CN117392868A - Urban parking partition method and system based on multi-source big data - Google Patents
Urban parking partition method and system based on multi-source big data Download PDFInfo
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Abstract
The invention provides a city parking partition method and a partition system based on multi-source big data, which comprises the steps of firstly determining a research subject and partition objects of a parking partition, and counting various data sets on a traffic district; secondly, identifying effective data feature dimensions through correlation analysis, and completing data dimension screening; then adopting a weighted improved K-means clustering algorithm to identify clusters to which traffic cells belong, and adopting a condensation clustering algorithm to combine similar clusters; and finally constructing a parking partition by utilizing a road network and an administrative boundary, and mapping the clustering attribute of the traffic district on the parking partition, thereby obtaining the parking partition with partition attribute and strong practical operability. The partition method and the partition system can solve the urban parking partition under various application scenes, are high in scientificity, rationality, solving efficiency and feasibility, and can provide technical support for differentiated management of the urban parking partition.
Description
Technical Field
The invention relates to the technical field of urban traffic and parking management, in particular to an urban parking partition method and system based on multi-source big data.
Background
1. Urban parking partition study overview
Urban traffic is an important foundation for efficient development of social activities, along with continuous promotion of urban progress in China, more and more oversized cities are emerging, the buses in the cities have high travel sharing rate, the contradiction between parking supply and demand is outstanding, and the problem of parking difficulty is increasingly serious.
Different areas of a large city have great differences in social economy, population vitality, land utilization, traffic facilities and operation, urban traffic and parking operation characteristics of different areas are mined and classified, and the urban parking is managed in a refined and differentiated manner by matching with proper management policies and measures, so that the urban parking is an effective method for improving the urban parking difficulty.
The main research thought of the urban parking partition is to excavate the characteristic difference of the partition object in the aspects of urban traffic and parking operation from the multi-source big data related to the urban parking, thereby identifying the cluster to which the partition object belongs and forming the urban parking partition. The method can be applied to the aspects of parking allocation index partition, parking charging partition, parking treatment partition and the like, and has wide and urgent application prospect.
2. Urban parking partition research current situation
The existing urban parking partition research mainly adopts the following three methods, namely a qualitative analysis method, a quantitative analysis method and a multi-level partition method.
(1) Qualitative analysis method
In the initial research stage, due to the large data acquisition difficulty, in order to facilitate practical operation and management, a manager considers the integral supply and demand characteristics of urban parking at the administrative division level, and tends to define the division step by step from the urban center. Taking Beijing city parking allocation index partition as an example, a manager divides Beijing into four parking partitions by taking two loops, four loops and five loops as boundaries, and a differentiated parking allocation policy is adopted.
(2) Quantitative analysis method
With the development of technology, a manager grasps the data related to urban parking step by step, usually takes urban traffic cells or urban matrix grids as basic units, and quantitatively analyzes traffic attributes of the basic units of the city by adopting methods such as a fuzzy clustering method, a multi-index weighted evaluation method, a nuclear density analysis method and the like, so that parking areas are defined according to scores of the basic units.
(3) Multi-level partitioning method
Because urban parking is different in planning, construction, operation and management characteristics of three microscopic space dimensions of a city macroscopic layer, a regional layer and a regional layer, part of researchers propose that urban parking subareas are correspondingly provided with a hierarchical system, so that parking construction and management are guided on different layers, the macroscopic layer is mainly responsible for urban parking strategy subareas, the mesoscopic layer is mainly responsible for regional parking planning subareas, and the microscopic layer is mainly responsible for specific parking lot implementation subareas of local road sections and plots.
3. Problems with existing urban parking partitions
The traditional urban parking partition has the problems of rough partition, fewer considerations, weak operability and the like:
firstly, in order to ensure operability, the traditional urban parking partition method is generally divided by combining administrative area boundaries, and has lower scientificity and precision;
secondly, the urban parking data dimension is single, the data size is small, and multidimensional depth analysis of urban parking characteristics is lacking due to the limitation of an early data acquisition technology and computer performance;
thirdly, the traditional urban parking partition method is difficult to process massive parking big data generated in cities with high popularization of traffic informatization equipment in the Internet era;
fourth, the traditional urban parking partition method is difficult to quickly construct the urban parking partition boundary with strong operability and convenient management in a short period.
