CN113192321A - Traffic demand distribution extraction method for comprehensive land utilization - Google Patents
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Abstract
The invention discloses a traffic demand distribution extraction method for comprehensive land utilization, and belongs to the technical field of calculation, calculation or counting. The method comprises the following steps: firstly, collecting historical travel data of residents and urban interest points; then, extracting travel origin-destination positions, and generating a traffic cell through clustering; calculating the proportion and density of interest points of each traffic cell, and dividing the comprehensive land utilization types; extracting daily traffic demand sequences of all traffic districts according to travel time according to historical travel data of residents; and integrating traffic demand sequences of traffic districts corresponding to each type of comprehensive land utilization type, and further extracting typical traffic demand distribution under each comprehensive land utilization type through time sequence clustering, wherein the typical traffic demand distribution specifically comprises traffic occurrence distribution and traffic attraction distribution. The method has higher popularization and application values, can provide objective suggestions for reasonable development and utilization of urban land, and provides reference information for fixed-point traffic management and control.
Description
Technical Field
The invention discloses a traffic demand distribution extraction method for comprehensive land utilization, relates to a data-driven urban traffic demand analysis technology, and belongs to the technical field of calculation, calculation or counting.
Background
In recent years, with the continuous acceleration of the urbanization process of China, the land development strength is remarkably improved, and comprehensive land utilization becomes the inevitable trend of the current urban land development. Under the background, traffic demand distribution under different comprehensive land utilization types is often greatly different, and urban traffic demand distribution is increasingly complex. The problem of urban congestion is more serious due to the relatively unordered land development in the early stage of cities, and particularly during the rush hour in the morning and at night, the overall urban traffic system is overloaded due to the highly concentrated traffic demand. Therefore, the traffic demand distribution is extracted in different comprehensive land utilization types, reasonable development and utilization of urban land are guided from the perspective of balancing traffic demands, and the problem of urban traffic jam at the present stage is relieved.
At present, with the popularization of smart phones, electronic maps are beginning to be widely used. By using an electronic map interface, points of interest inside a city can be downloaded. The interest points describe geographical entities and positions thereof, such as schools, banks, hospitals, supermarkets and the like, which are closely related to life, can directly reflect information such as urban population density, development degree, development intensity and the like, and provide support for quickly identifying the land utilization types in cities. In addition, with the continuous development of the GPS positioning technology, taxis, buses and shared bicycles can position the position of the vehicle in real time due to the fact that the GPS recorder is generally installed, historical travel data of residents can be recorded, and a foundation is laid for extraction and analysis of urban traffic demands.
Although much research has been focused on extracting urban traffic demands and analyzing their spatial-temporal distributions, various limitations still exist. First, the existing research focuses on the influence of a single land use type on the traffic demand distribution, but few scholars research the traffic demand distribution under the comprehensive land use type. Furthermore, under the same land use type, the traffic demand may still exhibit different distribution characteristics due to the influence of the land development intensity. In the past research, traffic demands under the same land utilization type are usually subjected to centralized analysis, the influence of land development intensity on traffic demand distribution is ignored, and the research result is limited.
Disclosure of Invention
The invention aims to provide a traffic demand distribution extraction method facing comprehensive land utilization, which aims to solve the technical problems that the existing traffic demand distribution extraction method focuses on the influence of a single land utilization type on the traffic demand distribution and ignores the influence of the land development intensity on the traffic demand distribution by objectively dividing the comprehensive land utilization type and reasonably extracting the traffic demand distribution and calculating the interest point density corresponding to the comprehensive land utilization type to reflect the land development intensity, and the invention aims to obtain the comprehensive land utilization type reflecting the influence of different land utilization types on the traffic demand distribution by clustering traffic cells according to the proportion and density of different types of interest points in the traffic cells.
