CN104794164A - Method for recognizing settlement parking spaces meeting social parking requirement on basis of open source data - Google Patents

Abstract

The invention discloses a method for recognizing settlement parking spaces meeting the social parking requirement on the basis of open source data. The method includes the following steps of firstly, mining and arranging research data; secondly, conducting address analysis on employment data through the Baidu LBS open platform; thirdly, conducting primary judgment, wherein spatial positioning is conducted on data of all types in a traffic zone base map through a GIS, and row coordinate correction and collection are conducted on the data; fourthly, conducting secondary judgment, wherein potential districts meeting the job-house matching requirement are obtained by screening spatial units with the job-house ratios higher than a preset value and spatial units with the employee number distribution densities higher than the whole-region average density; fifthly, recognizing settlement parking space distribution meeting the social parking requirement and the coverage of the settlement parking space distribution. The potential matching districts of cities are recognized through the open source data, and therefore settlement parking spaces can meet the social parking requirement, the social cost spent when an information issuing party searches potential target settlements is saved, and the space resource optimization in the staggered parking process is assisted.

Description

The method of social parking demand is mated based on parking stall, data identification settlement of increasing income

Technical field

The present invention relates to the research field of coupling of stopping, particularly a kind of method of mating social parking demand based on parking stall, data identification settlement of increasing income, saving the social cost of search potential target settlement, Information issued side.

Background technology

Under the ever-increasing background of car, the high density in city employment area is generally faced with the problem of parking stall deficiency in the daytime, and human Settlements daytime, relatively vacant parking stall then provided possibility for alleviating city parking problem.The subject matter that existing parking APP exists is the deficiency in parking space information source, the relation between supply and demand stopped in the daytime with employment district in potential target community cannot be resolved, make Information issued side be difficult to search for potential target settlement, constrain the possibility that parking stall timesharing is shared.

Along with the high speed development of information society, the parking coupling of employment-inhabitation is expected to be achieved by multiple technologies means.Wu Xiao (2014) is based on the urban employment spacial analytical method at Employment network visual angle and Employment network index system, construct one Employment network correlation model simply and intuitively, analyze urban employment space, this is for the invention provides suggestive thinking.Zhao Nan (2008) carries out the velocity amplitude in GPS traffic flow data sampling prediction section by Floating Car.Zhang Jianfeng (2014) proposes a kind of parking method and system of intelligent and high-efficiency rate based on cloud computing and large data.Ji Liju (2007), based on the multilevel city parking inducible system of wireless transmission, adopts GPRS cordless communication network, gathers parking position information of park in city, is guided intuitively by three grades of systems that release news to parking behavior.Ji Li (2013) studies Parking position querying method, and proposes a kind of real time inquiry system.In intelligent society, utilize large data to carry out Urban Traffic Planning Computer Aided Design and contribute to the feasible technical scheme of proposition science.But technique scheme ignores the search to potential settlement, parking space information source, do not solve the problem that parking resource makes full use of yet completely.

Because potential settlement all has certain probability to realize the supply of parking stall in the daytime, as long as therefore have abundant settlement just can ensure certain supply in high density area, and there is absolute parking demand in highdensity employment area, therefore, differentiated by the location of employment-inhabitation, can reduce the searching cost that Information issued sides such as being similar to APP identifies potential target settlement, what raising parking stall timesharing was shared realizes probability.

Summary of the invention

Fundamental purpose of the present invention is that the shortcoming overcoming prior art is with not enough, a kind of method of mating social parking demand based on parking stall, data identification settlement of increasing income is provided, potential coupling location, city is identified, social parking demand is mated with parking stall, settlement, save the social cost of search potential target settlement, Information issued side, final assisting realizes the space resources optimization of stopping of staggering the time.

In order to achieve the above object, the present invention is by the following technical solutions:

Mate the method for social parking demand based on parking stall, data identification settlement of increasing income, comprise the steps:

Excavation and the arrangement of S1, data: to increase income, network data combines as general data with business data, resident population's data, the network data being aided with community and parking facility captures;

S2, utilize Baidu LBS open platform to carry out address resolution to business data, and by GIS software by network data and enterprise, resident population's data are unified carries out space dropping place and coordinates correction, weighted stacking multidimensional data is with the potential coupling location of Comprehensive Assessment;

S3, once judgement: after the process of the collection and working base map that complete basic data, Various types of data is carried out space dropping place respectively by generalized information system in the base map of traffic zone, and coordinate correction is carried out to it and gathers;

S4, secondary judge: respectively by screening duty live than higher than the space cell of setting value and number of employees distribution density higher than the space cell of whole district's average density, obtain meeting the potentiality location that coupling demand is lived in duty;

S5, guiding parking area judge: select potential coupling location, obtain the related data in residential quarter and parking lot in region, then identify the distribution of parking stall, settlement and coverage thereof of mating social parking demand.

