CN111008730B - Crowd concentration prediction model construction method and device based on urban space structure - Google Patents
Crowd concentration prediction model construction method and device based on urban space structure Download PDFInfo
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Abstract
The invention discloses a crowd concentration prediction model construction and prediction method based on a city space structure, which provides four environment variable estimation crowd density using road network reachability, bus reachability, subway reachability and business vitality, wherein the four environment variable values can reflect the crowd concentration of a region to a great extent, have high correlation with the crowd concentration value, and can be used as four important environment variables for measuring the crowd concentration, thereby improving the accuracy of crowd concentration estimation.
Description
Technical Field
The invention relates to a method and a device for constructing a crowd concentration prediction model, in particular to a method and a device for constructing a crowd concentration prediction model based on a city space structure.
Background
With the gradual change of population and economic status of China, the development of China cities is changed from incremental planning to stock planning, so that urban management and comfort status are improved, and one of the most main means is to improve the convenience level of pedestrian walking.
In the past studies, there are mainly two methods for estimating pedestrian concentration: based on a traditional four-stage traffic estimation model, a pedestrian mode is included, but pedestrian cells are split only according to an average distribution space characteristic method, so that the demand characteristic of pedestrian travel cannot be estimated; the other is to calculate pedestrian flow and collision situation between pedestrians and vehicles based on the integration level calculated by using space syntax, and the acquisition method is limited by objective conditions, has few data points and is difficult to be truly applied to the whole urban road network.
In summary, the existing pedestrian concentration estimation method has the problem of inaccurate prediction results.
Disclosure of Invention
The invention aims to provide a crowd concentration prediction model construction method and device based on an urban space structure, which are used for solving the problems of inaccurate prediction results and the like of pedestrian concentration estimation methods and devices in the prior art.
In order to realize the tasks, the invention adopts the following technical scheme:
the crowd concentration prediction model construction method based on the urban space structure is used for obtaining a crowd concentration prediction model according to a walking road network of a region to be estimated, and is implemented according to the following method:
Step 1, obtaining a walking road network of a region to be estimated, and uniformly segmenting the walking road network to obtain a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
step 2, acquiring an environment variable of each road section sampling point, and acquiring a sampling point environment parameter set;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
and step 3, taking the environment variable set as input, taking the tag set as output, and training a random forest model to obtain a crowd concentration prediction model of the region to be estimated.
Further, when the road network reachability parameter of each road sampling point is obtained in the step 2, the method specifically includes:
step A, uniformly segmenting the walking road network according to a certain distance to obtain a plurality of line segment nodes;
step B, obtaining the road network reachability parameters of each line segment node in a plurality of radius ranges;
wherein the ith line segment node d is obtained by adopting the I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j The shortest distance between the two is m;
step C, obtaining a road network parameter of each line segment node in the same radius range, and obtaining a first road network parameter corresponding to each line segment node;
assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step D, repeating the step C until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point.
Further, when the bus accessibility parameter of each road section sampling point is obtained in the step 2, the method specifically includes:
Step a, obtaining bus routes of a region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein the bus line segments are formed by connecting a plurality of bus stops;
step b, repeating the step to obtain bus parameters of all bus stops in a plurality of radius ranges:
obtaining bus parameters B (p, R) of a p-th bus stop in a radius range R by adopting a formula II, wherein the unit of R is m:
wherein N is the total number of bus stops obtained in the step a, N is a positive integer, and k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
step c, obtaining bus parameters of each bus stop in the same radius range, and obtaining first bus parameters corresponding to each bus stop;
obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after converting the urban bus thermodynamic diagram raster data into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
Calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step d, repeating the step c until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
step e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
Further, when the subway reachability parameter of each road section sampling point is obtained in the step 2, the method specifically includes:
step I, obtaining a subway line map of a region to be estimated, merging identical sites in the subway line map, and breaking the sites to obtain an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein the subway line segments are formed by connecting a plurality of subway stations;
step II, repeating the step to obtain subway parameters of all subway stations in a plurality of radius ranges:
Obtaining subway parameters S (u, t) of a ith subway station in a radius range t by adopting a formula III, wherein the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
III, obtaining subway parameters of each subway station in the same radius range, and obtaining first subway parameters corresponding to each subway station;
obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
after converting the urban subway thermodynamic diagram raster data into point data, obtaining subway parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step IV, repeating the step III until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
v, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
The crowd concentration prediction method based on the urban space structure is implemented according to the following steps:
step1, acquiring a walking road network of a region to be estimated, and acquiring environmental parameters of sampling points of each road section in the region to be estimated by adopting a method of Step2 in a crowd aggregation prediction model construction method based on a city space structure to acquire an environmental parameter set;
step2, inputting the environmental parameter set obtained in Step1 into a crowd concentration prediction model constructed by a crowd concentration prediction model construction method based on the urban space structure, and obtaining crowd concentration.
