CN116050723A - TOD (time of day) station domain collaborative development evaluation method and device based on crowd source geographic data - Google Patents
TOD (time of day) station domain collaborative development evaluation method and device based on crowd source geographic data Download PDFInfo
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Abstract
The invention provides a TOD (total organic matter) station domain collaborative development evaluation method based on crowd source geographic data, which comprises data collection and preprocessing, comprises collection and arrangement of various data, data format conversion and unified expression of the data, index construction of a node-place model and a node-place-network model, index selection, index conversion and index calibration weight based on an entropy weight method, TOD station domain collaborative development state evaluation, a clustering method is utilized to quantitatively measure the track traffic and land utilization collaborative development state, the loading pressure of the TOD of the station domain is obtained according to a node-place model index aggregation result and a node-place-network model index aggregation result, the current passenger flow loading level of a station is evaluated, and the TOD station domain collaborative development state is comprehensively evaluated.
Description
Technical Field
The invention relates to the technical field of rail transit passenger flow planning, in particular to a TOD (total time of day) station domain collaborative development evaluation method and device based on crowd source geographic data.
Background
Worldwide rapid urbanization and population growth create many urban problems such as air pollution, traffic congestion, and excessive reliance on fossil fuels. The root cause of this phenomenon is that there is no comprehensive development plan for land utilization and traffic that is definitely effective. Traffic Oriented Development (TOD) has been adopted as a commonly followed urban planning strategy aimed at promoting efficient and mixed land utilization near public transportation hubs and stations. In metropolitan areas, subway stations offer more vehicles.
Many researchers have attempted to build different TOD site development types to better perform strategic planning, investment guidance, and site quantification. The most typical method of evaluating TOD types is the node-site model (NP) model, which is essentially a conceptual framework for evaluating station transportation supply and land utilization development. Based on the node-site model, many approaches make various modifications to the original model. The most significant improvement is the expansion of the design dimension, in particular, the need to study from three aspects in order to achieve an efficient fusion of traffic and land utilization: (1) multimode reachability of a station; (2) diversity and strength of land utilization; (3) a walking-oriented design. Similarly, there are also some approaches to extend node-place models in the experience dimension, i.e. reflect traffic quality from the traveler's perspective.
In recent years, passenger travel-related attributes, such as passenger flow volume, have been studied as key indicators. In part of the methods, the correlation of the passenger flow volume and the node-place model index is studied. The node value, the place value and the passenger flow volume have interaction relation. However, the traffic index has a limitation in reflecting characteristics of the travel network (such as transfer behavior). In addition, the existing research ignores the synergistic effect between the actual travel characteristics of the rail transit and the functional configuration of the physical environment thereof, and is difficult to evaluate the collaborative development state between the station domains of 'people-traffic-environment'.
Disclosure of Invention
According to the defects of the prior art, the invention aims to provide the TOD (total traffic light) station domain collaborative development evaluation method and device based on the crowd-sourced geographic data, so that the rapid and fine evaluation of the station domain passenger flow bearing capacity is realized, the defect that the actual resident trip of the station domain is not considered to cause the description of the station domain passenger flow requirement in the prior art is overcome, and the dimension of TOD station domain collaborative state evaluation is enriched.
In order to solve the technical problems, the invention adopts the following technical scheme:
a TOD station domain collaborative development evaluation method based on crowd source geographic data comprises the following steps:
step S1, data collection and preprocessing, including collection and arrangement of various open source geographic big data and related public data, data format conversion and data unified expression;
s2, constructing indexes of the node-place model and the node-place-network model, wherein the index construction comprises index selection, index transformation and index weighting based on an entropy weighting method;
and S3, evaluating the collaborative development state of the TOD station domain, quantitatively measuring the collaborative development state of the rail transit and the land utilization by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the node-place model index aggregation result and the node-place-network model index aggregation result, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the collaborative development state of the TOD station domain.
Further, in the step S1, the open source geographic big data includes POI data, track traffic card swiping data, OSM road network data, population data, and subway operation data information;
POI data and rail transit card swiping data are vector punctiform data, OSM road network data are vector linear data, population data are vector plane data, a certain range is used as a buffer zone, superposition analysis and statistics are carried out on various types of data, and the data are converted into various types of corresponding attributes of sites within a certain range.