4、Technical problem to be solved
The technical difficulties for solving the problems are as follows:
1) Under the background of large coupling and rapid increase of data magnitude of the urban parking system, most of domestic cities are still in a rapid development stage, and the iteration of urban parking partitions is extremely rapid, so that a set of method and system for efficiently and rapidly analyzing the urban parking partitions are lacked;
2) Under the background that urban parking is digitalized and intelligent, sensors and equipment for collecting data are increasingly popular, the data dimension for analysis is increased, and the validity of the data needs to be clarified;
3) Parking subareas of macroscopic dimensions based on administrative division are difficult to realize subarea differentiation management; on the basis of medium-microscopic traffic communities, the parking system is difficult to quickly convert into parking areas with strong operability through standardization measures.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a multi-source big data-based urban parking partition method and a multi-source big data-based urban parking partition system. Firstly, multi-source big data related to urban parking and the border of a traffic cell of the city are imported, and various data sets are counted on the traffic cell; secondly, identifying effective data feature dimensions through correlation analysis, and completing data dimension screening; then adopting a weighted improved K-means clustering algorithm to identify clusters to which traffic cells belong, and adopting a condensation clustering algorithm to combine similar clusters, so that the number of the clusters accords with industry specifications or related researches; and finally, constructing a parking partition by utilizing elements such as road network, administrative boundaries and the like, and mapping the clustering attribute of the traffic district on the parking partition, thereby obtaining the parking partition with partition attribute and strong operability. The partition method and the partition system can solve urban parking partitions of various application scenes, and are high in scientificity, rationality, solving efficiency and feasibility.
The technical scheme of the invention is as follows: a city parking partition method based on multi-source big data mainly comprises the following steps:
1) Data importing;
2) A dataset meter;
2.1 Completing cleaning of abnormal values, missing values and repeated values of the data set;
2.2 Counting the big data set corresponding to the main influencing factors of the partition object on each traffic cell;
3) Dimension screening
3.1 Calculating a correlation coefficient matrix among all influence factors, determining the correlation degree among the influence factors of the partition object, and identifying the influence degree of all influence factors on the partition result;
3.2 After the calculation of the correlation coefficient of the influence factors is completed, the influence factors with the absolute value of the correlation coefficient smaller than 0.5 are reserved and used as effective influence factors, and the total number M of the influence factors after screening is updated;
4) Traffic cell clustering
4.1 Traffic cell cluster): introducing an influence factor weight, and constructing an optimal clustering objective function of the traffic cell as follows:
wherein k is the cluster center of the cluster, u mn The m-dimensional influence factor value of the traffic cell n, c mk M-dimensional influence factor value representing cluster center k, w j The weight of the m-dimensional influence factors;
4.2 Calculating the optimal clustering objective function value of each situation in the k E [3,15] range by adopting an improved weighted k-means algorithm, and judging the optimal k value by utilizing an elbow method to obtain the preliminary clustering of the traffic cells under the optimal clustering condition;
4.3 Aggregation and clustering: reducing the number of clusters to the number of clusters specified by industry practice or specification to which the partition object belongs; calculating the average value of each influence factor attribute of each preliminary cluster, obtaining a tree diagram of the condensed clusters by utilizing the condensed clustering function of the SPSS, judging a clustering result after the number of clusters is reduced, and updating the final cluster of each traffic cell;
5) Constructing a parking partition network;
6) And (5) clustering and mapping.
Preferably, 1) the data import comprises:
1.1 Determining a research main body and a partition object of the parking partition, and importing a big data set corresponding to a main influence factor of the partition object into a partition system;
1.2 A traffic cell boundary within the scope of the study is imported into the zoning system.
Preferably, 2.2) counting the big data set corresponding to the main influencing factors of the partition object onto each traffic cell, specifically including:
the traffic cells of the designed city are represented by the symbol N, and the total number of the traffic cells is N, and the area of the traffic cells is s n The method comprises the steps of carrying out a first treatment on the surface of the The point elements of a large dataset related to urban parking are denoted by the symbol i,the total number is I; the surface elements are represented by J, the total of which is J, and the area of the surface elements is denoted by a j The method comprises the steps of carrying out a first treatment on the surface of the The influencing factors are represented by a symbol M, M factors are added in total, and a decision variable r for judging whether the point-plane elements intersect with the traffic cell or not is introduced in ,r jn The set of point and face elements is formulated as:
U m ＝(u m1 ,...,u mn ) T
wherein u is mn For the m-dimensional influence factor value of the traffic cell n, U m Column vector v for mth dimension influence factor of all traffic cells mi An m-dimensional influence factor value, v, for the i-th point element mj An m-dimensional influence factor value, a, for the jth face element jn The area where the jth face element intersects the traffic cell n.