A crowd concentration prediction model construction device based on an urban space structure comprises a data acquisition module, a data preprocessing module and a model construction module:
the data acquisition module is used for acquiring a walking road network of a region to be estimated, uniformly segmenting the walking road network, and acquiring a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
the data preprocessing module is used for acquiring environment variables of sampling points of each road section and acquiring a sampling point environment parameter set;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
The model construction module is used for taking the environment variable set as input, taking the tag set as output, training a random forest model and obtaining a crowd concentration prediction model of the region to be estimated.
Further, the data preprocessing module comprises a road network reachability parameter calculation sub-module for acquiring the road network reachability parameter of each road section sampling point;
the road network reachability parameter calculation submodule comprises a road network segmentation unit, a road network parameter calculation unit, a road network correlation calculation unit and a road network reachability parameter acquisition unit;
the road network segmentation unit is used for uniformly segmenting the walking road network to obtain a plurality of line segment nodes;
the road network parameter calculation unit is used for obtaining the road network accessibility parameter of each line segment node in a plurality of radius ranges, wherein the ith line segment node d is obtained by adopting the formula I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j The shortest distance between the two is m;
the road network correlation calculation unit is used for obtaining road network parameters of each line segment node in the same radius range and obtaining a first road network parameter corresponding to each line segment node;
assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
obtaining a correlation value corresponding to each radius range, and obtaining a plurality of correlation values;
the road network reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point.
Further, the data preprocessing module further comprises a bus accessibility parameter calculation sub-module for acquiring bus accessibility parameters of the sampling points of each road section;
The public transport reachability parameter calculation submodule comprises a public transport segmentation unit, a public transport parameter calculation unit, a public transport correlation calculation unit and a public transport reachability parameter obtaining unit;
the bus segmentation unit is used for acquiring bus routes of the region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein the bus line segments are formed by connecting a plurality of bus stops;
the bus parameter calculation unit is used for obtaining bus parameters of all bus stops in a plurality of radius ranges, wherein a formula II is adopted for obtaining bus parameters B (p, R) of a p-th bus stop in a radius range R, and the unit of R is m:
wherein N is the total number of bus stops obtained in the step a, N is a positive integer, and k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
the bus correlation calculation unit is used for obtaining bus parameters of each bus stop in the same radius range and obtaining a first bus parameter corresponding to each bus stop;
Obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after converting the urban bus thermodynamic diagram raster data into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the bus accessibility parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
Further, the data preprocessing module further comprises a subway reachability parameter calculation sub-module for acquiring the subway reachability parameter of each road section sampling point;
the subway reachability parameter calculation submodule comprises a subway segmentation unit, a subway parameter calculation unit, a subway correlation calculation unit and a subway reachability parameter obtaining unit;
The subway segmentation unit is used for acquiring a subway line map of an area to be estimated, merging stations with the same name in the subway line map, and breaking the subway line map at the stations to acquire an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein the subway line segments are formed by connecting a plurality of subway stations;
the subway parameter calculation unit is used for obtaining subway parameters of all subway stations in a plurality of radius ranges, wherein a formula III is adopted for obtaining subway parameters S (u, t) of a ith subway station in a radius range t, and the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
the subway correlation calculation unit is used for obtaining subway parameters of each subway station in the same radius range and obtaining first subway parameters corresponding to each subway station;
obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
after converting the urban subway thermodynamic diagram raster data into point data, obtaining subway parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
Calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the subway reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum relativity value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
The crowd concentration prediction device based on the urban space structure comprises a data acquisition module, a crowd concentration prediction model construction device based on the urban space structure and a prediction module;
the data acquisition module is used for acquiring a walking road network of the region to be estimated;
the data preprocessing module is also used for inputting the walking road network of the region to be estimated into the crowd aggregation prediction model building device based on the urban space structure to obtain the environmental parameters of each road section sampling point in the region to be estimated, and obtaining an environmental parameter set;
The crowd concentration prediction model construction device based on the urban space structure is used for obtaining a crowd concentration prediction model;
the prediction module is used for inputting the environment parameter set into the crowd concentration prediction model to obtain crowd concentration.