Further, in the step S2, the indexes include a node dimension index, a location dimension index, and a network dimension index;
the indexes of the node dimension comprise the number of adjacent bus stops, the number of track traffic stop lines, the track traffic departure frequency and the number of reachable stops within 20 minutes of the track traffic stops;
the indexes of the place dimension comprise land utilization mixing degree, volume rate, land utilization type, land utilization mixing degree, volume rate, population number, employment post number, community resident number and density of road intersections;
the network dimension indicators include site weighting, site intermediation centrality.
Further, in the step S2, the method for indicating the calibration weight based on the entropy weight method is as follows: for n sites, m indexes, then x ij A j index for an i site, where i=1, …, n; j=1, …, m;
normalizing the index to convert the absolute value of the index into a relative value, wherein the calculation formula is as follows
wherein ,x′ij Is the relative value of the index;
calculating the proportion p of the ith site value to the jth index ij ：
Calculating the entropy value e of the j-th index j ：
wherein ,
satisfy e j ≥0；
Calculating information entropy redundancy d j ：
d j ＝1-e j ，j＝1,…,m
Calculating each fingerTarget weight w j ：
For each site, calculating each integrated score of the node dimension index, the site dimension index and the network dimension index
Further, land utilization mix is measured using shannon entropy:
wherein pik represents the proportion of k-th facilities in the site i, n represents the number of all facilities in the site i, and the larger the value is, the higher the land utilization diversity is; the smaller the value, the more single the land utilization; the formula is only valid for areas with more than one land use type, otherwise, 0 is returned, which means that the land use diversity is completely absent in the range of the station domain;
the station weighting degree represents the sum of weighted connections among stations in rail transit, represents the total amount of travel passenger flow among the station i and all adjacent stations, and has the following calculation formula:
wherein ,lpq (i) Representing the number of shortest paths between site p and site q through site i, l pq The shortest path length between the stations p and q is represented, in the information propagation network, the stations located at the places with higher intermediation centrality can effectively control information transfer to further influence the group, and in the traffic network, one station with higher intermediation centrality can be considered to be located at the junction transfer point in the track traffic network.
Further, in the step S3, the node-place-network model is decomposed into a node-network model, a node-place model and a place-network model, and the traffic and land utilization cooperativity in the rail site area is classified into balanced development, excessive development, low-level development and unbalanced development;
and clustering each dimension index of the node-place-network model by using an SOM clustering method so as to quickly obtain a clustering result.
Further, the calculation formula of the bearing pressure of the station passenger flow is as follows:
wherein T (-) represents the TOD score, which is the sum of the Index scores of the dimensions, and is comparable, represents the level of TOD development of the site, and Index (-) represents the score of a certain dimension.
TOD station domain collaborative development evaluation device based on crowd source geographic data comprises:
the data collection and preprocessing module is used for data collection and preprocessing, and comprises collection and arrangement of various open source geographic big data, related public data, data format conversion and data unified expression;
the index construction module is used for index construction of the node-place model and the node-place-network model and comprises index selection, index transformation and index weighting based on an entropy weighting method;
the state evaluation module is used for evaluating the TOD station domain collaborative development state, quantitatively measuring the rail traffic and land utilization collaborative development state by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the TOD conditions of the node-place model and the node-place-network model, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the TOD station domain collaborative development state.
The TOD station domain collaborative development evaluation device based on the crowd-sourced geographic data comprises a processor and a memory for storing a computer program capable of running on the processor, wherein the processor is used for executing the TOD station domain collaborative development evaluation method based on the crowd-sourced geographic data when running the computer program.
A computer storage medium having a computer program stored therein, which when executed by a processor, implements the steps of any one of the aforementioned TOD site domain collaborative development evaluation methods based on crowd-sourced geographical data.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The invention adopts the refined passenger flow data and other open geographic data to evaluate the station domain development state, and has fine space-time resolution and higher timeliness;
(2) The node-place-network model provided by the invention can effectively identify the collaborative development state of the site 'human-traffic-environment';
(3) The bearing pressure index provided by the invention can objectively and accurately reveal the mismatch degree of the current environment and the passenger flow, and is beneficial to promoting the integrated development of traffic and built environment in site areas.
Drawings
In order to more clearly illustrate the technical solutions of the present embodiment, the drawings required for the description of the embodiment will be briefly described below, and it is obvious that the drawings in the following description are one embodiment of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a general flow chart of the present invention.
