking lot planning and site selection methods are difficult to meet planning needs, and it is also difficult to systematically guide parking lot construction to fill the demand gap. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for planning and site selection of urban public parking lots that integrates OD and POI models. The integration of OD and POI models can not only reflect the differences between different areas in the target area, but also break the regional boundaries to refine the site selection analysis, fully consider various factors affecting parking demand, and realize site selection from macro to micro to meet the systematic needs of special planning.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for planning and selecting a site for an urban public parking lot by integrating OD and POI models comprises the following steps:

[0008] Step 1: Divide the target area into multiple OD traffic zones, establish an OD parking space demand prediction model based on the traffic zone motor vehicle travel attraction data, obtain the public parking space demand of each traffic zone, and assign the public parking space demand of each traffic zone as an attribute value to the OD traffic zone surface vector attribute table;

[0009] Step 2: Acquire multiple types of parking influencing factor POI data that affect parking results, perform buffer analysis on each type of parking influencing factor POI data separately, and rasterize it into multiple grids;

[0010] Step 3: Determine the weights of multiple types of parking influencing factor POI data, and perform weighted integration of each grid to establish a site selection analysis POI model to obtain a parking site selection grid map of the target area;

[0011] Step 4: Divide the parking site selection grid map of the target area according to the traffic zone surface vector to obtain the parking site selection grid map of each traffic zone;

[0012] Step 5: Combine the public parking space demand of each traffic zone and the parking location grid map of each traffic zone to obtain the urban public parking lot location grid map integrating the OD and POI models;

[0013] Step 6: Obtain a grid map of the maximum berth demand area in the target area;

[0014] Step 7: Take the maximum value area as the study area, obtain the difference between the maximum number of parking spaces required in the study area and the actual number of parking spaces, and use this as the number of parking spaces to be built.

[0015] Furthermore, an OD berth demand prediction model is established, assuming that the motor vehicle travel attraction of traffic zone i is D i , then the public parking space demand P of the traffic community i The calculation formula is:

[0016]

[0017] Among them, D i is the number of vehicles attracted by traffic zone i, in times / day; α is the stop-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour parking volume to average hourly parking volume; R is the parking space turnover rate; t is the proportion of parking spaces in urban public parking lots.

[0018] Furthermore, all parking influencing factor POI data are classified and integrated into n categories and represented as x = {x1, x2...x n}, for various parking influencing factors POI data x m Perform multi-ring buffer analysis and set recommended site selection values ​​for different ring buffer distances to obtain buffer surface vector data, where 1≤m≤n.

[0019] Furthermore, the recommended site selection value is used as a raster value to realize the conversion of the buffer surface vector data into raster data.

[0020] Furthermore, the POI data of parking influencing factors include population distribution, cultural and sports facilities, large commercial districts, medical facilities and main roads.

[0021] Furthermore, the weights of the POI data of n types of parking influencing factors are determined by using the hierarchical analysis method, including:

[0022] Construct a judgment matrix and use pairwise comparison to establish a comparison judgment matrix A = (a pq ) n×n ,1≤p≤n,1≤q≤n,p≠q,x p Relative to x q The impact of a pq , x p Relative to x q The more important the impact of pq The larger the value of q Relative to x p The impact of a qp =1 / a pq , x q Relative to x p The more important the impact of qp The larger the value of ;

[0023] Weight calculation, normalize the judgment matrix A by column, add the normalized matrix rows to get the normalized column matrix to get the weight matrix W, the maximum eigenvalue λ max The calculation formula is:

[0024]

[0025] Consistency test: When the consistency ratio CR < 0.1, the result meets the consistency test, and the judgment matrix is ​​considered valid. CR can be calculated as:

[0026]

[0027]

[0028] Among them, CI is the consistency index and RI is the average random consistency index.