Compared with the prior art, the invention has the following technical effects:
1. the crowd concentration prediction model construction method and device based on the urban space structure provided by the invention provides four environment variables of application road network reachability, bus reachability, subway reachability and commercial vitality to estimate crowd density, the four environment variable values can reflect the crowd concentration of a region to a great extent, and have high correlation with the crowd concentration value, and can be used as four important environment variables for measuring the crowd concentration, so that the accuracy of crowd concentration estimation is improved;
2. the method and the device for constructing the crowd gathering prediction model based on the urban space structure provide a new method for calculating the accessibility parameters of the road network, and when the road network is acquired, the road network including walking roads can be acquired as detailed as possible, and the road network is processed after being broken again, so that overlong or excessively short road sections are avoided, and the calculation accuracy is improved; meanwhile, a simple calculation method of space syntax is adopted and is commonly accepted by the public, and finally, the road section sampling points are assigned again through neighbor analysis, and the radius with the strongest correlation with the crowd concentration degree is compared and screened again in the process to be used as the calculation radius, so that the accuracy of crowd concentration degree estimation is improved, and the calculation is convenient and concise, high in accuracy and strong in practicability;
3. The method and the device for constructing the crowd gathering prediction model based on the urban space structure provide a new method for calculating the bus accessibility and subway accessibility parameters, take stations as nodes for calculating the bus accessibility and subway accessibility, accord with the actual condition in real life, and change into more convenient stations at the places with higher crowd gathering degree; meanwhile, the calculated nodes are fused into the thermodynamic diagram, so that the accuracy of the obtained result can be intuitively seen and verified, meanwhile, the node value after raster data turning point data is used as the final result for assigning values to the sampling points, the node density is increased, the accuracy of the calculated result is improved, and accordingly the accuracy of crowd density estimation is improved.
Drawings
FIG. 1 is a graph of aggregate crowd flow thermodynamic diagrams for micro-confidence provided in one embodiment of the invention;
FIG. 2 is a graph of pedestrian accessibility metric radius versus pedestrian concentration correlation provided in one embodiment of the invention;
FIG. 3 is a graph of a correlation between a radius of a bus stop and a population concentration provided in one embodiment of the present invention;
FIG. 4 is a graph of the results of calculation of bus reachability parameters provided in one embodiment of the present invention;
FIG. 5 is a graph of subway reachability parameter calculation results provided in one embodiment of the present invention;
FIG. 6 is a graph of business POI aggregation level provided in one embodiment of the invention;
fig. 7 is a scatter plot of population concentration estimates versus measured values provided in one embodiment of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and examples. So that those skilled in the art may better understand the present invention. It is to be expressly noted that in the description below, detailed descriptions of known functions and designs are omitted here as perhaps obscuring the present invention.
The definition or concept of the present invention is described below:
road section sampling point: the road segment is represented by a data point that includes information such as the location of the road segment.
Road network reachability: the degree of accessibility of the road network under the influence of network layout, transport conditions (traffic patterns) and land use is referred to herein mainly as reachability in network layout space.
Bus accessibility: the degree of access of a certain bus on the network layout is mainly biased to the convenience degree of obtaining bus service by an individual.
Subway accessibility: the degree of accessibility of the subway on the space network structure and the convenience degree of transfer.
Urban commercial viability: the level of vitality of the commercial development is mainly represented by the density of the commercial interest points, and the denser the commercial interest points, the higher the commercial vitality value of the place.
Urban bus thermodynamic diagram: the advantage and disadvantage of bus accessibility are represented in the form of special highlighting, and the brighter place in the thermodynamic diagram represents the better bus accessibility.
Urban subway thermodynamic diagram: the accessibility of the subway is expressed in a special highlight mode, and the brighter places in the thermodynamic diagram are the more convenient and the better the accessibility of the subway is.
Example 1
The crowd concentration prediction model construction method based on the urban space structure is used for obtaining a crowd concentration prediction model according to a walking road network of a region to be estimated, and is implemented according to the following method:
step 1, acquiring a walking road network of a region to be estimated, and uniformly segmenting the walking road network to obtain a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
in this embodiment, a walking road network in the western city is acquired, broken into small road sections at intervals of 30m in arcgis, and these small road sections are converted into road section sampling points.
Acquiring a western security market crowd gathering thermodynamic diagram of micro-letter trips in different time periods as a data base, as shown in fig. 1; and then carrying out neighbor analysis on the crowd gathering thermodynamic diagram to obtain the crowd gathering value of the sampling points of each road section in the road network, namely the label value.
Step 2, acquiring an environment variable of each road section sampling point, and acquiring a sampling point environment parameter set;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
in this embodiment, the road network reachability parameter, the bus reachability parameter, the subway reachability parameter, and the city business vitality parameter are all available in the prior art.