Preferably, in 3.1), M is a set of influencing factors, and assuming that a, b e M, a is not equal to b, a calculation formula of a correlation coefficient between any two influencing factors is:
wherein u is an For the a-dimensional influence factor value of the traffic cell n,for the mean value of the a-dimensional influence factors, u bn B-dimensional influence factor value for traffic cell n,/->Is the mean value of the b-dimensional influence factors.
Preferably, the 5) parking partition network configuration specifically includes:
5.1 Selecting linear elements with high management correlation with partition objects, including administrative area boundaries, municipal road networks, river water systems and the like, introducing arcgis and merging the arcgis into the same layer;
5.2 Constructing topology network inspection problem nodes in arcgis and processing; loading a topology toolbar, and converting the parking partition initial network into a topology network according to an adding rule that no suspension points and no pseudo nodes exist on the basis of the parking partition initial network;
5.3 And (3) starting topology rule diagnosis on the topology network, inquiring out hanging points and pseudo nodes, and overlapping an actual road network diagram to perform trimming, extending and splicing correction operation to obtain a parking partition network consisting of a closed polygonal network.
Preferably, the 6) cluster mapping specifically includes:
6.1 Loading traffic cell boundaries with final clustering properties in acrgis, and carrying out intersection operation of two layers with the corrected parking partition network, and filling traffic cell partitions into each parking partition;
6.2 Calculating the clustering attribute of each parking partition, taking the contained decomposed traffic cells as a calculation unit and taking the area as a weight, calculating the weighted average clustering attribute of the traffic cells and rounding, wherein the clustering mapping formula is as follows:
wherein, the parking partition is represented by the symbol L, and L and r are counted in total nl A is a decision variable of whether a parking partition and a traffic district are intersected or not nl The area where the traffic zone n intersects with the parking partition l; clu n For the final cluster attribute of parking partition l, A l The urban parking partition is completed so far for the area of the parking partition l.
A parking partition system for realizing the urban parking partition method based on multi-source big data mainly comprises:
and a data importing module: implementing the step 1);
a data set counting module: implementing the step 2);
dimension screening module: implementing the step 3);
traffic cell clustering module: implementing the step 4);
parking partition network construction module: implementing the step 5);
and a cluster mapping module: implementing the step 6).
The urban parking partition system terminal based on the multi-source big data comprises a memory, a processor and at least one instruction or at least one section of computer program which is stored in the memory and can be loaded and operated on the processor, wherein the processor loads and operates the at least one instruction or the at least one section of computer program to realize the urban parking partition method based on the multi-source big data.
A non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of urban parking partitioning based on multisource big data.
Compared with the prior art, the invention has the following advantages:
1) The invention provides a urban parking partition method and a partition system based on big data, which can realize urban parking partition based on the big data of urban parking under the scale on the basis of acquiring the big data related to urban traffic cells and the urban parking.
2) According to the invention, through correlation analysis, effective data analysis dimension is identified, and invalid or high-correlation data interference is avoided.
3) The invention constructs the partition network by utilizing the existing linear elements such as road network, administrative boundary and the like, maps the traffic cell clustering attribute which cannot be directly applied to actual management to the partition network, and ensures the high operability of urban parking partitions.
The method efficiently and accurately constructs the urban parking partition with strong practical operability, and partition results can be quickly changed along with the bottom data so as to cope with urban changes of high-speed development, so that technical support is provided for the partition differentiation management of urban traffic and urban parking, planning management and decision making are assisted, and the scientificity of urban traffic management is improved.
Drawings
FIG. 1 is a schematic diagram of an urban parking partition system based on multi-source big data;
FIG. 2 is a schematic flow chart of a method for urban parking partition based on multi-source big data;
FIG. 3 is a flow chart of traffic cell clustering;
FIG. 4 is a schematic diagram of a corrected hanging point and a pseudo node;
FIG. 5 is a schematic diagram of a traffic zone and parking partition superposition;
FIG. 6 is a diagram of a large dataset calculation;
FIG. 7 is a schematic diagram of determining an optimal cluster K value by an elbow method;
FIG. 8 is an average value of the influence factor attributes of the initial partition;
FIG. 9 is a condensed cluster tree diagram.
Detailed Description
Referring to fig. 1, the present invention provides a multi-source big data-based urban parking partition system, which mainly comprises:
and a data importing module: after determining the study theme of the parking partition, the large data set corresponding to the influence factors with obvious left and right partition effects and the traffic cell boundary are imported into the system. The big data related to urban parking generally comprises data such as urban socioeconomic, population occupancy distribution, land utilization, other traffic service levels, parking operation and supply and demand, and the like, and the data is taken as basic data of urban parking subareas.