The invention adopts the following technical scheme for realizing the aim of the invention:
a traffic demand distribution extraction method for comprehensive land utilization comprises the following steps:
(1) collecting data: the method comprises the following steps of collecting historical travel data of residents and interest points of cities, and specifically comprising the following data:
(11) the historical trip data includes: travel date, departure time, departure position, arrival time, arrival position;
(12) the points of interest include: interest point type and interest point position;
(2) extracting a start-end position: extracting the starting positions and the arrival positions in all the historical travel data collected in the step (1) and integrating the starting positions and the arrival positions into a starting-destination position data set;
(3) and (3) generating a traffic cell: the method comprises two steps of dividing a start-destination cluster and drawing a traffic cell, and specifically comprises the following steps:
(31) dividing a start-to-end cluster: clustering the origin-destination position data sets obtained in the step (2) to obtain different origin-destination clusters;
(32) drawing a traffic cell: connecting peripheral origin-destination points aiming at each origin-destination point cluster to form a closed area, namely a traffic cell; the connecting line of the peripheral origin-destination point is the boundary of the traffic cell;
(4) and (3) counting the proportion and density of interest points: the method comprises three steps of dividing interest points, calculating interest point proportion and calculating interest point density, and specifically comprises the following steps:
(41) dividing interest points: matching the position of the interest point collected in the step (1) with the boundary of the traffic cell obtained in the step (32), and dividing the interest point into each traffic cell;
(42) and (3) calculating the interest point proportion: counting the proportion of different interest point types in each traffic cell;
(43) calculating the density of the interest points: counting the number of interest points in each traffic cell, simultaneously calculating the area of the traffic cell, and dividing the number of the interest points by the area of the traffic cell to obtain the density of the interest points;
(5) dividing comprehensive land utilization types: clustering the interest point proportion and the interest point density of each traffic cell obtained in the step (4), and dividing the traffic cells into different comprehensive land utilization types;
(6) and (3) counting a traffic demand sequence: the method comprises two steps of calculating a traffic occurrence sequence and a traffic attraction sequence, and specifically comprises the following steps:
(61) counting a traffic occurrence sequence: aiming at each comprehensive land utilization type, sequentially extracting traffic occurrence of each traffic cell within a fixed time interval according to departure time by using historical travel data acquired in the step (1) to generate a traffic occurrence sequence;
(62) and (3) counting a traffic attraction sequence: aiming at each comprehensive land utilization type, sequentially extracting the traffic attraction of each traffic cell within a fixed time interval according to arrival time by using the historical travel data acquired in the step (1) to generate a traffic attraction sequence;
(7) extracting traffic demand distribution: the method comprises two steps of classifying traffic demand sequence categories and extracting typical traffic demand distribution, and specifically comprises the following steps:
(71) dividing traffic demand sequence categories: dividing the traffic generation quantity sequence and the traffic attraction quantity sequence under each comprehensive land utilization type obtained in the step (6) into different categories through clustering;
(72) extracting a typical traffic demand distribution: and (5) extracting the clustering centers of all the traffic demand sequence categories in the step (71), thereby obtaining the typical traffic demand distribution under all the comprehensive land utilization types.
Compared with the existing traffic demand extraction method, the method has the following obvious advantages: firstly, based on urban interest point data, the urban land utilization is quantitatively analyzed by calculating the proportion and the density of each type of interest point, and the objective classification of urban comprehensive land utilization types is realized; secondly, the traffic demand distribution extraction is realized by facing comprehensive land utilization, reasonable suggestions can be provided for urban land utilization layout from the perspective of balanced traffic demand distribution, and reference information is provided for fixed-point traffic management and control.
Drawings
Fig. 1 is a flow chart of the traffic demand distribution extraction method for comprehensive land utilization of the present invention.
FIG. 2 is a schematic representation of the area of investigation in an example of the invention.
Fig. 3 is a diagram illustrating the distribution result of traffic cells according to an embodiment of the present invention.
Fig. 4(a) to 4(f) are graphs showing typical traffic occurrence distribution extraction results corresponding to type i, type ii, type iii, type iv, type v, and type vi in the examples of the present invention.
Fig. 5(a) to 5(f) are graphs showing typical traffic suction distribution extraction results corresponding to type i, type ii, type iii, type iv, type v, and type vi in the examples of the present invention.