Preferably, in step S1, described in network data of increasing income comprise Sina's microblog data, described Sina microblog data is captured by python instrument, and concrete steps are:

S1.1.1, the public open platform account of application Sina, create and apply and obtain App Key and App Secret;

S1.1.2, Python instrument is utilized to calculate search microblogging dynamic beginning and ending time stamp;

S1.1.3, in the Config file of Python instrument, configuration hunting zone center point coordinate, the beginning and ending time stamp, search radius, preserve file;

S1.1.4, utilize IDLE tool to open weibo with pois file, run and obtain microblog data, after the data of acquisition are carried out coded format conversion, obtain temporal information, the geography information of microblogging.

Preferably, in step S2, the step of business data being carried out to address resolution and coordinates correction is:

S2.1.1, submit applications, obtain the api interface key of Baidu LBS open platform;

S2.1.2, the interface secret key obtained according to correlation parameter requirement and previous step, for URL request is write in address resolution, be converted to corresponding network address by enterprise address;

S2.1.3, the address resolution URL request network address write by enterprise's address information according to previous step, batch imports in Locoy Spider software, and after collection label task, text template are set, by Locoy Spider instrument, enterprise address is carried out to the address resolution of batch, thus get the geographic coordinate information of each enterprise.

Preferably, in step S2, to the concrete steps of resident population's data processing be:

S2.2.1, reject in each street non-constructive land after, by resident population's data of adding up in the unit of street divided by each street area, calculate the density of population in each street of institute's survey region;

The area of S2.2.2, computation partition each traffic zone space cell out;

S2.2.3, be multiplied with the street density of population residing for it by each traffic zone space cell area, estimation obtains resident population's quantity of each traffic zone.

Preferably, in step S2, the concrete steps based on the network data excavation of LBS open platform are:

The API key of S2.3.1, acquisition Baidu open platform;

S2.3.2, the URL request parameter of object search is set;

S2.3.3, the URL request parameter set opened in a browser, whether inspection URL request parameter is qualified, if the quantity of object search exceedes setting number, then needs again to reduce hunting zone;

S2.3.4, qualified for spelling URL batch to be imported in Locoy Spider software, arrange and gather label and text template, run and obtain related data.

Preferably, in step S3, the concrete grammar once judged as:

S3.1, Various types of data key element is carried out standardization

For the consideration that the dimension to Various types of data key element, the order of magnitude are different, the concrete grammar of standardization is as follows, if the quantity of jth item Data Elements in space cell i is α _ij, first define:

$e_{\mathrm{ij}}=({\mathrm{\α}}_{\mathrm{ij}}+1)/\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_{\mathrm{ij}}+1)---\left(1\right)$

Wherein, the n in formula (1) represents that studied city has n traffic zone unit, then i=1,2 ..., n, Data Elements j=1,2,3,4;

It should be noted that and work as e _ijwhen=0, ln (e _ij) meaningless, therefore at calculating standard value P _ijtime, need to e _ijrevise, thus obtain the standard value P of different key element in each unit of redefining _ij:

P _ij＝e _ij·ln(e _ij) (2)

S3.2, calculate each unit duty live than

After Various types of data is carried out standardization, further, respectively by microblogging day data and night data, enterprise's number of employees and resident population's data carry out duty and live coupling, thus the two class duties calculating each traffic zone unit are firmly than β _ij, calculation procedure is as follows:

or ${\mathrm{\β}}_{\mathrm{ij}}=\frac{Q_i}{R_i}---\left(3\right)$

I=1 in formula, 2 ..., m, j=1,2; In addition, W _{between i day}the microblogging day data of traffic zone unit i after expression standardization, W _{i night}the microblogging data at night of traffic zone unit i after expression standardization; Q _ienterprise's number of employees of traffic zone unit i after expression standardization, R _iresident population's data of traffic zone unit i after expression standardization;

S3.3, utilize average variance method to calculate objective weight that ratio is lived in all kinds of duty

First the mean value of two class key elements is calculated:

$Q_j=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\β}}_{\mathrm{ij}}}{m}---\left(4\right)$

Then, the standard deviation that ratio is lived in two class duties is calculated:

${\mathrm{\δ}}_j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{\β}}_{\mathrm{ij}}-Q_j)}^2}---\left(5\right)$

I=1 in formula, 2 ..., m, j=1,2;

Finally, the objective weight of two class key elements is calculated:

${\mathrm{\θ}}_j=\frac{{\mathrm{\δ}}_j}{{\mathrm{\Σ}}_{j=1}^2{\mathrm{\δ}}_j}---\left(6\right)$

S3.4, utilize Te Feierfa to calculate subjective weight that ratio is lived in all kinds of duty;

Live the data source accuracy of ratio according to two class duties and the degree of association is lived in duty, utilize Delphi to live than carrying out importance judge two class duties, obtain the subjective weight that ratio is lived in two class duties, computing formula is:

${\mathrm{\μ}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{K}_{\mathrm{ij}}}{n}---\left(7\right)$

I=1 in formula, 2 ..., n, j=1,2; Wherein K _ijbe that the weight that i-th people lives ratio for the duty of jth class is passed judgment on;

S3.5, calculate the comprehensive weight that ratio is lived in two class duties;

On the basis of the above, the average by calculating subjectivity and objectivity weight obtains the comprehensive weight relation of two class key elements:

${\mathrm{\ω}}_j=\frac{{\text{\θ}}_j+{\mathrm{\μ}}_j}{2}---\left(8\right)$

S3.6, calculate each unit comprehensive duty live than;

Two class duties are lived than according to its weight relationship, carry out comprehensive duty that comprehensive superposition obtains each space cell firmly than:

${\mathrm{\γ}}_i=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\β}}_{\mathrm{ij}}\·{\mathrm{\ω}}_j)---\left(9\right)$

I=1 in formula, 2 ..., m, j=1,2.