However, in the present invention, because of considering several factors closely related to crowd concentration, four parameters including road network accessibility, bus accessibility, subway accessibility and business activity are selected, and in the process of processing data and further calculating, in order to improve the accuracy of calculation, a series of data processing methods are provided, and example results further indicate that the selected parameters and the used methods have high accuracy and practicality.
Optionally, when the road network reachability parameter of each road section sampling point is obtained in step 2, the method specifically includes:
Step A, uniformly segmenting a walking road network to obtain a plurality of line segment nodes;
step B, obtaining the road network reachability parameters of each line segment node in a plurality of radius ranges;
wherein the ith line segment node d is obtained by adopting the I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j Shortest distance between each other, unitIs m;
step C, obtaining a road network parameter of each line segment node in the same radius range, and obtaining a first road network parameter corresponding to each line segment node;
assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
Step D, repeating the step C until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point.
In this embodiment, the statistical process is shown in fig. 2, and when the radius is 6000 meters, the walking reachability and crowd gathering degree correlation are the strongest. And finally, assigning the value of the line segment node closest to each sampling point in the obtained line segment nodes to the sampling point by a neighbor calculation method, and taking the value as a road network reachability value of the sampling point.
Optionally, when the bus accessibility parameter of each road section sampling point is obtained in step 2, the method specifically includes:
step a, obtaining bus routes of a region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein each bus line segment consists of a plurality of bus stop connecting lines;
step b, repeating the step to obtain bus parameters of all bus stops in a plurality of radius ranges:
Obtaining bus parameters B (p, R) of a p-th bus stop in a radius range R by adopting a formula II, wherein the unit of R is m:
wherein N is the total number of bus stops obtained in the step a, N is a positive integer, and k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
step c, obtaining bus parameters of each bus stop in the same radius range, and obtaining first bus parameters corresponding to each bus stop;
obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after urban bus thermodynamic diagram raster data are converted into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step d, repeating the step c until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
Step e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
In this embodiment, the analysis process is shown in fig. 3, and when the radius is 2600m, the public transportation reachability has the strongest correlation with the crowd gathering degree, and then the reachability coverage area with the radius of 2600m is constructed by the nuclear density method and is fused into the public transportation reachability thermodynamic diagram of the whole city; finally, the bus accessibility parameters of the sampling points of the first road section are obtained through a neighbor analysis method, and the final bus accessibility processing result is shown in fig. 4.
Optionally, when the subway reachability parameter of each road section sampling point is obtained in step 2, the method specifically includes:
step I, obtaining a subway line map of a region to be estimated, merging identical sites in the subway line map, and breaking the sites to obtain an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein each subway line segment consists of a plurality of subway station connecting lines;
step II, repeating the step to obtain subway parameters of all subway stations in a plurality of radius ranges:
Obtaining subway parameters S (u, t) of a ith subway station in a radius range t by adopting a formula III, wherein the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
III, obtaining subway parameters of each subway station in the same radius range, and obtaining first subway parameters corresponding to each subway station;
obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
after urban subway thermodynamic diagram raster data are converted into point data, subway parameters of sampling points of each road section corresponding to the current radius range are obtained by utilizing a neighbor calculation method;
calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step IV, repeating the step III until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
v, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
In the embodiment, firstly, three subway lines built in the western security city are selected, stations with the same name are combined, and then after the stations are broken, a ground railway network is converted into an axis map with space syntax; space integration is then calculated using DepthMap space syntax. Finally, constructing an accessibility coverage area with the radius of 1500m by taking each subway station as the center of a circle through a nuclear density method, fusing the accessibility coverage area into a subway accessibility thermodynamic diagram of the whole city, and calculating the subway accessibility of the sampling points through a proximity calculation method, wherein the processing result is shown in fig. 5.
In this embodiment, obtaining the city business activity parameters of the road section sampling points specifically includes:
1. acquiring a map of a region to be estimated and business activity data of catering, entertainment and shopping, wherein the business activity data comprises attribute information such as geographic positions, names, categories and the like of business activities;
2. the obtained commercial POI data are assigned to a map according to the coordinate positions, and a city commercial activity thermodynamic diagram is generated;
3. converting the business activity thermodynamic diagram raster data into point data, performing neighbor analysis on a sampling point i, and selecting the nearest business activity point value of the sampling point of the ith road section to be assigned to the sampling point as the city business activity parameter of the ith sampling point;
In this embodiment, first, data of business catering and shopping poi of the western security city 5 in 2018 is collected and acquired, and 151819 pieces of data are collected in total, including attribute information such as geographic location, name and category. Secondly, for simplicity, the differences in size and class of different poi are not considered, assuming that the impact of various commercial poi points of interest on pedestrians is similar; further, the commercial viability thermodynamic diagram of the entire western city was calculated by the nuclear density buffering method (the buffer radius was set to 300 meters), and the commercial viability of the sampling points was calculated by the proximity calculation method as shown in fig. 6.