Detailed Description
The invention introduces a traffic demand distribution extraction method oriented to comprehensive land utilization. The technical solution of the present invention will be further described in detail with reference to the following examples and accompanying drawings.
In the example, the data of the travel of the urban residents and the points of interest of the urban are taken from 1 day at 11 months to 30 days at 11 months in 2016, and the division of the urban comprehensive land utilization type and the extraction of traffic demand distribution are realized according to the data processing steps in the technical scheme. The flow chart of the method is shown in figure 1, and the method comprises the following 6 steps:
(1) collecting data: the example uses within three rings of metropolis as the main study area, as shown in fig. 2, and mainly covers the golden ox area, the blue sheep area, the Wuhou area, the Jinjiang area and the Chenghua area. And collecting travel data of residents and urban interest points in the research area. The resident travel data comprises five fields of travel date, departure time, departure position, arrival time and arrival position, and the interest point comprises two fields of interest point type and interest point position. The types of the interest points are divided into eight categories: catering services, shopping services, science and education culture, company enterprises, transportation facilities, life services, medical care and business housing. The resulting resident travel data table and the point of interest data table are shown in tables 1 and 2, respectively. The departure position, the arrival position and the interest point position are stored in a data form of (A, B), wherein A represents longitude and B represents latitude.
TABLE 1 adult city resident trip data sheet
TABLE 2 adult city interest point data Table
(2) Extracting origin-destination positions and generating a traffic cell: extracting the starting positions and the arrival positions of all historical travel data and integrating the starting positions and the arrival positions into a starting-destination position data set; dividing the origin-destination positions into 120 origin-destination clusters by adopting a K-Mediods clustering algorithm; the peripheral origin-destination of each origin-destination cluster is connected as the boundary of the traffic cell. Finally, 120 traffic cells are generated, see fig. 3.
(3) And (3) counting interest point types and densities: dividing interest points into all traffic districts by matching the interest points of the adult cities with the outlines of the traffic districts; counting the proportion of eight interest point types in each traffic cell; in addition, the area (unit: square kilometer) of each traffic cell is counted, and the number of interest points is divided by the area of the traffic cell to obtain the density of the interest points (per square kilometer). The interest point ratio and the interest point density statistics of each traffic cell are shown in table 3.
Table 3 statistics table of interest point proportion and interest point density of each traffic cell
(4) Dividing comprehensive land utilization types: and clustering the interest point proportion and the interest point density of 120 traffic districts by adopting a Mean-shift clustering algorithm to obtain 6 typical comprehensive land utilization types. Wherein, the total number of the traffic districts with the comprehensive land type of type I is 85, and the occupied proportion is the maximum; in addition, the number of the traffic districts with the comprehensive land utilization types of type II, type III, type IV, type V and type VI is respectively 25, 2 and 4. By extracting the clustering center of each type, as shown in table 4, the characteristics of each comprehensive land use type are analyzed.
Table 4 comprehensive land use type clustering center data table
As can be seen from Table 4, in the comprehensive land utilization types I, II and III, the sum of the proportion of the catering service, the shopping service and the living service exceeds 65%; in addition, the increasing density of points of interest from type I to type III indicates that the intensity of land development is increasing. The type IV business residences have a larger proportion, and the corresponding land utilization is mainly the residential residences. The enterprises and business residences of the company in the category V have a large proportion, and the corresponding land utilization is mainly based on the position and the residence mixture. Type vi medical care accounts for a large proportion, indicating that the corresponding land use is concentrated in a large number of hospitals and health care places.
(5) And (3) counting a traffic demand sequence: in this example, the traffic demand extraction time interval is set to 1 hour. Therefore, for the daily historical travel data of each traffic cell, the traffic occurrence amount per hour is sequentially extracted according to the departure time, and the traffic attraction amount per hour is sequentially extracted according to the arrival time, so that a traffic occurrence amount sequence and a traffic attraction amount sequence are obtained. Finally, 3600 traffic occurrence sequence and 3600 traffic attraction sequence are extracted in the example. And integrating the traffic occurrence quantity sequence and the traffic attraction quantity of the traffic cells with the same land use type, thereby obtaining the traffic occurrence quantity sequence and the traffic attraction quantity sequence corresponding to each comprehensive land use type.