Preferably, in step S4, the concrete grammar that secondary judges as:

S4.1, live on the basis of ratio in the duty obtaining each space cell, by γ _ithe space cell of>=0.8 is considered as meeting the potentiality location that coupling demand is lived in duty;

S4.2, by calculating the number of employees distribution density (ρ of each unit in business data _i) with whole areal distribution density average (ρ), number of employees distribution density is identified as higher than the space cell of mean value and meets the potentiality location that coupling demand is lived in duty:

${\mathrm{\ρ}}_i=\frac{P_i}{S_i}---\left(10\right)$

$\mathrm{\ρ}=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\ρ}}_i}{m}---\left(11\right)$

I=1 in formula, 2 ..., m, in addition, P _ifor the number of employees of space cell, S _ifor the area of space cell;

S4.3, screening duty are firmly than γ _i>=0.8 and number of employees distribution density ρ _ithe space cell of>=ρ, thus the potentiality space cell obtaining coupling demand.

Preferably, in step S5, the concrete grammar that judges of guiding parking area as:

S5.1, Baidu open platform is utilized to obtain the related data in residential quarter and parking lot in region, two class data are carried out in generalized information system space dropping place and coordinate correction, and crossing with the residential estate in coupling parking demand unit obtain this unit parking facility and residential quarter distribute;

S5.2, to be filtered out by network inquiry that district is built-in to grow up on behalf of later community in 2000, and the corresponding parking lot filtering out such residential quarter, thus obtain the parking lot, residential quarter with coupling parking demand potentiality;

S5.3, respectively calculating meet 300 meters and 500 meters of service radiuses in the parking lot, residential quarter of coupling parking demand, obtain satisfactory I grade of potential Parking in residential area coupling location and II grade of potential Parking in residential area coupling location in district.

Preferably, before step S1, also comprise the step obtaining working base map, its concrete grammar is:

Obtain the administrative division border of institute's survey region, traffic network, land character, physical features border, CAD is utilized to draw the base map dividing traffic zone, and in GIS software, be converted to the base map file of shp form, whole region is divided, finally obtains the working base map behind institute's survey region division traffic zone.

Preferably, in step S4, described setting value is 0.8.

Compared with prior art, tool has the following advantages and beneficial effect in the present invention:

1, the present invention with the space cell of urban transportation community for working base map, spatially superimposed Sina microblogging in the daytime, the data at night and working demographic data, resident population's data of enterprise, calculate duty respectively and live matching relationship, and after calculating weight by average variance method and Delphi, Comprehensive Assessment is carried out to potential coupling location, identifies comprehensive duty and firmly firmly mix plot than the duty higher than 0.8.Further, the block of social parking demand higher than mean value is identified, thus screening obtains highdensity duty firmly potential coupling location, settlement is wherein screened, and carry out dropping place by building up age newer Parking in residential area field, realize the facility supply suggestion of stopping with parking stall, settlement coupling society.

2, the settlement in the potential coupling location of identification of the present invention, and live relation based on the stronger social parking demand in these locations and duty, propose the method that parking stall, settlement is mated social parking demand, saved the social cost of search potential target settlement, Information issued side, thus realize the resource distribution of parking facility.

Accompanying drawing explanation

Fig. 1 is specific implementation process flow diagram of the present invention;

Fig. 2 is division figure in traffic zone of the present invention;

Fig. 3 is that ratio distribution plan is lived in the duty that the present invention is based on data of increasing income;

Fig. 4 is the number of employees density profile that the present invention is based on business data;

Fig. 5 is the potentiality space cell distribution plan of the Parking in residential area coupling demand that the present invention is based on data of increasing income;

Fig. 6 is I grade and II grade of potential Parking in residential area coupling location distribution plan the present invention is based on data of increasing income.

Embodiment

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.

Embodiment

As shown in Figure 1, the present embodiment mates the method for social parking demand based on parking stall, data identification settlement of increasing income, and comprises the steps:

(1) preliminary work base map divide traffic zone;

The present invention for working base map, superposes the center line of present situation road network with the border in city (or region) in CAD, and divides traffic zone according to technical standard further.