And 3, taking the environment variable set as input, taking the tag set as output, and training the neural network model to obtain the crowd concentration prediction model of the region to be estimated.
In this embodiment, the neural network model is a random forest prediction module, and first, initializing a tree (N) and a depth (M) of a random tree, where n=500 (default), and m=4; secondly, taking 70% of data of the obtained parameter matrix as input as a training set, adopting a Bootstrap resampling technology, and randomly extracting N (500) sub-training sets in a replaced mode; and finally, independently learning the extracted 500 training sets to generate 500 decision trees to form a required random forest regression model.
Example two
The crowd concentration prediction method based on the urban space structure is implemented according to the following steps:
step1, acquiring a walking road network of a region to be estimated, and acquiring environmental parameters of sampling points of each road section in the region to be estimated by adopting the method of the Step2 in the crowd aggregation prediction model construction method based on the urban space structure in the first embodiment to acquire an environmental parameter set;
step2, inputting the environmental parameter set obtained in Step1 into the crowd concentration prediction model constructed by the crowd concentration prediction model construction method based on the urban space structure in the first embodiment, so as to obtain the crowd concentration.
In this embodiment, four environmental variables including walking convenience, bus accessibility, subway accessibility and city business vitality of the remaining 30% of verification set data in the first embodiment are used as inputs to obtain crowd density prediction values, and accuracy of the calculation model is calculated. The method comprises the following specific steps:
firstly, based on the obtained random forest, the rest 30% of data is used as a verification set, and four environment variables including walking convenience, bus accessibility, subway accessibility and urban business vitality are input to obtain the predicted crowd gathering degreeAccording to the above; and secondly, calculating the average value of the predicted people group concentration data of each decision tree as a final predicted value. Finally, by calculating the Mean Square Error (MSE) and the true value and the deterministic coefficient (R 2 ) Judging the accuracy and the reference value of the model, wherein the calculation formula is as follows:
in which Q i As a result of the actual measurement of the value,for predictive value +.>Is the mean of the true values.
Based on Mean Square Error (MSE) and deterministic coefficient (R) 2 ) Judging the model precision of a random forest regression model, wherein the smaller the MSE is, the higher the model precision is, and R is 2 The closer to 1, the stronger the model reference value. The calculation results are shown in table 1, the certainty coefficients of the random forest regression model in each period are higher than 95% and the mean square error is smaller than 0.02, so that the random forest regression model established by the model has high prediction accuracy while ensuring high reference value, and is sufficient for predicting the spatial distribution condition of people in a city in the city range according to related data, and the model is scientific enough for predicting the actual people flow aggregation degree.
TABLE 1 statistical table of prediction results of random forest regression model at different time periods
In order to visually observe the relationship between the actual value and the predicted value of the crowd concentration, a scatter diagram of the actual value and the predicted value of the crowd concentration is shown in fig. 7, and the distribution of the actual value and the predicted value of the crowd concentration shows high consistency. Therefore, the random forest regression model obtained through training of four kinds of western medicine basic data is very ideal in prediction accuracy and certainty index of people group concentration, and the certainty coefficient is up to more than 95%, so that the prediction model established through random forest regression can predict the crowd distribution condition in the city range quite accurately according to the basic data.
Example III
A crowd concentration prediction model construction device based on an urban space structure comprises a data acquisition module, a data preprocessing module and a model construction module:
the data acquisition module is used for acquiring a walking road network of a region to be estimated, uniformly segmenting the walking road network, and acquiring a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
the data preprocessing module is used for acquiring environment variables of sampling points of each road section and acquiring an environment parameter set of the sampling points;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
the model building module is used for taking the environment variable set as input, taking the tag set as output, training the neural network model and obtaining the crowd concentration prediction model of the region to be estimated.