FIG. 2 is a flow chart of an embodiment of the present invention.
FIG. 3 is a correlation of nodes, locales and network metrics.
Fig. 4 (a) is a node-network dimension SOM classification result.
Fig. 4 (b) is a site-network dimension SOM classification result.
Fig. 4 (c) is a site-node dimension SOM classification result.
Fig. 5 is a low-dimensional representation of a site SOM cluster feature.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a TOD (total organic light) station domain collaborative development evaluation method based on crowd-sourced geographic data, which is shown in fig. 1 and 2 and comprises the following steps of:
step S1, data collection and preprocessing, including collection and arrangement of various open source geographic big data and related public data, data format conversion and data unified expression;
s2, constructing indexes of the node-place model and the node-place-network model, wherein the index construction comprises index selection, index transformation and index weighting based on an entropy weighting method;
and S3, evaluating the collaborative development state of the TOD station domain, quantitatively measuring the collaborative development state of the rail transit and the land utilization by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the node-place model index aggregation result and the node-place-network model index aggregation result, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the collaborative development state of the TOD station domain.
The invention provides a TOD (total traffic light) station domain collaborative development evaluation method based on crowd-sourced geographic data, which realizes rapid and fine evaluation of the station domain passenger flow bearing capacity, overcomes the defect that the conventional node-place model does not consider the actual resident travel of the station domain to cause the description of the station domain passenger flow demand, and enriches the dimensionality of TOD station domain collaborative state evaluation.
The invention provides a TOD (total time of day) station domain collaborative development evaluation method based on crowd-sourced geographic data, which adopts refined passenger flow data and other open geographic data to evaluate the development state of the station domain, and has fine space-time resolution and higher timeliness;
the invention provides a TOD (total energy distribution) station domain collaborative development evaluation method based on crowd-sourced geographic data, and the proposed node-place-network model can effectively identify the collaborative development state of a station 'people-traffic-environment';
the invention provides a TOD (total internal area) station domain collaborative development evaluation method based on crowd-sourced geographic data, which provides a bearing pressure index, can objectively and accurately reveal the mismatch degree of the current environment and passenger flow, and is beneficial to promoting the integrated development of traffic and built-up environment in a station area.
In the invention, in the step S1, the open source geographic big data includes POI data, track traffic card swiping data, OSM road network data, population data and subway operation data information;
POI data and rail transit card swiping data are vector punctiform data, OSM road network data are vector linear data, population data are vector plane data, a certain range is used as a buffer zone, superposition analysis and statistics are carried out on various types of data, and the data are converted into various types of corresponding attributes of sites within a certain range, wherein the certain range is within 1 KM.
Specifically, POI data can be obtained through a golde platform; government website data provides basic information of a subway network, such as subway daily frequency, subway service direction number and the like. Road related information, such as intersection density and reachable network length, can be obtained from OSM data. Demographic data was from LandScan, the American oak ridge national laboratory (http:// web. Ornl. Gov/sci/land can).
In the present invention, in the step S2, the indexes include a node dimension index, a location dimension index, and a network dimension index, and a node (node) dimension represents the density and diversity of the traffic facilities; site dimensions (places) characterize land utilization density and diversity; a network dimension characterizes the network importance of a station in a rail transit system; the availability of the data, the availability of the data and the effectiveness of the data are comprehensively considered, and geographic big data (such as electronic map POI data, passenger flow trip data and the like) and traditional statistical investigation data (such as rail transit management department line operation information and the like) are combined.
The indexes of the node dimension comprise the number of adjacent bus stops, the number of track traffic stop lines, the track traffic departure frequency and the number of reachable stops within 20 minutes of the track traffic stops.
The indicators of site dimensions include land utilization mix, volume rate, land type (corporate enterprise, recreational facility, public facility, green park, restaurant), population count, employment number, cell population count, and density of road intersections.
The network dimension indicators include site weighting, site intermediation centrality.
In the present invention, in the step S2, in order to enhance the comparability between dimensions and reduce the influence of data skew distribution during the index transformation, the Box-Cox index transformation is performed on the index.