With reference to the criteria for classifying of domestic and international traffic zone, general urban central zone traffic zone area is 1-3 square kilometre, the area of traffic zone, Urban Marginal Areas is then 5-15 square kilometre, in addition the density of population that Chinese city is higher and more complicated land character, in this research method, each block divides in conjunction with actual, the traffic zone area of local center built-up areas can get smaller value according to city road network, and urban periphal defence then generally gets higher value.In addition, division border, this patent traffic zone is boundary according to road axis, substantially be auxiliaryly carry out region restriction based on major trunk roads road network, some areas subsidiary road road network, consider the factor such as natural boundary and geological location simultaneously, and substantially follow the principle of not crossing over administrative line.

Obtain the administrative division border, traffic network, land character, physical features border etc. of institute's survey region, CAD is utilized to draw the base map dividing traffic zone, and in GIS software, be converted to the base map file of shp form, principle according to previous step divides whole region, finally obtains the working base map behind institute's survey region division traffic zone.

(2) excavation of data and arrangement;

This research method network data of increasing income combines as general data with business data, resident population's data, and the network data being aided with community and parking facility captures, and enriches Data Source.

In addition, utilize Baidu LBS open platform to carry out address resolution to business data, and by GIS software, network data and enterprise, the unification of resident population's data are carried out space dropping place and coordinates correction, weighted stacking multidimensional data is with the potential coupling location of Comprehensive Assessment.

1. Sina's microblogging text data is captured with python instrument;

The present invention is by utilizing microblogging api interface in Sina's development platform and geography information interface (http://open.weibo.com/wiki/2/place/nearby_timeline), obtain at the microblogging of a period of time inner region dynamic, comprising time, geography information, text message etc. that microblogging upgrades.By analyzing the spatial distribution characteristic round the clock of microblogging text, obtain the distribution situation that duty in scope is lived.Concrete operation step is:

(1) apply for the public open platform account of Sina, create and apply and obtain App Key and App Secret;

(2) the Python instrument utilizing author to write calculates the dynamic beginning and ending time stamp of search microblogging, needs specified otherwise, and by the restriction of microblogging api interface, the suggestion search microblogging of nearly about a week is dynamic;

(3) in the Config file of Python instrument, the parameter such as center point coordinate, beginning and ending time stamp, search radius (being not more than 11132 meters) of configuration hunting zone, preserves file;

(4) the weibo with pois file utilizing IDLE tool to open author to write, runs and obtains microblog data, obtain the temporal information, geography information etc. of microblogging after the data of acquisition are carried out coded format conversion; It should be noted that at this, the interface by Sina's microblogging limits, and each account can only obtain 1000 microblog data for one hour, within each hour, empties once.

2. address resolution is carried out to business data, arrange the number of employees data of enterprise;

According to the business directory of industrial and commercial bureau's registration statistics, filter out the business data of institute's survey region, wherein pay close attention to the number of employees one item number certificate of each enterprise.

The address resolution of business data, based on the Geocoding API function in Baidu's open platform, can get the geographic coordinate information of each enterprise.Because the coordinate information obtained from Baidu's LBS open platform exists certain error, in late time data process and space dropping place, need to carry out certain screening and rectification work to geographic coordinate.The concrete steps of enterprise's address resolution and coordinate correction are as follows:

(1) submit applications, obtains the api interface key of Baidu LBS open platform;

(2) according to the interface secret key that correlation parameter requires and previous step obtains, for URL request is write in address resolution, enterprise address is converted to corresponding network address;

(3) according to the address resolution URL request network address that previous step is write by enterprise's address information, batch imports in Locoy Spider software, and after collection label task, text template etc. are set, by Locoy Spider instrument, enterprise address is carried out to the address resolution of batch, thus get the geographic coordinate information of each enterprise.

3. resident population's data are arranged;

The statistics of whole nation resident population is often that space cell is added up with street, the traffic zone space cell then divided to satisfy the criteria in this method is as working base map, and the principle avoiding the Administrative boundaries boundary line crossing over street is followed in the division of traffic zone substantially.Therefore, after getting rid of the natural farmland in street, massif, resident population's data regarded as approx and be uniformly distributed in each street unit, with resident population's quantity of each traffic zone in the density of population of dividing equally estimation institute survey region, concrete steps are as follows:

(1) after rejecting the non-constructive land such as natural farmland, massif in each street, by resident population's data of adding up in the unit of street divided by each street area, the density of population in each street of institute's survey region is calculated;

(2) area of computation partition each traffic zone space cell out;

(3) be multiplied with the street density of population residing for it by each traffic zone space cell area, estimation obtains resident population's quantity of each traffic zone.

4. based on the network data excavation of LBS open platform;

The present invention utilizes Baidu's open platform, obtain the geography information in " parking lot " and " community " in certain limit, age distribution situation is built up, the distribution of synthetic determination guiding parking settlement by the space distribution of the residential quarter and parking lot, community of screening and analyze potential coupling location and residential quarter.Network data excavation step wherein based on LBS open platform is as follows:

(1) the API key of Baidu's open platform is obtained;

(2) the URL request parameter of object search is set, mainly comprises search key, object search title (parking lot and community), hunting zone (latitude and longitude coordinates of rectangular extent) etc.;

(3) the URL request parameter set opened in a browser, whether inspection URL request parameter is qualified, if the quantity of object search is more than 760, then needs again to reduce hunting zone;

(4) URL qualified for spelling batch is imported in Locoy Spider software, arrange and gather label and text template, run and obtain related data.The main information of data comprises facility name, latitude and longitude coordinates, facility address etc.