A data set counting module: and cleaning abnormal values, missing values and repeated values of the data sets, finishing data preprocessing, and fusing various large data set meters into various traffic cells. The urban traffic big data are generally represented as two types of points and faces in space, and the data are integrated by intersecting geometric elements of GIS software.
Dimension screening module: after the large data set corresponding to the influence factors is counted towards the traffic cell, in order to judge the effectiveness of the influence factors to avoid the interference of invalid factors, the correlation coefficient among various data is calculated, the correlation analysis is completed, and the effective influence factors with lower correlation are reserved to serve as the data basis of the subsequent subareas.
Traffic cell clustering module: the module comprises two steps. Step 1, clustering traffic cells, wherein the traffic cells after data fusion are counted as clustering objects, the attribute of each influencing factor carried by the traffic cells is taken as the characteristic dimension of the clustering, and a K-means method with improved weighting is adopted to identify the primary clustering of each traffic cell; step 2 is condensation clustering, the number of the preliminary clusters of the traffic cells is generally large, the preliminary clusters cannot be directly applied to actual management, the number of the preliminary clusters needs to be reduced, and similar clusters are combined by adopting a condensation clustering algorithm, so that the final clusters of the traffic cells are obtained.
Parking partition network construction module: traffic cell boundaries are difficult to directly apply to actual management, and a parking partition network suitable for actual application needs to be additionally constructed. Therefore, linear elements such as administrative area boundaries, municipal road networks and the like are selected to construct a parking partition network. By correcting the pseudo nodes and hanging points in the network, the parking partition network is ensured to be composed of a closed area.
And a cluster mapping module: and overlapping the traffic district with the final partition attribute with the parking partition network in space, mapping the clustering attribute of the traffic district with the final partition attribute to the parking partition network, and associating the partition result with the administrative district boundary and the municipal road network of the city, thereby constructing the urban parking partition which can be finally applied to actual management.
Referring to fig. 2, the invention provides a city parking partition method based on multi-source big data, which mainly comprises the following steps:
1) The data importing module is used for importing big data related to urban parking and traffic cells;
2) Adopting a data set meter module to realize the set meter fusion of multi-source big data to traffic cells;
3) Adopting a dimension screening module to realize correlation analysis of big data related to urban parking, and screening effective analysis dimensions;
4) Adopting a traffic cell clustering module to realize traffic cell clustering;
5) Adopting a parking partition network construction module to realize parking partition construction based on the existing linear elements;
6) And the cluster mapping module is adopted to realize the mapping of the traffic district cluster attribute to the parking partition, so that the operability of the partition is ensured.
The method aims at carrying out optimal partition on a study subject of urban parking.
Each step is described in detail below with reference to the accompanying drawings.
1 data import
This step aims to import big data related to urban parking and traffic cell boundaries into the system. Big data generally refers to mass data related to urban parking collected through various equipment sensors, and default data of the method and the system are obtained, and the method and the system are data bases for carrying out parking partition work. After determining a research main body and a partition object of a parking partition, judging main influencing factors of the partition object by combining industry specifications and related researches, and importing corresponding big data. The data generally includes urban socioeconomic, demographic and occupancy distribution, land utilization, other traffic service levels, parking operations and supply and demand data, etc. The urban socioeconomic data refer to regional yield values, regional incomes and the like, the population occupancy distribution refers to residence and employment distribution of population, the land utilization data refer to land prices, land types, land volume rates and the like, the other traffic service levels refer to facility distribution and operation conditions of various traffic modes, and the parking operation and supply and demand refer to the dynamic operation level and supply and demand level of parking lots in the region. The traffic cell is a basic analysis unit for researching traffic travel in traffic planning, and generally takes traffic edges of roads, railways and the like, natural barriers of rivers and the like as partition boundaries, has similar social economy, traffic travel and other characteristics, has homogeneity, has perfect traffic cell basic data in traffic planning departments of all cities, can be used as a space carrier for finely representing the micro-level difference characteristics of urban traffic in the middle, and is a basic analysis unit for urban parking partition.
2 data set meter
The step aims at preprocessing the imported large data set, cleaning abnormal values, missing values and repeated values of the data set, and fusing the data set into each traffic cell by a centralized meter. The space form of big data is generally represented as two types of points and planes, wherein point elements are matched to each traffic cell through the point-plane intersection operation of GIS software, and the data set carried by the point elements is counted to each traffic cell by utilizing the fusion operation; the surface element decomposes the surface element and the data carried by the surface element into each traffic cell through the surface-surface intersecting operation of GIS software, and calculates the data set carried by the surface element into each traffic cell through the fusion operation.