In the present invention, in the step S2, the index transformation and the index weighting based on the entropy weighting method mainly include index normalization and weighting the index by using entropy. In information theory, entropy is a measure of uncertainty. The larger the uncertainty, the larger the entropy and the larger the amount of information contained; the smaller the uncertainty, the smaller the entropy and the smaller the amount of information contained. According to the characteristics of entropy, the randomness and disorder degree of an event can be judged by calculating the entropy value, or the degree of dispersion of a certain index can be judged by using the entropy value, and the larger the degree of dispersion of the index is, the larger the influence (weight) of the index on comprehensive evaluation is. The entropy weighting method is an objective weighting method because it depends only on the discreteness of the data itself. The invention uses entropy weight method to weight the index of node-place-network model.
The method for indicating the calibration weight based on the entropy weight method comprises the following steps: for n sites, m indexes, then x ij A j index for an i site, where i=1, …, n; j=1, …, m;
the normalization processing of the index converts the absolute value of the index into a relative value, and the calculation formula is as follows:
wherein ,x′ij Is the relative value of the index;
calculating the proportion p of the ith site value to the jth index ij ：
Calculating the entropy value e of the j-th index j ：
wherein ,
satisfy e j ≥0；
Calculating information entropy redundancy d j ：
d j ＝1-e j ，j＝1,…,m
Calculating the weight w of each index j ：
For each site, calculating each integrated score of the node dimension index, the site dimension index and the network dimension index
In the invention, the land utilization mixing degree is a measure of the degree of spatial diversity of land utilization types and urban functions in an area, and the land utilization mixing degree is measured by using shannon entropy:
wherein pik represents the proportion of k-th facilities in the site i, n represents the number of all facilities in the site i, and the larger the value is, the higher the land utilization diversity is; the smaller the value, the more single the land utilization; the formula is valid only for regions with more than one land use type, otherwise 0 is returned, indicating that the land use diversity is completely absent within the range of the station.
The central emphasis in the site is the ability of the point to mediate to other points. If a node is located between multiple nodes, the node has a large intermediation center and plays an important role in intermediation for other nodes. The intermediation centrality reflects the connectivity between sites, which is an important indicator characterizing subway hub sites. Nodes with higher intermediacy centers indicate more shortest paths through the site in the network.
The station weighting degree represents the sum of weighted connections among stations in rail transit, represents the total amount of travel passenger flow among the station i and all adjacent stations, and has the following calculation formula:
wherein ,lpq (i) Representing the number of shortest paths between site p and site q through site i, l pq The shortest path length between the stations p and q is represented, in the information propagation network, the stations located at the places with higher intermediation centrality can effectively control information transfer to further influence the group, and in the traffic network, one station with higher intermediation centrality can be considered to be located at the junction transfer point in the track traffic network.
In the present invention, in the step S3, the node-place-network model is decomposed into the node-network model, the node-place model and the place-network model, and the traffic and land utilization cooperativity in the rail site area is classified into balanced development, excessive development, low development and unbalanced development.
For example, the node-site model, rail site regional traffic and land utilization cooperativity can be divided into 5 cooperativity levels of balanced development, excessive development, low-level development, node imbalance and site imbalance. The two sides of the middle oblique line are provided with 'balanced development' areas, which indicate that the node value and the place value are basically cooperated. Located at the top of the midline is an "over-developed" area, representing the maximum state of traffic flow and functional activity and diversity, which is the type of most urban central site areas. The "low development" area at the bottom of the midline represents a third type of typical situation where space requirements are minimal in the site area, but traffic travel demands from local residents, employment personnel and other users are as low as urban functional activity demands, as is typical in site areas. In addition, two types of "imbalance development" conditions are included: i.e., areas where traffic development is relatively better than urban functional activity development ("unbalanced nodes"); as opposed to "unbalanced nodes" ("unbalanced sites"). The site area in the state of 'unbalanced node' can either increase the site value or weaken the node value, so that the site area can be guided to return to the cooperative equilibrium state, and the 'unbalanced site' is just opposite, namely, the traffic support level is increased or the function aggregation degree is reduced.
The invention adds network dimension in the existing node-place model, and can effectively identify the collaborative development state of the site 'human-traffic-environment'.
As shown in fig. 3, the correlation between the node, the location and the network index is shown.
In the invention, the self-organizing neural network clustering method, namely the SOM clustering method is utilized to cluster each dimension index of the node-place-network model so as to quickly obtain a clustering result.
Public transportation and land development collaborative level division is based on an SOM clustering method, and the SOM clustering method belongs to the category of competitive learning networks. SOM is based on unsupervised learning, and can be used to detect features inherent to a problem without human intervention during training, and is therefore also known as SOFM, i.e., self-origin feature map. SOM reduces the dimension of data and displays similar attributes between data.