[0029] Furthermore, obtaining the urban public parking lot location grid map integrating the OD and POI models specifically includes:

[0030] For i traffic area grid matrix array i The sum of all elements is sum i , the calculation formula is:

[0031]

[0032] Among them, XSize is the number of pixels in the X direction of the raster image, YSize is the number of pixels in the Y direction of the raster image, array i is the grid matrix of the ith traffic zone;

[0033] The number of parking spaces required for traffic area i is P i , the public parking space demand of each traffic zone is distributed according to the grid value ratio to generate a new grid matrix for each traffic zone. The calculation formula is:

[0034]

[0035] Among them, new_array i ——The grid matrix of the i traffic zone after reassignment;

[0036] The grid matrix of the fusion OD and POI model is obtained by summing up the new grid matrices of all traffic areas. The calculation formula is:

[0037]

[0038] Among them, new_array is the result matrix finally written into the raster.

[0039] Further, traverse the new_array matrix to find the maximum value and return the maximum value demand max The new_array matrix has m row values ​​above and below the maximum center point and m column values ​​on the left and right sides, and the rest are set to zero. The maximum grid matrix of the required berth is written to obtain the maximum area grid map of the required berth.

[0040] Furthermore, the vacant lots are investigated, and based on the difference between the maximum number of parking spaces required in the study area and the actual number of parking spaces, the difference is used as the number of parking spaces to be built at the vacant lots.

[0041] Furthermore, the method further includes step 8, wherein the parking traffic flow in the study area is calculated by the maximum value, and the traffic flow is taken as data input together with the number of vacant lots and the number of parking lots to be built to realize traffic simulation, and the average travel delay of the road section, the average queue length at the intersection and the parking lot utilization rate are output to realize the evaluation of the parking lot to be built, wherein the calculation formula of the parking traffic flow in the study area is:

[0042]

[0043] Among them, traffic is the parking traffic flow in the study area, the unit is veh / h, demand max is the demand for public parking spaces in the study area; α is the park-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour to average hourly parking volume; R is the parking space turnover rate, and t is the proportion of parking spaces in urban public parking lots.

[0044] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0045] 1. The present invention provides a method for planning and site selection of urban public parking lots by integrating OD and POI models. Based on the traditional OD demand parking space prediction model, the spatial distribution characteristics of parking lot construction of POI model are integrated to break the community boundaries and realize the spatial distribution of demand parking spaces.

[0046] 2. The present invention provides a method for planning and site selection of urban public parking lots that integrates OD and POI models. While analyzing the impact of infrastructure, namely points of interest, on parking lot construction, an OD parking space demand prediction model is established based on the motor vehicle travel attraction data of the traffic community, the public parking space demand of each traffic community is obtained, and the quantitative analysis of the POI model is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of urban parking lot planning and site selection that integrates OD and POI models.

[0048] Figure 2 It is the technical roadmap of the site selection analysis POI model.

[0049] Figure 3 It is a technical roadmap integrating OD and POI models.

[0050] Figure 4 It is a schematic diagram of the parking site selection grid for the transportation community.

[0051] Figure 5 It is a schematic diagram of the parking site selection grid map of the traffic community after extraction.

[0052] Figure 6 It is a grid map of urban public parking lot location selection that integrates OD and POI models.

[0053] Figure 7 This is a schematic diagram of the area with the maximum berth demand.

[0054] Figure 8 This is a schematic diagram of SUMO simulation. DETAILED DESCRIPTION

[0055] In order to facilitate ordinary technicians in the field to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0056] To facilitate the description of the present invention, concepts related to the present invention are introduced below.

[0057] Traffic cells are several geographical units into which the study area is divided in combination with the needs of traffic analysis and traffic demand forecasting models. They are the smallest spatial units for analyzing the travel and distribution of residents and vehicles.