Optionally, the data preprocessing module comprises a road network reachability parameter calculation sub-module for acquiring the road network reachability parameter of each road section sampling point;
the road network reachability parameter calculation submodule comprises a road network segmentation unit, a road network parameter calculation unit, a road network correlation calculation unit and a road network reachability parameter obtaining unit;
The road network segmentation unit is used for uniformly segmenting the walking road network to obtain a plurality of line segment nodes;
the road network parameter calculation unit is used for obtaining the road network accessibility parameter of each line segment node in a plurality of radius ranges, wherein the I line segment node d is obtained by adopting the I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j The shortest distance between the two is m;
the road network correlation calculation unit is used for obtaining road network parameters of each line segment node in the same radius range and obtaining a first road network parameter corresponding to each line segment node;
assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
Obtaining a correlation value corresponding to each radius range, and obtaining a plurality of correlation values;
the road network reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point.
Optionally, the data preprocessing module further comprises a bus accessibility parameter calculation sub-module for acquiring bus accessibility parameters of the sampling points of each road section;
the public transport reachability parameter calculation submodule comprises a public transport segmentation unit, a public transport parameter calculation unit, a public transport correlation calculation unit and a public transport reachability parameter acquisition unit;
the bus segmentation unit is used for acquiring bus routes of the region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing a topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein each bus line segment consists of a plurality of bus stop connecting lines;
the public transportation parameter calculation unit is used for obtaining public transportation parameters of all public transportation stations in a plurality of radius ranges, wherein a formula II is adopted for obtaining public transportation parameters B (p, R) of the p-th public transportation station in a radius range R, and the unit of R is m:
Wherein N is the total number of bus stops obtained in the step a, N is a positive integer, and k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
the bus correlation calculation unit is used for obtaining bus parameters of each bus stop in the same radius range and obtaining a first bus parameter corresponding to each bus stop;
obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after urban bus thermodynamic diagram raster data are converted into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the bus accessibility parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
Optionally, the data preprocessing module further comprises a subway reachability parameter calculation sub-module for acquiring the subway reachability parameter of each road section sampling point;
the subway reachability parameter calculation submodule comprises a subway segmentation unit, a subway parameter calculation unit, a subway correlation calculation unit and a subway reachability parameter obtaining unit;
the subway segmentation unit is used for acquiring a subway line map of a region to be estimated, merging sites with the same name in the subway line map, and breaking the sites to obtain an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein each subway line segment consists of a plurality of subway station connecting lines;
the subway parameter calculation unit is used for obtaining subway parameters of all subway stations in a plurality of radius ranges, wherein a formula III is adopted for obtaining subway parameters S (u, t) of a ith subway station in a radius range t, and the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
the subway correlation calculation unit is used for obtaining subway parameters of each subway station in the same radius range and obtaining first subway parameters corresponding to each subway station;
Obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
after urban subway thermodynamic diagram raster data are converted into point data, subway parameters of sampling points of each road section corresponding to the current radius range are obtained by utilizing a neighbor calculation method;
calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the subway reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum relativity value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
Example IV
The crowd concentration prediction device based on the urban space structure comprises a data acquisition module, a crowd concentration prediction model construction device based on the urban space structure in the third embodiment and a prediction module;
the data acquisition module is used for acquiring a walking road network of the region to be estimated;
The method is also used for inputting a walking road network of the region to be estimated into a crowd aggregation prediction model construction device based on the urban space structure in the third embodiment, and obtaining environmental parameters of sampling points of each road section in the region to be estimated by a data preprocessing module to obtain an environmental parameter set;
the crowd concentration prediction model construction device based on the urban space structure is used for obtaining a crowd concentration prediction model;
the prediction module is used for inputting the environmental parameter set into the crowd concentration prediction model to obtain crowd concentration.
Claims (8)
1. The method for constructing the crowd concentration prediction model based on the urban space structure is used for obtaining the crowd concentration prediction model according to the walking road network of the region to be estimated, and is characterized by comprising the following steps of:
step 1, obtaining a walking road network of a region to be estimated, and uniformly segmenting the walking road network to obtain a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
step 2, acquiring an environment variable of each road section sampling point, and acquiring a sampling point environment parameter set;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
When obtaining the road network reachability parameter of each road section sampling point, the method specifically comprises the following steps:
step A, uniformly segmenting the walking road network according to a certain distance to obtain a plurality of line segment nodes;
step B, obtaining the road network reachability parameters of each line segment node in a plurality of radius ranges;
wherein the ith line segment node d is obtained by adopting the I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j The shortest distance between the two is m;
step C, obtaining a road network parameter of each line segment node in the same radius range, and obtaining a first road network parameter corresponding to each line segment node;
assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
Step D, repeating the step C until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point;
and step 3, taking the environmental parameter set as input, taking the tag set as output, and training a random forest model to obtain a crowd concentration prediction model of the region to be estimated.