The traffic cells of the designed city are represented by the symbol N, and the total number of the traffic cells is N, and the area of the traffic cells is s n The method comprises the steps of carrying out a first treatment on the surface of the The dot elements of the big data set are represented by symbol I, and there are I total; the surface elements are represented by J, the total of which is J, and the area of the surface elements is denoted by a j The method comprises the steps of carrying out a first treatment on the surface of the The influencing factors are represented by a symbol M, M factors are added in total, and a decision variable r for judging whether the point-plane elements intersect with the traffic cell or not is introduced in ,r jn The calculation formula of the data set meter is as follows:
U m ＝(u m1 ,...,u mn ) T equation 3
Wherein u is mn For the m-dimensional influence factor value of the traffic cell n, U m Column vector v for mth dimension influence factor of all traffic cells mi An m-dimensional influence factor value, v, for the i-th point element mj An m-dimensional influence factor value, a, for the jth face element jn The area where the jth face element intersects the traffic cell n.
3-dimensional screening
This step aims at screening out the effective influencing factors of the urban traffic big data set counted to the traffic cell. Partition objects are typically affected by a number of factors, which if taken as the underlying data, can interfere with the partition results. The column vectors of all influence factors on the traffic cell level are calculated in the last step, the correlation coefficient matrix among the column vectors corresponding to the influence factors is calculated, the correlation among the influence factors of the partition object is clear, the importance of all influence factors on the partition result is further judged, and the lower correlation is reserved, so that the partition algorithm is optimized, and the calculated amount is reduced.
M is a set of influence factors, and a, b E M, a is not equal to b, and a calculation formula of a correlation coefficient between any two influence factors is as follows:
wherein u is an For the a-dimensional influence factor value of the traffic cell n,for the mean value of the a-dimensional influence factors, u bn B-dimensional influence factor value for traffic cell n,/->Is the mean value of the b-dimensional influence factors. After finishing the calculation of the two-phase relation number of the influence factors, the influence factors with the absolute value of the correlation coefficient smaller than 0.5 are reserved and used as effective influence factors, and the total number M of the influence factors after screening is updated.
4 traffic cell clustering
Urban parking partition is a systematic complex project, and the essence of the partition is to divide traffic cells with high similarity of urban parking attribute characteristics into the same partition, namely the essence of the partition is to cluster the traffic cells. The method comprises the sub-steps of preliminary clustering and condensation clustering of traffic cells.
Step 1: and taking the data set corresponding to the screened effective influence factors as a data chassis, fully reflecting the influence of the data related to urban parking on the subareas, introducing influence factor weights according to the industry specification and related research of subarea objects to represent the importance degree of each influence factor, taking each traffic cell as a clustering sample, taking the minimum weighted two norms (SSEs) of each sample point and the center of the cluster to which each sample point belongs as an optimization target, and then carrying out the clustering process as follows, wherein the optimal clustering objective function of the traffic cells is shown in the attached figure 3.
Wherein k is the cluster center of the cluster, u mn The m-dimensional influence factor value of the traffic cell n, c mk M-dimensional influence factor value representing cluster center k, w j The weight of the m-dimensional influence factor.
Since the clustering number k of urban parking partitions cannot be determined in advance, k E [3,15] is given according to the related research and actual management]Calculating objective function values SSE under various clustering number conditions, judging an optimal k value by using an elbow method, and acquiring a preliminary clustering of traffic cells under the optimal clustering condition, wherein the preliminary clustering of the traffic cell n is clu n ，clu n ∈[1,k]The flow of the traffic cell optimal clustering algorithm is shown in fig. 3.