[0058] The present invention provides a method for planning and selecting a site for an urban public parking lot by integrating OD and POI models. Figure 1 As shown, the following steps are included:

[0059] Step 1: Divide the target area into multiple OD traffic zones, establish an OD parking space demand prediction model based on the traffic zone motor vehicle travel attraction data, obtain the public parking space demand of each traffic zone, and assign the public parking space demand of each traffic zone as an attribute value to the OD traffic zone surface vector attribute table;

[0060] Step 2: Acquire multiple types of parking influencing factor POI data that affect parking results, perform buffer analysis on each type of parking influencing factor POI data separately, and rasterize it into multiple grids;

[0061] Step 3: Determine the weights of multiple types of parking influencing factor POI data, and perform weighted integration of each grid to establish a site selection analysis POI model to obtain a parking site selection grid map for the target area;

[0062] Step 4: Divide the parking site selection grid map of the target area according to the traffic zone surface vector to obtain the parking site selection grid map of each traffic zone;

[0063] Step 5: Combine the public parking space demand of each traffic zone and the parking location grid map of each traffic zone to obtain the urban public parking lot location grid map integrating the OD and POI models;

[0064] Step 6: Obtain a grid map of the maximum berth demand area in the target area;

[0065] Step 7: Take the maximum value area as the study area, obtain the difference between the maximum number of parking spaces required in the study area and the actual number of parking spaces, and use this as the number of parking spaces to be built.

[0066] The existing parking demand prediction model mainly focuses on the impact of traffic flow and actual parking demand on parking lot location selection, lacks consideration of infrastructure, and is difficult to achieve spatial distribution of demand prediction. Based on the traditional OD parking demand prediction model, the present invention integrates the spatial distribution characteristics of parking lot construction in the POI model, breaks the community boundaries, and achieves the spatial distribution of parking demand.

[0067] The existing site selection model mainly focuses on analyzing the impact of infrastructure on parking lot construction, and lacks consideration of traffic and actual parking demand. While analyzing the impact of infrastructure, i.e., points of interest, on parking lot construction, the present invention establishes an OD parking demand prediction model based on the motor vehicle travel attraction data of the traffic area, obtains the public parking demand of each traffic area, and realizes the quantitative analysis of the POI model.

[0068] In step 1, an OD parking space demand prediction model is established. The OD parking space demand prediction model starts from the traffic zone attraction volume, removes some vehicles that stop and go, uses the peak hour correction coefficient to correct the peak parking demand times, and finally realizes the conversion of parking times and parking spaces through the parking space turnover rate to realize the parking space demand prediction.

[0069] Specifically, the OD berth demand prediction model is established, assuming that the motor vehicle travel attraction of traffic zone i is D i , then the public parking space demand P of the traffic community i The calculation formula is:

[0070]

[0071] Among them, D i is the number of vehicles attracted by traffic zone i, in times / day; α is the stop-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour parking volume to average hourly parking volume; R is the parking space turnover rate; t is the proportion of parking spaces in urban public parking lots.

[0072] Site selection analysis POI model: Based on the different impacts of different types of points of interest on parking lot construction, the recommendation analysis of parking lot construction is achieved with the help of GIS spatial analysis methods and hierarchical analysis method weight analysis.

[0073] In step 2, all parking influencing factor POI data are classified and integrated into n categories, represented as x = {x1, x2...x n}, perform buffer analysis and rasterization on each type of parking influencing factor POI data;

[0074] Buffer analysis and rasterization specifically include:

[0075] Buffer zone analysis: Parking influencing factor POI data has a certain impact range on parking lot construction and the impact degree varies with distance. Therefore, various types of parking influencing factor POI data x m (1≤m≤n) Set the buffer distance L and perform multi-ring buffer analysis. Set recommended site selection values ​​for the buffer according to different buffer distances L to obtain buffer surface vector data.

[0076] Rasterization: Since raster data directly reflects attribute values ​​and can be overlaid, the recommended site selection value is used as the raster value to realize the conversion of buffer surface vector data into raster data.

[0077] Specifically, the POI data of parking influencing factors include population distribution, cultural and sports facilities, large commercial districts, medical facilities and main roads.