2. The method for constructing the crowd gathering prediction model based on the urban space structure according to claim 1, wherein the step 2 is characterized by comprising the following steps:
step a, obtaining bus routes of a region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein the bus line segments are formed by connecting a plurality of bus stops;
step b, repeating the step to obtain bus parameters of all bus stops in a plurality of radius ranges:
Obtaining bus parameters B (p, R) of a p-th bus stop in a radius range R by adopting a formula II, wherein the unit of R is m:
wherein N is the total number of bus stops obtained in the step a,n is a positive integer, k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
step c, obtaining bus parameters of each bus stop in the same radius range, and obtaining first bus parameters corresponding to each bus stop;
obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after converting the urban bus thermodynamic diagram raster data into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step d, repeating the step c until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
Step e, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
3. The method for constructing the crowd gathering prediction model based on the urban space structure according to claim 1, wherein the step 2 is characterized by comprising the following steps:
step I, obtaining a subway line map of a region to be estimated, merging identical sites in the subway line map, and breaking the sites to obtain an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein the subway line segments are formed by connecting a plurality of subway stations;
step II, repeating the step to obtain subway parameters of all subway stations in a plurality of radius ranges:
obtaining subway parameters S (u, t) of a ith subway station in a radius range t by adopting a formula III, wherein the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
III, obtaining subway parameters of each subway station in the same radius range, and obtaining first subway parameters corresponding to each subway station;
obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
after converting the urban subway thermodynamic diagram raster data into point data, obtaining subway parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained in the step 1, and obtaining a correlation value corresponding to the current radius range;
step IV, repeating the step III until the correlation value corresponding to each radius range is obtained, and obtaining a plurality of correlation values;
v, selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
4. The crowd concentration prediction method based on the urban space structure is characterized by comprising the following steps of:
Step1, acquiring a walking road network of a region to be estimated, and acquiring environmental parameters of sampling points of each road section in the region to be estimated by adopting the method of Step2 in the crowd gathering prediction model construction method based on the urban space structure according to any one of claims 1-3 to acquire an environmental parameter set;
step2, inputting the environmental parameter set obtained in Step1 into the crowd concentration prediction model constructed by the crowd concentration prediction model construction method based on the urban space structure according to any one of claims 1-3, so as to obtain the crowd concentration.
5. The crowd concentration prediction model construction device based on the urban space structure is characterized by comprising a data acquisition module, a data preprocessing module and a model construction module:
the data acquisition module is used for acquiring a walking road network of a region to be estimated, uniformly segmenting the walking road network, and acquiring a plurality of road section sampling points;
acquiring pedestrian aggregation degree of sampling points of each road section to obtain a tag set;
the data preprocessing module is used for acquiring environment variables of sampling points of each road section and acquiring a sampling point environment parameter set;
the environment variables comprise road network reachability parameters, bus reachability parameters, subway reachability parameters and urban commercial vitality parameters;
The data preprocessing module comprises a road network reachability parameter calculation sub-module for acquiring the road network reachability parameter of each road section sampling point;
the road network reachability parameter calculation submodule comprises a road network segmentation unit, a road network parameter calculation unit, a road network correlation calculation unit and a road network reachability parameter acquisition unit;
the road network segmentation unit is used for uniformly segmenting the walking road network to obtain a plurality of line segment nodes;
the road network parameter calculation unit is used for obtaining that each line segment node is in a plurality of radius rangesThe road network reachability parameter of (1) wherein the ith line segment node d is obtained by adopting the I i Road network parameters I (d) within the radius r i R), r is m:
wherein d j Represents the J-th line segment node, i.noteq.j, j.=1, 2, …, J is the division of the i-th line segment node d obtained in step a i The total number of all line segment nodes except for J is a positive integer,representing node d from the ith line segment i To the j-th line segment node d j The shortest distance between the two is m;
the road network correlation calculation unit is used for obtaining road network parameters of each line segment node in the same radius range and obtaining a first road network parameter corresponding to each line segment node;
Assigning the first road network parameters corresponding to each line segment node to all the road segment sampling points obtained in the step 1 by utilizing a neighbor calculation method to obtain the road network parameters of each road segment sampling point corresponding to the current radius range;
calculating the correlation between the road network parameters of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
obtaining a correlation value corresponding to each radius range, and obtaining a plurality of correlation values;
the road network reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; taking the road network parameters of each road section sampling point corresponding to the optimal radius range as the road network reachability parameters of each road section sampling point;
the model construction module is used for taking the environment parameter set as input, taking the tag set as output, training a random forest model and obtaining a crowd concentration prediction model of the region to be estimated.