Step 2: the number of the preliminary clusters of the traffic cells is generally large, the method cannot be directly applied to actual management, and the similar clusters are combined by adopting a condensation clustering algorithm to further reduce the number of the clusters to the number of the clusters specified by industry practices or specifications of the partition objects, so that the final cluster attribute of each traffic cell is obtained, wherein the specific flow is as follows:
(1) Calculating the average value of the attribute of each influence factor by taking the traffic cells contained in each primary cluster as a whole;
(2) Importing each preliminary cluster and the corresponding influence factor mean value into SPSS software;
(3) Selecting a menu [ analysis, classification and systematic clustering ], selecting influencing factors participating in aggregation cluster analysis into a [ variable ] box, and selecting aggregation in the [ cluster ] boxThe class type is aggregation clustering, the clustering result after the cluster number is reduced is judged according to the tree diagram of the aggregation clustering after click determination, and the final cluster clu of each traffic cell is updated n 。
5 parking partition network structure
The step aims at constructing a parking partition network, and as the traffic cell boundaries cannot be directly applied to actual management, after traffic cell clustering is completed, a boundary for connecting actual management elements, namely the parking partition network, needs to be constructed. According to the study main body and the partition object of urban parking, linear elements such as administrative district boundaries, municipal road networks, river water systems and the like which are highly related to the study or management of the urban parking are selected, and a closed polygonal network formed by interweaving the elements is the parking partition network. The network can be used as a traffic district after clustering, and extends related transitional carriers to administrative areas, municipal roads and the like, and the specific steps of parking partition network construction are as follows:
(1) Merging the linear elements: combining administrative area boundaries of a research range, introducing vector files applicable to partition objects such as municipal single-line networks, river water systems and the like of different grades into arcgis, and combining linear elements of each layer into the same layer by using combining operation to serve as a parking partition initial network;
(2) Constructing a topological network: because each line graph layer has the conditions of break points and the like, the acquired initial network is not completely composed of closed polygons, so that a topological network is constructed, and the problem nodes are inspected and processed. Loading a topology toolbar, and converting the parking partition initial network into a topology network according to an adding rule that no suspension points and no pseudo nodes exist on the basis of the parking partition initial network;
(3) Correction topology network: and (3) topology rule diagnosis is started on the topology network, suspension points and pseudo nodes are inquired, and the actual road network diagram is overlapped to perform trimming, extending and splicing correction operation, so that a parking partition network consisting of closed polygons is finally obtained, and the parking partition network is shown in a figure 4.
6 Cluster mapping
This step aims at mapping the cluster partition attribute of the traffic cell to the parking partition network, therebyObtaining the final urban parking partition. Opening arcgis, loading traffic cell boundaries with final clustering properties and the corrected parking partition network. The parking subareas are represented by a symbol L, L total are provided, and decision variables r for introducing whether the parking subareas are intersected with traffic communities or not nl . And performing intersection operation of the two layers, and filling the traffic cell areas into each parking partition, thereby mapping the final clustering attribute of the traffic cell to the parking partition. Calculating the clustering attribute of each parking partition, taking the contained decomposed traffic cells as a calculation unit and taking the area as a weight, calculating the weighted average clustering attribute of the traffic cells and rounding, wherein the calculation formula is as follows:
wherein a is nl Is the area where the traffic zone n intersects the parking partition l, as shown in fig. 5. A is that l For the area of parking partition l, clu l Is the final cluster attribute of parking partition l. Thus, urban parking partition is completed.
The method and the system for urban parking partition are used for displaying expected effects by taking a certain urban center six areas (A area, B area, C area, D area, E area and F area) as a research range, and the parking allocation index partition is taken as an example, and six steps and results of a model are as follows:
(1) Data importation
The study subject and the partition object of the example are parking allocation partitions, and the aim is to formulate differentiated parking facility supply strategies according to different functional spaces of cities. According to urban parking facility planning guidelines for the departure of living building sections, parking subareas relate to a plurality of factors such as population, land, traffic and the like, five influencing factors such as population density, land volume rate, road congestion degree, track site coverage rate and parking berth demand ratio are initially selected, and corresponding large data sets are collected and tidied. And importing the large data sets corresponding to 2374 traffic cell boundaries and five influencing factors contained in the research scope into the partition system.
(2) Data set meter
In the initial large data set, population density and parking space demand ratio are dot elements, land volume rate, road crowding degree and track site coverage rate are plane elements, and the data set meter module can rapidly meter the large data set onto each traffic cell to form a 2374 row and 5 column data set meter matrix, and part of the matrix is shown in fig. 6.
(3) Dimension screening
According to the dimension screening step, calculating correlation coefficients between every two of the 5 influence factors to form a correlation coefficient matrix, wherein the absolute value of the correlation coefficient of each influence factor is smaller than 0.5, and the influence factors selected preliminarily are considered to be effective.
(4) Traffic cell clustering
Step 1: the study subject of the example is a parking allocation area, a larger weight is given to the parking berth required supply ratio, a weighted K-means algorithm is applied, and an elbow method is combined to judge that the optimal clustering K value is 6 as shown in the attached figure 7, then traffic cells taking the parking allocation as the subject are initially classified into 6 types, the clustering result corresponding to the clustering K value is called, and the clustering result is given to each traffic cell.
Step 2: according to urban parking facility planning guidance of the living building, parking areas are generally divided into three categories of strict limit areas, general limit areas and moderate development areas, and the initial clustering quantity is reduced to three categories by using a condensation clustering algorithm. The average value of the attribute of each influence factor of the six types of preliminary clustering partitions is calculated, and the result is shown in figure 8. Through the aggregation clustering operation of the SPSS, the number of clusters to which the traffic cells belong is reduced from 6 types to 3 types, as shown in figure 9.