[0078] In one embodiment of the present invention, a certain type of parking influencing factor POI data x m For example, buffer zone analysis, parking impact factor POI data x m There is a certain range of influence on the construction of parking lots, and the degree of influence varies with the distance. Therefore, m The buffer distance L is set and a multi-ring buffer analysis is performed. As shown in Table 1, the recommended location values ​​for the buffer are set according to the different buffer distances L to obtain the buffer surface vector data.

[0079] Table 1 Buffer zone classification establishment reference

[0080]

[0081]

[0082] In step 3, according to the different degrees of influence of the number of POIs of various parking influencing factors on parking lot construction, the weights of n types of parking influencing factor POI data are determined, and based on this, each grid is weighted and integrated to obtain a parking location grid map.

[0083] In step 3, the parking lot construction recommendation is visualized by using colors to reflect different grid values, such as Figure 4 FIG. 1 is a parking location grid diagram in one embodiment of the invention.

[0084] Preferably, in step 3, Figure 2 As shown, the weights of n types of parking influencing factor POI data are determined by using the hierarchical analysis method, and the weights determined by the hierarchical analysis method specifically include:

[0085] Construct a judgment matrix to compare these n types of factors x={x1,x2...x n The impact of parking demand on the parking demand is established by pairwise comparison. pq ) n×n , x p Relative to x q The impact of a pq , x p Relative to x q The more important the impact of pq The larger the value of q Relative to x p The impact of a qp =1 / a pq , x q Relative to x p The more important the impact of qp The larger the value of .

[0086] where a ij The values ​​of are shown in Table 2.

[0087] Table 2 Judgment matrix scoring basis table

[0088]

[0089] To calculate the weight, we first normalize the judgment matrix A by column, and then normalize the column matrix obtained by adding the rows of the normalized matrix to obtain the weight matrix W. The maximum eigenvalue λ max The calculation formula is:

[0090]

[0091] Consistency test: When the consistency ratio CR < 0.1, the result meets the consistency test, and the judgment matrix is ​​considered valid. CR can be calculated as:

[0092]

[0093]

[0094] Among them, CI is the consistency index, RI is the average random consistency index, and the value reference is shown in Table 2;

[0095] Table 2 Reference table for average random consistency index values

[0096]

[0097] In step 4, mask extraction is performed to divide the parking site selection grid map according to the traffic zone surface vector, and after the division, the data is expanded to the entire target area using zero values ​​to obtain the parking site selection grid maps of each traffic zone;

[0098] like Figure 5 As shown in the figure, in an embodiment of the invention, after a division, the data is expanded to the entire target area using zero values ​​to obtain a parking location grid map for each traffic zone.

[0099] In step 5, data fusion is performed to extract the grid matrix of parking location grid map of each traffic zone, and the parking space demand is allocated according to the grid value ratio to generate a new grid matrix. Finally, the new grid matrices of each traffic zone are added and merged into a new matrix; the new matrix is ​​written into the new grid map, and the following is obtained: Figure 6 The grid map of urban public parking lot location selection that integrates OD and POI models is shown.

[0100] In step 5, a new grid matrix is ​​generated according to the grid value ratio, such as Figure 3 As shown, specifically:

[0101] Step 5.1, for the i traffic cell grid matrix array i The sum of all elements is sum i, the calculation formula is:

[0102]

[0103] Among them, XSize is the number of pixels in the X direction of the raster image, YSize is the number of pixels in the Y direction of the raster image, array i is the grid matrix of the ith traffic zone;

[0104] Step 5.2, the number of parking spaces required for traffic zone i is P i , the public parking space demand of each traffic zone is distributed according to the grid value ratio to generate a new grid matrix for each traffic zone. The calculation formula is:

[0105]

[0106] Among them, new_array i ——The grid matrix of the i traffic zone after reassignment;

[0107] Step 5.3, sum up the new grid matrices of all traffic areas to obtain the grid matrix of the fusion OD and POI model. The calculation formula is:

[0108]

[0109] Among them, new_array is the result matrix finally written into the raster.