(6) Extracting traffic demand distribution: and classifying the traffic occurrence quantity sequences and the traffic attraction quantity sequences of the six types of comprehensive land utilization types respectively by using a K-shape algorithm. Meanwhile, the clustering centers of all traffic demand sequences are extracted to obtain typical traffic demand distributions under all comprehensive land utilization types, wherein the number of the typical traffic demand distributions corresponding to the types I, II, III, IV, V and VI is respectively 4, 3, 2 and 2. The traffic generation amount distribution of the six general land types extracted in the present example is shown in fig. 4(a) to 4(f), and the traffic attraction amount distribution of the six general land types extracted in the present example is shown in fig. 5(a) to 5 (f).
Claims (9)
1. A traffic demand distribution extraction method oriented to comprehensive land utilization is characterized in that,
collecting historical travel data of residents and urban interest points;
extracting origin-destination positions from collected resident historical travel data and generating a traffic cell;
counting the proportion and the density of interest points of each traffic cell;
dividing the comprehensive land utilization types in a mode of clustering the interest point proportion and the interest point density of each traffic cell;
and counting the traffic demand sequences corresponding to the comprehensive land utilization types, and clustering the traffic demand sequences corresponding to the comprehensive land utilization types to obtain typical traffic demand distribution under the comprehensive land utilization types.
2. The comprehensive land utilization-oriented traffic demand distribution extraction method according to claim 1, wherein the historical travel data of the residents comprises: travel date, departure time, departure location, arrival time, arrival location.
3. The method for extracting traffic demand distribution for comprehensive land use according to claim 1, wherein the urban interest points comprise interest point types and interest point positions.
4. The method for extracting traffic demand distribution for comprehensive land utilization according to claim 2, wherein the method for extracting origin-destination points from collected resident historical travel data and generating traffic cells comprises the following steps: and extracting departure positions and arrival positions in all collected resident historical travel data, integrating the departure positions and the arrival positions into an origin-destination position data set, clustering the origin-destination position data set to obtain origin-destination clusters, and obtaining a closed area formed by connecting peripheral origin-destination points of each origin-destination cluster as a traffic cell.
5. The comprehensive land utilization-oriented traffic demand distribution extraction method as claimed in claim 3, wherein the method for counting the interest point proportion and density of each traffic cell comprises the following steps: the interest points are divided into the traffic cells in a mode of matching the collected interest point positions with the boundaries of the traffic cells, and the proportion of different interest point types in the traffic cells and the number of the interest points in unit area of the traffic cells are counted.
6. The comprehensive land utilization-oriented traffic demand distribution extraction method as recited in claim 1, wherein a Mean-shift clustering algorithm is adopted to cluster interest point ratios and interest point densities of traffic cells.
7. The method for extracting traffic demand distribution for comprehensive land use according to claim 1, wherein the traffic demand sequence corresponding to each comprehensive land use type comprises: and the traffic occurrence quantity sequence and the traffic attraction quantity sequence correspond to each comprehensive land utilization type.
8. The method for extracting traffic demand distribution for comprehensive land use according to claim 7, wherein the method for counting the traffic demand sequences corresponding to the comprehensive land use types comprises the following steps: and extracting the hourly traffic occurrence amount and traffic attraction amount of each traffic cell according to the historical travel data of each traffic cell, generating a traffic occurrence amount sequence and a traffic attraction amount sequence of each traffic cell, and integrating the traffic occurrence amount sequence and the traffic attraction amount of the traffic cells with the same land use type to obtain a traffic occurrence amount sequence and a traffic attraction amount sequence corresponding to each comprehensive land use type.
9. The method for extracting traffic demand distribution oriented to comprehensive land utilization according to claim 7, characterized in that a traffic demand sequence corresponding to each comprehensive land utilization type is clustered by using a K-shape algorithm to obtain typical traffic demand distribution under each comprehensive land utilization type.
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