(5) Parking partition network structure
And constructing a parking partition network by utilizing road networks and administrative district boundaries above secondary road grades in the research range, correcting pseudo nodes and hanging points in arcgis, and finally, forming 439 closed polygons by the parking partition network.
(6) Cluster mapping
Overlapping the traffic cells with the final clustering attribute with the parking partition network constructed in the fifth step, executing intersection operation by arcgis to obtain 5099 decomposed traffic cells, and transferring the clustering attribute of the decomposed traffic cells to the parking partition by using a clustering mapping formula to obtain the final three types of parking partition.
Summarizing: the parking building partition space finally generated is as follows: the areas are mainly distributed along the two sides of the Zhongshan and Huang Xuang streets, from west to Kang Wanglu and from east to horse, and basically comprise areas with the best Guangzhou public transportation service, highest opening strength and the most intense parking supply and demand in the research range; the second-class area expands the management range outwards for one circle, namely, the north-to-white cloud newcastle, the west-to-inner loop west side, the aromatic village, the west Lang, the south-to-industrial large road, the east-to-C area African west and the D area are vehicle-to-vehicle, the old urban area with higher build density and the new area with higher build and development strength are basically included, and the parking supply pressure is higher; the rest areas are three types of areas, and basically comprise areas with low public transportation service level and good parking supply and demand level in the research range.
The parking allocation partition obtained by the urban parking partition method and the partition system comprehensively considers a plurality of factors such as urban building level, population density, public transportation service level, car operation level, parking supply and demand level and the like, and the partition method and the partition system can quickly, scientifically and accurately identify clusters to which traffic cells belong from big data, and the result accords with reality. Through constructing a parking partition network, traffic cell clustering attributes are mapped to parking partitions formed by road networks and administrative district boundaries, a manager can directly apply partition achievements to assist the manager in planning, constructing, operating and managing the whole flow decision, and the method is high in universality, applicable to urban parking partitions under various situations and extremely high in operability and practical significance.
Compared with the prior art, the invention has the following advantages:
1) The invention relates to a city parking partition method and a partition system based on big data, which can realize city parking partition based on optimal cluster recognition of city traffic cells by applying an improved weighted k-means algorithm and a condensation clustering algorithm based on multi-source big data related to partition objects.
2) The invention relates to a large data-based urban parking partition method and a large data-based urban parking partition system, which can construct a parking partition network based on any linear elements related to partition object research or management, such as administrative district boundaries and municipal road networks. And mapping the optimal cluster of the traffic cell to the parking partition network by using a cluster mapping module.
3) The invention relates to a urban parking partition method and a partition system based on big data, which can be used for efficiently and accurately constructing urban parking partitions directly applied to management, and partition results can be updated rapidly along with basic data iteration so as to cope with urban changes which develop at high speed, assist managers in decision making and diagnose urban parking problem boundaries, really realize partition differentiation management by using a scientific method in big data age, and improve the scientificity of urban traffic management.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (9)
1. A city parking partition method based on multi-source big data is characterized by mainly comprising the following steps:
1) Data importing;
2) A dataset meter;
2.1 Completing cleaning of abnormal values, missing values and repeated values of the data set;
2.2 Counting the big data set corresponding to the main influencing factors of the partition object on each traffic cell;
3) Dimension screening
3.1 Calculating a correlation coefficient matrix among all influence factors, determining the correlation degree among the influence factors of the partition object, and identifying the influence degree of all influence factors on the partition result;
3.2 After the calculation of the correlation coefficient of the influence factors is completed, the influence factors with the absolute value of the correlation coefficient smaller than 0.5 are reserved and used as effective influence factors, and the total number M of the influence factors after screening is updated;
4) Traffic cell clustering
4.1 Traffic cell cluster): introducing an influence factor weight, and constructing an optimal clustering objective function of the traffic cell as follows:
wherein k is the cluster center of the cluster, u mn The m-dimensional influence factor value of the traffic cell n, c mk M-dimensional influence factor value representing cluster center k, w j The weight of the m-dimensional influence factors;
4.2 Calculating the optimal clustering objective function value of each situation in the k E [3,15] range by adopting an improved weighted k-means algorithm, and judging the optimal k value by utilizing an elbow method to obtain the preliminary clustering of the traffic cells under the optimal clustering condition;
4.3 Aggregation and clustering: reducing the number of clusters to the number of clusters specified by industry practice or specification to which the partition object belongs; calculating the average value of each influence factor attribute of each preliminary cluster, obtaining a tree diagram of the condensed clusters by utilizing the condensed clustering function of the SPSS, judging a clustering result after the number of clusters is reduced, and updating the final cluster of each traffic cell;
5) Constructing a parking partition network;
6) And (5) clustering and mapping.