6. The device for constructing the crowd gathering prediction model based on the urban space structure according to claim 5, wherein the data preprocessing module further comprises a bus accessibility parameter calculation sub-module for obtaining the bus accessibility parameter of each road section sampling point;
The public transport reachability parameter calculation submodule comprises a public transport segmentation unit, a public transport parameter calculation unit, a public transport correlation calculation unit and a public transport reachability parameter obtaining unit;
the bus segmentation unit is used for acquiring bus routes of the region to be estimated, merging bus stops with the same name in the bus routes, and reconstructing topology to obtain a topology map;
the topological graph comprises a plurality of bus stops and a plurality of bus line segments, wherein the bus line segments are formed by connecting a plurality of bus stops;
the bus parameter calculation unit is used for obtaining bus parameters of all bus stops in a plurality of radius ranges, wherein a formula II is adopted for obtaining bus parameters B (p, R) of a p-th bus stop in a radius range R, and the unit of R is m:
wherein N is the total number of bus stops obtained in the step a, N is a positive integer, and k p To the number of bus line segments passing through the p-th bus stop, k p Is a positive integer, d pq The unit is m for the minimum bus line segment length from the p-th bus stop to the q-th bus stop;
the bus correlation calculation unit is used for obtaining bus parameters of each bus stop in the same radius range and obtaining a first bus parameter corresponding to each bus stop;
Obtaining urban bus thermodynamic diagram grid data according to the first bus parameters corresponding to each bus stop;
after converting the urban bus thermodynamic diagram raster data into point data, obtaining bus parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the public transport parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the bus accessibility parameter obtaining unit is used for selecting a radius range corresponding to the maximum correlation value to obtain an optimal radius range; and taking the bus parameters of each road section sampling point corresponding to the optimal radius range as the bus accessibility parameters of each road section sampling point.
7. The crowd gathering modeling device based on the urban space structure as defined in claim 5, wherein the data preprocessing module further comprises a subway reachability parameter calculation sub-module for acquiring the subway reachability parameter of each road section sampling point;
The subway reachability parameter calculation submodule comprises a subway segmentation unit, a subway parameter calculation unit, a subway correlation calculation unit and a subway reachability parameter obtaining unit;
the subway segmentation unit is used for acquiring a subway line map of an area to be estimated, merging stations with the same name in the subway line map, and breaking the subway line map at the stations to acquire an axis map of space syntax;
the axis map of the space syntax comprises a plurality of subway stations and a plurality of subway line segments, wherein the subway line segments are formed by connecting a plurality of subway stations;
the subway parameter calculation unit is used for obtaining subway parameters of all subway stations in a plurality of radius ranges, wherein a formula III is adopted for obtaining subway parameters S (u, t) of a ith subway station in a radius range t, and the unit of t is m:
wherein U is the total number of subway stations obtained in the step I, U is a positive integer, d uv For the minimum subway line segment length from the u-th subway station to the v-th subway station, the unit is m,
the subway correlation calculation unit is used for obtaining subway parameters of each subway station in the same radius range and obtaining first subway parameters corresponding to each subway station;
obtaining urban subway thermodynamic diagram grid data according to first subway parameters corresponding to each subway station;
After converting the urban subway thermodynamic diagram raster data into point data, obtaining subway parameters of sampling points of each road section corresponding to the current radius range by utilizing a neighbor calculation method;
calculating the correlation between the subway parameter of each road section sampling point corresponding to the current radius range and the pedestrian concentration degree of each road section sampling point obtained by the data obtaining module, and obtaining a correlation value corresponding to the current radius range;
until the correlation value corresponding to each radius range is obtained, obtaining a plurality of correlation values;
the subway reachability parameter obtaining unit is used for selecting a radius range corresponding to the maximum relativity value to obtain an optimal radius range; and taking the subway parameter of each road section sampling point corresponding to the optimal radius range as the subway reachability parameter of each road section sampling point.
8. A crowd concentration prediction device based on a city space structure, which is characterized by comprising a data acquisition module, the crowd concentration prediction model construction device based on the city space structure as claimed in any one of claims 5-7, and a prediction module;
the data acquisition module is used for acquiring a walking road network of the region to be estimated;
The method is also used for inputting the walking road network of the region to be estimated into the crowd gathering prediction model building device based on the urban space structure according to any one of claims 5-7, and the data preprocessing module is used for obtaining the environmental parameters of each road section sampling point in the region to be estimated and obtaining an environmental parameter set;
the crowd concentration prediction model construction device based on the urban space structure is used for obtaining a crowd concentration prediction model;
the prediction module is used for inputting the environment parameter set into the crowd concentration prediction model to obtain crowd concentration.
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