(3) potential coupling location judges

1. once judge

After completing the collection of basic data and the process of working base map again, Various types of data is carried out space dropping place respectively by generalized information system in the base map of traffic zone, and coordinate correction is carried out to it and gathers.According to microblog data, business data and resident population's data, the microblogging day data of 08:00-18:00 and enterprise's number of employees are classified as employed population distributed data, the microblogging data at night of 18:00-08:00 and census data are classified as the distribution of resident population, ratio distribution situation is lived in the comprehensive duty utilizing average variance method and special fell's method to calculate each unit, thus completes and judge the first time in potential coupling location.Concrete steps are as follows:

(1) Various types of data key element is carried out standardization

For the consideration that the dimension to Various types of data key element, the order of magnitude are different.The concrete grammar of standardization is as follows, if the quantity of jth item Data Elements in space cell i is α _ij, first define:

$e_{\mathrm{ij}}=({\mathrm{\α}}_{\mathrm{ij}}+1)/\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_{\mathrm{ij}}+1)---\left(1\right)$

Wherein, the n in formula (1) represents that studied city (or region) has n traffic zone unit, then i=1,2 ..., n, Data Elements j=1,2,3,4.

It should be noted that and work as e _ijwhen=0, ln (e _ij) meaningless, therefore at calculating standard value P _ijtime, need to e _ijrevise, thus obtain the standard value P of different key element in each unit of redefining _ij:

P _ij＝e _ij·ln(e _ij) (2)

(2) calculate each unit duty live than

After Various types of data is carried out standardization, further, respectively by microblogging day data and night data, enterprise's number of employees and resident population's data carry out duty and live coupling, thus the two class duties calculating each traffic zone unit are firmly than β _ij, calculation procedure is as follows:

or ${\mathrm{\β}}_{\mathrm{ij}}=\frac{Q_i}{R_i}---\left(3\right)$

I=1 in formula, 2 ..., m, j=1,2.In addition, W _{between i day}the microblogging day data of traffic zone unit i after expression standardization, W _{i night}the microblogging data at night of traffic zone unit i after expression standardization; Q _ienterprise's number of employees of traffic zone unit i after expression standardization, R _iresident population's data of traffic zone unit i after expression standardization.

(3) utilize average variance method to calculate objective weight that ratio is lived in all kinds of duty

First the mean value of two class key elements is calculated:

$Q_j=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\β}}_{\mathrm{ij}}}{m}---\left(4\right)$

Then, the standard deviation that ratio is lived in two class duties is calculated:

${\mathrm{\δ}}_j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{\β}}_{\mathrm{ij}}-Q_j)}^2}---\left(5\right)$

I=1 in formula, 2 ..., m, j=1,2.

Finally, the objective weight of two class key elements is calculated:

${\mathrm{\θ}}_j=\frac{{\mathrm{\δ}}_j}{{\mathrm{\Σ}}_{j=1}^2{\mathrm{\δ}}_j}---\left(6\right)$

(4) utilize Te Feierfa to calculate subjective weight that ratio is lived in all kinds of duty.

Live the data source accuracy of ratio according to two class duties and the degree of association is lived in duty, utilize Delphi to live than carrying out importance judge two class duties, obtain the subjective weight that ratio is lived in two class duties, computing formula is:

${\mathrm{\μ}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{K}_{\mathrm{ij}}}{n}---\left(7\right)$

I=1 in formula, 2 ..., n, j=1,2.Wherein K _ijbe that the weight that i-th people lives ratio for the duty of jth class is passed judgment on.

(5) comprehensive weight that ratio is lived in two class duties is calculated.

On the basis of the above, the average by calculating subjectivity and objectivity weight obtains the comprehensive weight relation of two class key elements:

${\mathrm{\ω}}_j=\frac{{\text{\θ}}_j+{\mathrm{\μ}}_j}{2}---\left(8\right)$

(6) calculate each unit comprehensive duty live than.

Two class duties are lived than according to its weight relationship, carry out comprehensive duty that comprehensive superposition obtains each space cell firmly than:

${\mathrm{\γ}}_i=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\β}}_{\mathrm{ij}}\·{\mathrm{\ω}}_j)---\left(9\right)$

I=1 in formula, 2 ..., m, j=1,2.

2. secondary judges

Respectively by screening duty live than higher than 0.8 space cell and number of employees distribution density higher than the space cell of whole district's average density, obtain meeting the potentiality location that coupling demand is lived in duty.Concrete steps are as follows:

First, live on the basis of ratio in the duty obtaining each space cell, by γ _ithe space cell of>=0.8 is considered as meeting the potentiality location that coupling demand is lived in duty.