2. The urban parking partition method based on multi-source big data according to claim 1, wherein 1) the data importing comprises:
1.1 Determining a research main body and a partition object of the parking partition, and importing a big data set corresponding to a main influence factor of the partition object into a partition system;
1.2 A traffic cell boundary within the scope of the study is imported into the zoning system.
3. The urban parking partition method based on multi-source big data according to claim 1, wherein 2.2) counts big data sets corresponding to main influencing factors of partition objects onto each traffic cell, specifically comprising:
the traffic cells of the designed city are represented by the symbol N, and the total number of the traffic cells is N, and the area of the traffic cells is s n The method comprises the steps of carrying out a first treatment on the surface of the The dot elements of the large data set related to urban parking are represented by symbol I, and there are I total dot elements; the surface elements are represented by J, the total of which is J, and the area of the surface elements is denoted by a j The method comprises the steps of carrying out a first treatment on the surface of the The influencing factors are represented by a symbol M, M factors are added in total, and a decision variable r for judging whether the point-plane elements intersect with the traffic cell or not is introduced in ,r jn The set of point and face elements is formulated as:
U m ＝(u m1 ,...,u mn ) T
wherein u is mn For the m-dimensional influence factor value of the traffic cell n, U m Column vector v for mth dimension influence factor of all traffic cells mi An m-dimensional influence factor value, v, for the i-th point element mj An m-dimensional influence factor value, a, for the jth face element jn The area where the jth face element intersects the traffic cell n.
4. The urban parking partition method based on multi-source big data according to claim 1, wherein in 3.1), M is a set of influencing factors, and if a, b e M, a is not equal to b, a calculation formula of a correlation coefficient between any two influencing factors is:
wherein u is an For the a-dimensional influence factor value of the traffic cell n,for the mean value of the a-dimensional influence factors, u bn B-dimensional influence factor value for traffic cell n,/->Is the mean value of the b-dimensional influence factors.
5. The urban parking partition method based on multi-source big data according to claim 1, wherein 5) the parking partition network structure specifically comprises:
5.1 Selecting linear elements with high management correlation with partition objects, including administrative area boundaries, municipal road networks, river water systems and the like, introducing arcgis and merging the arcgis into the same layer;
5.2 Constructing topology network inspection problem nodes in arcgis and processing; loading a topology toolbar, and converting the parking partition initial network into a topology network according to an adding rule that no suspension points and no pseudo nodes exist on the basis of the parking partition initial network;
5.3 And (3) starting topology rule diagnosis on the topology network, inquiring out hanging points and pseudo nodes, and overlapping an actual road network diagram to perform trimming, extending and splicing correction operation to obtain a parking partition network consisting of a closed polygonal network.
6. The urban parking partition method based on multi-source big data according to any one of claims 1 to 5, wherein 6) cluster mapping specifically comprises:
6.1 Loading traffic cell boundaries with final clustering properties in acrgis, and carrying out intersection operation of two layers with the corrected parking partition network, and filling traffic cell partitions into each parking partition;
6.2 Calculating the cluster attribute of each parking partition, taking the contained decomposed traffic cells as a calculation unit and taking the area proportion of the traffic cells occupying the parking partition as a weight, and calculating the weighted average cluster attribute of the traffic cells and rounding the weighted average cluster attribute, wherein the cluster mapping formula is as follows:
wherein, the parking partition is represented by the symbol L, and L and r are counted in total nl A is a decision variable of whether a parking partition and a traffic district are intersected or not nl The area where the traffic zone n intersects with the parking partition l; clu n For the final cluster attribute of parking partition l, A l The urban parking partition is completed so far for the area of the parking partition l.
7. A parking partition system for implementing a multi-source big data based urban parking partition method according to any one of claims 1-6, characterized in that it essentially comprises:
and a data importing module: implementing the step 1);
a data set counting module: implementing the step 2);
dimension screening module: implementing the step 3);
traffic cell clustering module: implementing the step 4);
parking partition network construction module: implementing the step 5);
and a cluster mapping module: implementing the step 6).
8. A terminal for a multi-source big data based urban parking partition system, comprising a memory, a processor and at least one instruction or at least one section of computer program stored on the memory and loadable and executable on the processor, wherein the processor loads and executes the at least one instruction or the at least one section of computer program to implement a multi-source big data based urban parking partition method according to any one of claims 1-6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a multi-source big data based urban parking partition method according to any of claims 1-6.
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