The SOM-based clustering method specifically comprises the following steps:
step S321, initializing the weight of each site;
step S322, randomly selecting a vector from the training data set and presenting the vector to the grid;
step S323, checking each station to calculate which weights most resemble input vectors, the winning station is commonly referred to as a Best Matching Unit (BMU);
step S324, the radius of the Best Matching Unit (BMU) neighborhood is now calculated. This is a very large value at the beginning, usually set to the radius of the crystal lattice, but decreases every time step. Any node found within this radius is considered to be within the neighborhood of the BMU;
step S325, adjusting the weight of each neighboring site (the site found in step S4) to make them more like the input vector, the closer the site is to the Best Matching Unit (BMU); the more its weight changes;
step S326, repeating step S322, and performing N iterations.
The invention utilizes SOM clustering algorithm to cluster each dimension index of the node-place-network model so as to quickly obtain clustering result,
in the invention, the calculation formula of the bearing pressure of the station passenger flow is as follows:
wherein T (-) represents the TOD score, which is the sum of the Index scores of the dimensions, and is comparable, represents the level of TOD development of the site, and Index (-) represents the score of a certain dimension.
For example, index (node) represents the node dimension weighted composite score.
In one embodiment of the invention, taking rail transit in 2018 Shanghai city as an example, the TOD station domain collaborative development evaluation method based on crowd source geographic data is adopted.
Wherein, shanghai POI data is obtained by writing web crawlers to crawl Gao Deping (https:// lbs. amap. Com /), the acquisition year is 2018, and the total is about 64 ten thousand; the subway card number (SCD) in Shanghai is obtained through an open interface (http:// soda. Shdatacenter. Cn /), and the data period is in March 2018; the subway card swiping data contains detailed information of each trip of passengers, including unique identity ID, time of entering and exiting, train fee and station name. Government website data provides basic information of a subway network, such as subway daily frequency, subway service direction number and the like. Road related information, such as intersection density and reachable network length, can be obtained from OSM data. Demographic data was accessed from LandScan, the American oak-ridge national laboratory (http:// web. Ornl. Gov/sci/land can), at 9.2018, which provided demographic information at a resolution of 1km by 1km in Shanghai city.
In the process of data format conversion and data unified expression. Wherein the POI data and the card swiping data can be regarded as vector punctiform data; the OSM data are vector linear data; landscan is vector planar data. According to Shanghai city 2017-2035 general program, TOD stops are defined as rail transit stops 600m range. And (3) taking 600m as a buffer area, carrying out superposition analysis and statistics on various types of data, and converting the data into various attributes corresponding to sites within the 600m range.
The invention utilizes the SOM clustering algorithm to cluster each dimension index of the node-place-network model so as to quickly obtain a clustering result, see fig. 4 (a), 4 (b), 4 (c) and 5.
Through analysis of different cluster bearing pressures, the overall development profile of the clustered sites and the specific conditions of the sites can be obtained. Stations with a high bearing pressure can be classified into three types: 1) Transportation hubs such as rainbow bridge train stations, pudong International airports; 2) Large residential areas such as kiosks and wide blue stations; 3) End stations such as Shen Du highway stations. The site with high bearing pressure has great development potential and can be used as a development candidate area of a potential new castle in the future, and the site with high bearing pressure screened in the research result is consistent with the regional center in Shanghai 2017-2035 general planning, thereby proving the feasibility of the method.
The invention also provides a TOD station domain collaborative development evaluation device based on the crowd source geographic data, which comprises the following steps:
the data collection and preprocessing module is used for data collection and preprocessing, and comprises collection and arrangement of various open source geographic big data, related public data, data format conversion and data unified expression;
the index construction module is used for index construction of the node-place model and the node-place-network model and comprises index selection, index transformation and index weighting based on an entropy weighting method;
the state evaluation module is used for evaluating the TOD station domain collaborative development state, quantitatively measuring the rail traffic and land utilization collaborative development state by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the TOD conditions of the node-place model and the node-place-network model, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the TOD station domain collaborative development state.
The invention also provides TOD (total organic matter) station domain collaborative development evaluation equipment based on the crowd-sourced geographic data, which comprises a processor and a memory, wherein the memory is used for storing a computer program capable of running on the processor, and the processor is used for executing the TOD station domain collaborative development evaluation method based on the crowd-sourced geographic data when running the computer program.