Then, by calculating the number of employees distribution density (ρ of each unit in business data _i) with whole areal distribution density average (ρ), number of employees distribution density is identified as higher than the space cell of mean value and meets the potentiality location that coupling demand is lived in duty:

${\mathrm{\ρ}}_i=\frac{P_i}{S_i}---\left(10\right)$

$\mathrm{\ρ}=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\ρ}}_i}{m}---\left(11\right)$

I=1 in formula, 2 ..., m.In addition, P _ifor the number of employees of space cell, S _ifor the area of space cell.

Finally, duty is screened firmly than γ _i>=0.8 and number of employees distribution density ρ _ithe space cell of>=ρ, thus the potentiality space cell obtaining coupling demand.

(4) guiding parking settlement judges

On this basis, Baidu's open platform is utilized to obtain the related data in residential quarter and parking lot in region, two class data are carried out in generalized information system space dropping place and coordinate correction, and crossing with the residential estate in coupling parking demand unit obtain this unit parking facility and residential quarter distribute.

Then, filtered out by network inquiry that district is built-in to grow up on behalf of later community in 2000, and the corresponding parking lot filtering out such residential quarter, thus obtain the parking lot, residential quarter with coupling parking demand potentiality.

Finally, calculate 300 meters and 500 meters of service radiuses in the parking lot, residential quarter meeting coupling parking demand respectively, obtain satisfactory I grade of potential Parking in residential area coupling location and II grade of potential Parking in residential area coupling location in district.

(5) example operation

It is example operation scope that the present invention have chosen reported in Tianhe district of Guangzhou, by analyzing business data, resident population's data and the microblogging text data etc. in Tianhe District, utilize mean square deviation, the data in special fell's method and relevant residential quarter and parking lot thereof obtain Parking in residential area coupling location potential within the scope of Tianhe District, whole process comprises judgement three parts of the preparation of basic data, the judgement in potential coupling location and guiding parking settlement.

5.1, basic data prepares;

According to present situation road net and the Administrative boundaries of Tianhe District, according to the general division principle of traffic zone, Tianhe District is divided into 81 space cells, as the working base map that follow-up study is analyzed.It should be noted that at this, due to the parking demand that the present invention mainly studies employment and lives, so in district the non-constructive land such as stove mountain, South China Botanical Garden not in this research range.

Obtain 46217 business data within the scope of Tianhe District by screening, utilize LBS open platform to carry out address resolution to it, data are carried out in generalized information system space dropping place and coordinate correction.

By the ratio of the quantity and each street area that calculate each street resident population, obtain resident population's distribution density in each street, then obtain resident population's quantity of each space cell according to resident population's distribution density of each space cell.It should be noted that at this, consider the situation of population distribution inequality, non-constructive land not being taken into account when calculating resident population's distribution density.

Utilizing Python instrument, by searching for the microblogging text data obtained within the scope of Tianhe District continuously, obtaining 107859 microblogging text datas in Tianhe District, and in generalized information system, carry out space dropping place and coordinate correction.The microblogging day data of 08:00-18:00 and enterprise's number of employees are classified as employed population distributed data, the microblogging data at night of 18:00-08:00 and census data are classified as the distribution of resident population.

5.2, the judgement in potential coupling location;

On this basis, utilize formula (1) and formula (2) that the microblog data in Tianhe District, business data and resident population's data are carried out standardization respectively, recycling formula (3) can obtain Tianhe District based on the duty of microblog data live than and based on enterprise, resident population's data duty firmly than.

Then, utilize average variance method, calculate according to formula (4), formula (5) and formula (6) objective weight that ratio is lived in two class duties.Utilize special fell's method, can be calculated according to formula (7) the subjective weight that ratio is lived in two class duties, utilize formula (8) to carry out the superposition of weight, finally obtain the comprehensive weight relation that ratio is lived in two class duties: ω ₁=0.733 and ω ₂=0.267.

On this basis, utilize formula (9) two class duties firmly to be superposed than according to comprehensive weight relation, thus obtain Tianhe District duty firmly than distribution plan (as shown in Figure 2).

According to the number of employees distribution situation of business data in each space cell and the area of each space cell, ratio both calculating according to formula (10) obtains the Density Distribution (as shown in Figure 3) of number of employees, and the space cell of unit number of employees density higher than the average number of employees density of the whole district is screened, as the potential coupling location that employed population is intensive.

In addition, live in the distributed basis of ratio obtaining Tianhe District duty, duty is firmly screened than the space cell being greater than 0.8, carry out superimposed with number of employees density higher than the space cell of mean value, finally obtain in Tianhe District the potentiality space cell (as shown in Figure 4) meeting Parking in residential area coupling demand.

5.3, the judgement of guiding parking settlement;

After the potentiality space cell obtaining meeting in Tianhe District Parking in residential area coupling demand, the residential estate in unit is screened, as the analyst coverage analyzing parking settlement.In addition, Baidu open platform is utilized to obtain the distribution situation in residential quarter within the scope of Tianhe District and parking lot, residential quarter, the community that in residential estate in potentiality space cell 2000 year are built up later is screened, make 300 meters of service radiuses and 500 meters of service radiuses of such community parking field respectively, finally obtain potential coupling settlement, I grade, Tianhe District and II grade of potential coupling settlement (as shown in Figure 5, Figure 6).