[0110] Steps 6 and 7 are integrated for analysis. In step 6, the new_array matrix is ​​traversed to find the maximum value and the maximum value demand is returned. max The new_array matrix has m row values ​​above and below the maximum center point and m column values ​​on the left and right sides, and the rest are set to zero. The maximum grid matrix of the required berth is written to obtain the maximum area grid map of the required berth.

[0111] In the embodiment of the present invention, the matrix has 25 row values ​​above and below the center point of the maximum value and 25 column values ​​on the left and right sides respectively, and the rest are set to zero, and the maximum value grid matrix of the required berth is written, such as Figure 7 As shown, a grid map of the maximum area of ​​berth demand is obtained.

[0112] As described in step 6, traverse the new_array matrix to find the maximum value, specifically:

[0113] Take 500m*500m (equivalent to 51*51 grid) as the maximum berth demand calculation range. Start from the grid matrix element (25, 25), sum the 51*51 rectangular range with 25 rows above and below the element and 25 columns on the left and right, traverse the entire matrix, and output the maximum demand max and the maximum center point.

[0114] In step 7, the maximum value area is used as the research area to investigate the location of the empty site (x 空 , x 空 ) and the actual number of parking spaces. The number of parking spaces to be built in the study area is the maximum demand. max The difference between the actual number of parking spaces will be used as a reference for setting the number of parking spaces in the planned parking lot.

[0115] Step 8 is to conduct simulation evaluation. In step 8, the maximum demand max 、Empty point position(x 空 , x 空 ) and the number of parking spaces in the planned parking lot in the study area, calculate the parking traffic flow in the study area.

[0116] In step 8, the parking traffic flow in the study area is calculated, specifically:

[0117] The calculation formula for parking traffic flow in the study area is:

[0118]

[0119] Among them, traffic is the parking traffic flow in the study area, the unit is veh / h, demand max is the demand for public parking spaces in the study area; α is the park-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour to average hourly parking volume; R is the parking space turnover rate, and t is the proportion of parking spaces in urban public parking lots.

[0120] like Figure 8 As shown in Figure 3, traffic simulation is realized with the help of SUMO simulation, and evaluation indicators such as the average travel delay time of the road section, the average queue length at the intersection and the parking lot utilization rate are output to realize the evaluation of the planned parking lot. As shown in Table 3, this is the simulation evaluation before and after the construction of the planned parking lot. It is found that the average travel time of the road section where the parking lot is planned to be built is greatly reduced after construction, the queue length at the intersection is greatly reduced after construction, and the parking lot occupancy rate is also very high.

[0121] Table 3 Simulation evaluation of the proposed parking lot before and after construction

[0122]

[0123]

[0124] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for planning and selecting urban public parking lots by integrating OD and POI models, characterized in that: The following steps are involved: Step 1: Divide the target area into multiple OD traffic zones, establish an OD parking space demand prediction model based on the traffic zone motor vehicle travel attraction data, obtain the public parking space demand of each traffic zone, and assign the public parking space demand of each traffic zone as an attribute value to the OD traffic zone surface vector attribute table; Step 2: Acquire multiple types of parking influencing factor POI data that affect parking results, perform buffer analysis on each type of parking influencing factor POI data separately, and rasterize it into multiple grids; Step 3: Determine the weights of multiple types of parking influencing factor POI data, and perform weighted integration of each grid to establish a site selection analysis POI model to obtain a parking site selection grid map of the target area; Step 4: Divide the parking site selection grid map of the target area according to the traffic zone surface vector to obtain the parking site selection grid map of each traffic zone; Step 5: Combine the public parking space demand of each traffic zone and the parking location grid map of each traffic zone to obtain the urban public parking lot location grid map integrating the OD and POI models; Step 6: Obtain a grid map of the maximum berth demand area in the target area; Step 7: Take the maximum value area as the study area, obtain the difference between the maximum number of parking spaces required in the study area and the actual number of parking spaces, and use this as the number of parking spaces to be built.

2. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 1 is characterized by: Establish an OD parking demand prediction model, assuming that the motor vehicle travel attraction of traffic zone i is D i , then the public parking space demand P of the traffic community i The calculation formula is: Among them, D i is the number of vehicles attracted by traffic zone i, in times / day; α is the stop-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour parking volume to average hourly parking volume; R is the parking space turnover rate; t is the proportion of parking spaces in urban public parking lots.

3. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 1 is characterized by: All parking influencing factors POI data are classified and integrated into n categories and represented as x = {x1, x2...x n }, for various parking influencing factors POI data x m Perform multi-ring buffer analysis and set recommended site selection values ​​for different ring buffer distances to obtain buffer surface vector data, where 1≤m≤n.

4. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 3 is characterized by: The recommended site selection value is used as the raster value to realize the conversion of buffer area vector data into raster data.

5. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 3 is characterized by: The POI data of parking influencing factors include population distribution, cultural and sports facilities, large commercial districts, medical facilities and main roads.

6. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 3 is characterized by: The weights of POI data of n types of parking influencing factors are determined by using the hierarchical analysis method, including: Construct a judgment matrix and use pairwise comparison to establish a comparison judgment matrix A = (a pq ) n×n ,1≤p≤n,1≤q≤n,p≠q,x p Relative to x q The impact of a pq , x p Relative to x q The more important the impact of pq The larger the value of q Relative to x p The impact of a qp =1 / a pq , x q Relative to x p The more important the impact of qp The larger the value of ; Weight calculation, normalize the judgment matrix A by column, add the normalized matrix rows to get the normalized column matrix to get the weight matrix W, the maximum eigenvalue λ max The calculation formula is: Consistency test: When the consistency ratio CR is less than 0.1, the result meets the consistency test, and the judgment matrix is ​​considered valid. CR can be calculated as: Among them, CI is the consistency index and RI is the average random consistency index.

7. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 1 is characterized in that: The specific steps of obtaining the urban public parking lot location grid map integrating OD and POI models are as follows: For i traffic area grid matrix array i The sum of all elements is sum i , the calculation formula is: Among them, XSize is the number of pixels in the X direction of the raster image, YSize is the number of pixels in the Y direction of the raster image, array i is the grid matrix of the i-th traffic zone; The number of parking spaces required for traffic area i is P i , the public parking space demand of each traffic zone is distributed according to the grid value ratio to generate a new grid matrix for each traffic zone. The calculation formula is: Among them, new_array i ——The grid matrix of the i traffic zone after reassignment; The grid matrix of the fusion OD and POI model is obtained by summing up the new grid matrices of all traffic areas. The calculation formula is: Among them, new_array is the result matrix finally written into the raster.

8. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 7 is characterized by: Traverse the new_array matrix to find the maximum value and return the maximum value demand max The new_array matrix has m row values ​​above and below the maximum center point and m column values ​​on the left and right sides, and the rest are set to zero. The maximum grid matrix of the required berth is written to obtain the maximum area grid map of the required berth.

9. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 8 is characterized by: Investigate the vacant locations and use the difference between the maximum number of parking spaces required in the study area and the actual number of parking spaces as the number of parking spaces to be built at the vacant locations.

10. The urban public parking lot planning and site selection method integrating OD and POI models according to claim 9 is characterized in that: The method also includes step 8, which calculates the parking traffic flow in the study area by the maximum value, takes it and the number of vacant lots and the number of parking lots to be built as data input to realize traffic simulation, outputs the average travel delay practice of the road section, the average queue length at the intersection and the parking lot utilization rate, and realizes the evaluation of the parking lot to be built. The calculation formula of the parking traffic flow in the study area is: Where: traffic is the parking traffic flow in the study area, in veh / h, demand max is the demand for public parking spaces in the study area; α is the park-and-go rate; β is the peak hour parking correction parameter, that is, the ratio of peak hour to average hourly parking volume; R is the parking space turnover rate, and t is the proportion of parking spaces in urban public parking lots.

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