The memory in the embodiment of the invention is used for storing various types of data to support the operation of TOD station domain collaborative development evaluation equipment based on crowd source geographic data. Examples of such data include: any computer program for operating on a TOD site domain co-development assessment device based on crowd source geographical data.
The TOD station domain collaborative development evaluation method based on the crowd source geographic data can be applied to a processor or realized by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the TOD station domain collaborative development evaluation method based on the crowd source geographic data can be completed through an integrated logic circuit of hardware in a processor or instructions in a software form. The processor may be a general purpose processor, a digital signal processor (DSP, digital SignalProcessor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the invention can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium, where the storage medium is located in a memory, and the processor reads information in the memory, and combines with hardware to implement the steps of the TOD site domain collaborative development evaluation method based on crowd source geographic data provided by the embodiment of the invention.
In an exemplary embodiment, the crowd source geographic data based TOD station domain co-development evaluation device may be implemented by one or more application specific integrated circuits (ASICs, application Specific Integrated Circuit), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex programmable logic devices (CPLDs, complex Programmable LogicDevice), FPGAs, general purpose processors, controllers, microcontrollers (MCUs, micro Controller Unit), microprocessors (microprocessors), or other electronic elements for performing the aforementioned methods.
It will be appreciated that the memory can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random AccessMemory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronousDynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr sdram, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). The memory described by embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
The invention also provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program realizes the steps of the TOD station domain collaborative development evaluation method based on the crowd source geographic data.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The TOD station domain collaborative development evaluation method based on the crowd source geographic data is characterized by comprising the following steps of:
step S1, data collection and preprocessing, including collection and arrangement of various open source geographic big data and related public data, data format conversion and data unified expression;
s2, constructing indexes of the node-place model and the node-place-network model, wherein the index construction comprises index selection, index transformation and index weighting based on an entropy weighting method;
and S3, evaluating the collaborative development state of the TOD station domain, quantitatively measuring the collaborative development state of the rail transit and the land utilization by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the node-place model index aggregation result and the node-place-network model index aggregation result, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the collaborative development state of the TOD station domain.
2. The method for evaluating the co-development of TOD (total organic light emitting diode) station domains based on crowd-sourced geographic data as claimed in claim 1, wherein the method comprises the following steps of:
in the step S1, the open source geographic big data includes POI data, track traffic card swiping data, OSM road network data, population data and subway operation data information;
POI data and rail transit card swiping data are vector punctiform data, OSM road network data are vector linear data, population data are vector plane data, a certain range is used as a buffer zone, superposition analysis and statistics are carried out on various types of data, and the data are converted into various types of corresponding attributes of sites within a certain range.
3. The method for evaluating the co-development of TOD (total organic light emitting diode) station domains based on crowd-sourced geographic data as claimed in claim 1, wherein the method comprises the following steps of:
in the step S2, the indexes include a node dimension index, a location dimension index and a network dimension index;
the indexes of the node dimension comprise the number of adjacent bus stops, the number of track traffic stop lines, the track traffic departure frequency and the number of reachable stops within 20 minutes of the track traffic stops;
the indexes of the place dimension comprise land utilization mixing degree, volume rate, land utilization type, land utilization mixing degree, volume rate, population number, employment post number, community resident number and density of road intersections;
the network dimension indicators include site weighting, site intermediation centrality.