Above-described embodiment is the present invention's preferably embodiment; but embodiments of the present invention are not restricted to the described embodiments; change, the modification done under other any does not deviate from Spirit Essence of the present invention and principle, substitute, combine, simplify; all should be the substitute mode of equivalence, be included within protection scope of the present invention.

Claims (10)

1. mate the method for social parking demand based on parking stall, data identification settlement of increasing income, it is characterized in that, comprise the steps:

Excavation and the arrangement of S1, data: to increase income, network data combines as general data with business data, resident population's data, the network data being aided with community and parking facility captures;

S2, utilize Baidu LBS open platform to carry out address resolution to business data, and by GIS software by network data and enterprise, resident population's data are unified carries out space dropping place and coordinates correction, weighted stacking multidimensional data is with the potential coupling location of Comprehensive Assessment;

S3, once judgement: after the process of the collection and working base map that complete basic data, Various types of data is carried out space dropping place respectively by generalized information system in the base map of traffic zone, and coordinate correction is carried out to it and gathers;

S4, secondary judge: respectively by screening duty live than higher than the space cell of setting value and number of employees distribution density higher than the space cell of whole district's average density, obtain meeting the potentiality location that coupling demand is lived in duty;

S5, guiding parking area judge: select potential coupling location, obtain the related data in residential quarter and parking lot in region, then identify the distribution of parking stall, settlement and coverage thereof of mating social parking demand.

2. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, it is characterized in that, in step S1, described in network data of increasing income comprise Sina's microblog data, described Sina microblog data is captured by python instrument, and concrete steps are:

S1.1.1, the public open platform account of application Sina, create and apply and obtain App Key and App Secret;

S1.1.2, Python instrument is utilized to calculate search microblogging dynamic beginning and ending time stamp;

S1.1.3, in the Config file of Python instrument, configuration hunting zone center point coordinate, the beginning and ending time stamp, search radius, preserve file;

S1.1.4, utilize IDLE tool to open weibo with pois file, run and obtain microblog data, after the data of acquisition are carried out coded format conversion, obtain temporal information, the geography information of microblogging.

3. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S2, the step of business data being carried out to address resolution and coordinates correction is:

S2.1.1, submit applications, obtain the api interface key of Baidu LBS open platform;

S2.1.2, the interface secret key obtained according to correlation parameter requirement and previous step, for URL request is write in address resolution, be converted to corresponding network address by enterprise address;

S2.1.3, the address resolution URL request network address write by enterprise's address information according to previous step, batch imports in Locoy Spider software, and after collection label task, text template are set, by Locoy Spider instrument, enterprise address is carried out to the address resolution of batch, thus get the geographic coordinate information of each enterprise.

4. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S2, to the concrete steps of resident population's data processing is:

S2.2.1, reject in each street non-constructive land after, by resident population's data of adding up in the unit of street divided by each street area, calculate the density of population in each street of institute's survey region;

The area of S2.2.2, computation partition each traffic zone space cell out;

S2.2.3, be multiplied with the street density of population residing for it by each traffic zone space cell area, estimation obtains resident population's quantity of each traffic zone.

5. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S2, the concrete steps based on the network data excavation of LBS open platform are:

The API key of S2.3.1, acquisition Baidu open platform;

S2.3.2, the URL request parameter of object search is set;

S2.3.3, the URL request parameter set opened in a browser, whether inspection URL request parameter is qualified, if the quantity of object search exceedes setting number, then needs again to reduce hunting zone;

S2.3.4, qualified for spelling URL batch to be imported in Locoy Spider software, arrange and gather label and text template, run and obtain related data.

6. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S3, the concrete grammar once judged as:

S3.1, Various types of data key element is carried out standardization

For the consideration that the dimension to Various types of data key element, the order of magnitude are different, the concrete grammar of standardization is as follows, if the quantity of jth item Data Elements in space cell i is α _ij, first define:

$e_{\mathrm{ij}}=({\mathrm{\α}}_{\mathrm{ij}}+1)/\underset{i=1}{\overset{n}{\mathrm{\Σ}}}({\mathrm{\α}}_{\mathrm{ij}}+1)---\left(1\right)$

Wherein, the n in formula (1) represents that studied city has n traffic zone unit, then i=1,2 ..., n, Data Elements j=1,2,3,4;

It should be noted that and work as e _ijwhen=0, ln (e _ij) meaningless, therefore at calculating standard value P _ijtime, need to e _ijrevise, thus obtain the standard value P of different key element in each unit of redefining _ij:

P _ij＝e _ij·ln(e _ij) (2)

S3.2, calculate each unit duty live than

After Various types of data is carried out standardization, further, respectively by microblogging day data and night data, enterprise's number of employees and resident population's data carry out duty and live coupling, thus the two class duties calculating each traffic zone unit are firmly than β _ij, calculation procedure is as follows:

I=1 in formula, 2 ..., m, j=1,2; In addition, W _{between i day}the microblogging day data of traffic zone unit i after expression standardization, W _{i night}the microblogging data at night of traffic zone unit i after expression standardization; Q _ienterprise's number of employees of traffic zone unit i after expression standardization, R _iresident population's data of traffic zone unit i after expression standardization;

S3.3, utilize average variance method to calculate objective weight that ratio is lived in all kinds of duty

First the mean value of two class key elements is calculated:

$Q_j=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\β}}_{\mathrm{ij}}}{m}---\left(4\right)$

Then, the standard deviation that ratio is lived in two class duties is calculated:

${\mathrm{\δ}}_j=\sqrt{\frac{1}{m}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{({\mathrm{\β}}_{\mathrm{ij}}-Q_j)}^2}---\left(5\right)$

I=1 in formula, 2 ..., m, j=1,2;

Finally, the objective weight of two class key elements is calculated:

${\mathrm{\θ}}_j=\frac{{\mathrm{\δ}}_j}{{\mathrm{\Σ}}_{j=1}^2{\mathrm{\δ}}_j}---\left(6\right)$

S3.4, utilize Te Feierfa to calculate subjective weight that ratio is lived in all kinds of duty;

Live the data source accuracy of ratio according to two class duties and the degree of association is lived in duty, utilize Delphi to live than carrying out importance judge two class duties, obtain the subjective weight that ratio is lived in two class duties, computing formula is:

${\mathrm{\μ}}_j=\frac{{\mathrm{\Σ}}_{i=1}^n{K}_{\mathrm{ij}}}{n}---\left(7\right)$

I=1 in formula, 2 ..., n, j=1,2; Wherein K _ijbe that the weight that i-th people lives ratio for the duty of jth class is passed judgment on;

S3.5, calculate the comprehensive weight that ratio is lived in two class duties;

On the basis of the above, the average by calculating subjectivity and objectivity weight obtains the comprehensive weight relation of two class key elements:

${\mathrm{\ω}}_j=\frac{{\mathrm{\θ}}_j+{\mathrm{\μ}}_j}{2}---\left(8\right)$

S3.6, calculate each unit comprehensive duty live than;

Two class duties are lived than according to its weight relationship, carry out comprehensive duty that comprehensive superposition obtains each space cell firmly than:

${\mathrm{\γ}}_i=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}({\mathrm{\β}}_{\mathrm{ij}}\·{\mathrm{\ω}}_j)---\left(9\right)$

I=1 in formula, 2 ..., m, j=1,2.

7. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S4, the concrete grammar that secondary judges as:

S4.1, live on the basis of ratio in the duty obtaining each space cell, by γ _ithe space cell of>=0.8 is considered as meeting the potentiality location that coupling demand is lived in duty;

S4.2, by calculating the number of employees distribution density (ρ of each unit in business data _i) with whole areal distribution density average (ρ), number of employees distribution density is identified as higher than the space cell of mean value and meets the potentiality location that coupling demand is lived in duty:

${\mathrm{\ρ}}_i=\frac{P_i}{S_i}---\left(10\right)$

$\mathrm{\ρ}=\frac{{\mathrm{\Σ}}_{i=1}^m{\mathrm{\ρ}}_i}{m}---\left(11\right)$

I=1 in formula, 2 ..., m, in addition, P _ifor the number of employees of space cell, S _ifor the area of space cell;

S4.3, screening duty are firmly than γ _i>=0.8 and number of employees distribution density ρ _ithe space cell of>=ρ, thus the potentiality space cell obtaining coupling demand.

8. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, in step S5, the concrete grammar that judges of guiding parking area as:

S5.1, Baidu open platform is utilized to obtain the related data in residential quarter and parking lot in region, two class data are carried out in generalized information system space dropping place and coordinate correction, and crossing with the residential estate in coupling parking demand unit obtain this unit parking facility and residential quarter distribute;

S5.2, to be filtered out by network inquiry that district is built-in to grow up on behalf of later community in 2000, and the corresponding parking lot filtering out such residential quarter, thus obtain the parking lot, residential quarter with coupling parking demand potentiality;

S5.3, respectively calculating meet 300 meters and 500 meters of service radiuses in the parking lot, residential quarter of coupling parking demand, obtain satisfactory I grade of potential Parking in residential area coupling location and II grade of potential Parking in residential area coupling location in district.

9. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, before step S1, also comprise the step obtaining working base map, its concrete grammar is:

Obtain the administrative division border of institute's survey region, traffic network, land character, physical features border, CAD is utilized to draw the base map dividing traffic zone, and in GIS software, be converted to the base map file of shp form, whole region is divided, finally obtains the working base map behind institute's survey region division traffic zone.

10. method of mating social parking demand based on parking stall, data identification settlement of increasing income according to claim 1, is characterized in that, in step S4, described setting value is 0.8.