4. The method for evaluating the co-development of TOD station domains based on crowd-sourced geographical data according to claim 3, wherein the method comprises the following steps of:
in the step S2, the method for indicating the calibration right based on the entropy weight method comprises the following steps: for n sites, m indexes, then x ij A j index for an i site, where i=1, …, n; j=1, …, m;
normalizing the index to convert the absolute value of the index into a relative value, wherein the calculation formula is as follows
wherein ,x′ij Is the relative value of the index;
calculating the proportion p of the ith site value to the jth index ij ：
Calculating the entropy value e of the j-th index j ：
wherein ,
satisfy e j ≥0；
Calculating information entropy redundancy d j ：
d j ＝1-e j ，j＝1,…,m
Calculating the weight w of each index j ：
For each site, calculating each integrated score of the node dimension index, the site dimension index and the network dimension index
5. The method for evaluating the co-development of TOD station domains based on crowd-sourced geographical data according to claim 3, wherein the method comprises the following steps of:
land utilization mix is measured using shannon entropy:
wherein pik represents the proportion of k-th facilities in the site i, n represents the number of all facilities in the site i, and the larger the value is, the higher the land utilization diversity is; the smaller the value, the more single the land utilization; the formula is only valid for areas with more than one land use type, otherwise, 0 is returned, which means that the land use diversity is completely absent in the range of the station domain;
the station weighting degree represents the sum of weighted connections among stations in rail transit, represents the total amount of travel passenger flow among the station i and all adjacent stations, and has the following calculation formula:
wherein ,lpq (i) Representing the number of shortest paths between site p and site q through site i, l pq The shortest path length between the stations p and q is represented, in the information propagation network, the stations located at the places with higher intermediation centrality can effectively control information transfer to further influence the group, and in the traffic network, one station with higher intermediation centrality can be considered to be located at the junction transfer point in the track traffic network.
6. The method for evaluating the co-development of TOD (total organic light emitting diode) station domains based on crowd-sourced geographic data as claimed in claim 1, wherein the method comprises the following steps of:
in the step S3, the node-place-network model is decomposed into a node-network model, a node-place model and a place-network model, and the traffic and land utilization cooperativity of the rail site area is divided into balanced development, excessive development, low-level development and unbalanced development;
and clustering each dimension index of the node-place-network model by using an SOM clustering method so as to quickly obtain a clustering result.
7. The method for evaluating the co-development of TOD (total organic light emitting diode) station domains based on crowd-sourced geographic data as claimed in claim 1, wherein the method comprises the following steps of:
the calculation formula of the bearing pressure of the station passenger flow is as follows:
wherein T (-) represents the TOD score, which is the sum of the Index scores of the dimensions, and is comparable, represents the level of TOD development of the site, and Index (-) represents the score of a certain dimension.
8. TOD station domain collaborative development evaluation device based on crowd source geographic data, characterized by comprising:
the data collection and preprocessing module is used for data collection and preprocessing, and comprises collection and arrangement of various open source geographic big data, related public data, data format conversion and data unified expression;
the index construction module is used for index construction of the node-place model and the node-place-network model and comprises index selection, index transformation and index weighting based on an entropy weighting method;
the state evaluation module is used for evaluating the TOD station domain collaborative development state, quantitatively measuring the rail traffic and land utilization collaborative development state by using a clustering method, obtaining the bearing pressure of the TOD of the station domain according to the TOD conditions of the node-place model and the node-place-network model, evaluating the current passenger flow bearing level of the station, and finally comprehensively evaluating the TOD station domain collaborative development state.
9. TOD station domain collaborative development evaluation equipment based on crowd source geographic data is characterized in that: a memory comprising a processor and a computer program for storing a computer program capable of running on the processor, the processor being adapted to perform the steps of the co-development assessment method for TOD site domains based on crowd source geographical data as defined in any one of the preceding claims 1-7 when running the computer program.
10. A computer storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the TOD site domain collaborative development evaluation method based on crowd source geographical data according to any one of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116502960A (en) * | 2023-06-27 | 2023-07-28 | 北京城建交通设计研究院有限公司 | Station area TOD development potential evaluation method, system and electronic equipment |
| CN116738702A (en) * | 2023-06-05 | 2023-09-12 | 广州地铁设计研究院股份有限公司 | Multi-objective optimization method for land utilization structure of station area based on TOD mode |
| CN117273212A (en) * | 2023-09-17 | 2023-12-22 | 广州市交通规划研究院有限公司 | Site function grading optimization method based on passenger flow benefit and land value |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116738702A (en) * | 2023-06-05 | 2023-09-12 | 广州地铁设计研究院股份有限公司 | Multi-objective optimization method for land utilization structure of station area based on TOD mode |
| CN116502960A (en) * | 2023-06-27 | 2023-07-28 | 北京城建交通设计研究院有限公司 | Station area TOD development potential evaluation method, system and electronic equipment |
| CN116502960B (en) * | 2023-06-27 | 2023-09-26 | 北京城建交通设计研究院有限公司 | Station area TOD development potential evaluation method, system and electronic equipment |
| CN117273212A (en) * | 2023-09-17 | 2023-12-22 | 广州市交通规划研究院有限公司 | Site function grading optimization method based on passenger flow benefit and land value |
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