CN110188953B - O-D space-time distribution prediction method based on space durin model - Google Patents
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Abstract
The invention relates to an O-D space-time distribution prediction method based on a space durin model, and belongs to the technical field of urban traffic planning and management and intelligent traffic systems. Adding the built environment as an explanatory variable of O-D space-time distribution, and demonstrating the explanatory property of the built environment on the O-D space-time distribution by a case; a method of estimating the amount of traffic generation or attraction for neighboring cells using the amount of traffic generation or attraction for a certain traffic cell is presented. The method has the advantages that the overflow effect of the built environment on the O-D space-time distribution is explained, the overflow effect is decomposed into a direct effect, an indirect effect and a total effect, and the accuracy of the urban O-D space-time distribution prediction result is improved.
Description
Technical Field
The invention belongs to the technical field of urban traffic planning and management, relates to the field of transportation travel Origin-Destination (O-D) space-time distribution and ITS intelligent traffic system, and is particularly suitable for an O-D space-time distribution interpretation and O-D space-time distribution prediction method based on urban built-up environment.
Background
The existing research on O-D demand distribution mainly comprises two steps of O-D data acquisition and O-D matrix construction to obtain the O-D distribution of the urban traffic system. Alexander adopts mobile phone triangulation data as the daily travel track of individuals and families, and the accuracy and timeliness of the mobile phone triangulation data can replace the traditional family travel survey data. Hadavi and Shafahi propose an O-D estimation based on traffic sensor data, which applies license plate recognition sensors and proposes four location models to obtain an O-D stream. The construction method of the O-D matrix is mainly divided into two types: statistical methods and mathematical planning methods. The Ge and Fukuda utilize a maximum entropy principle, and travel O-D demand estimation related to work is achieved based on mobile phone GPS track data. Lee proposes a robust optimization method for O-D network evaluation that can generate O-D matrices and overcome the uncertainty of O-D demand distribution.
Whether in the O-D data acquisition process or the O-D matrix construction process, more history origin-destination data is used in the research, and a mathematical method is adopted to carry out O-D back-estimation, so that the intrinsic factors influencing the O-D distribution are not (cannot be) analyzed. Therefore, the invention adopts taxi data, provides an O-D space-time distribution prediction method based on a space Dubin model, and fully considers the influence of the urban built-up environment on the O-D space-time distribution, thereby providing a more accurate prediction method.
Disclosure of Invention
The technical problem to be solved by the invention is to firstly obtain O-D distribution of each cell by utilizing taxi GPS data in each traffic cell, then construct a space durin model on the basis of the O-D distribution, estimate overflow effect of urban built-up environment on the O-D distribution, and estimate O-D distribution number of adjacent cells by using the distribution number of vehicles O-D in the urban traffic cell.
The technical scheme of the invention is as follows:
an O-D space-time distribution prediction method based on a space durin model is characterized by comprising the following steps:
(1) traffic cell division
Firstly, the traffic zones of the research area are divided, and a mode of dividing the administrative township streets or rasterizing the administrative township streets can be adopted.
(2) Urban built environment element extraction and statistics
According to research needs, indexes of environment elements built in various cities in a traffic community are extracted, wherein the indexes mainly comprise density, land utilization diversity, block design, destination accessibility and distance from public transport facilities, and statistics of the environment elements built in the traffic community is carried out. In addition, the traffic generation amount and the attraction amount of each traffic cell in the study period are obtained through basic data processing.
(3) Basic form of space durin model
y＝ρWy+Xβ+γWX+ε,ε～N(0,σ 2 I n ) (1)
In the formula, n is the number of traffic districts; y is a vector of n x 1 and represents an interpreted variable, namely the early peak traffic generation amount or the attraction amount of a certain traffic cell; x is an nxk data matrix which represents an explanatory variable, namely various built environment indexes of a certain traffic cell, and k is the number of urban built environment elements; w is a spatial weight matrix, rho is a coefficient of a spatial lag dependent variable Wy, gamma is a coefficient of a spatial lag independent variable WX, beta reflects the influence of an explanatory variable on the change of the dependent variable y, and epsilon is a random error term;
the spatial weight matrix W reveals the interaction between spatial cells, of the form:
each element in the space weight matrix is space weight, and the calculation method of the space weight adopts an inverse distance weight matrix, and the form is as follows:
(4) effect decomposition of spatial durbin model
In order to explain the overflow effect in the space duren model, the direct effect, the indirect effect and the total effect of independent variables on dependent variables are introduced, namely the change of a single built-up environment element associated with the traffic generation amount or the attraction amount in any traffic cell not only influences the traffic generation amount or the attraction amount of the cell, but also indirectly influences the traffic generation amount and the attraction amount of other adjacent areas;
to facilitate the measurement of direct and indirect effects, equation (1) is rewritten as follows:
(I n -ρW)y＝ι n α+Xβ+WXθ+ε (4)
y＝(I n -ρW) -1 ι n α+(I n -ρW) -1 X(I n β+Wθ)+(I n -ρW) -1 ε (5)
order (I) n -ρW) -1 ＝V(W)，S(W)＝V(W)(I n β + W θ), then equation (6) is obtained, the matrix form is equation (7), seeThe matrix of the number effect is expressed as formula (8):
y＝V(W)ι n α+S(W)X+V(W)ε (6)
the average direct effect is the sum of the main diagonal elements in the formula (8) divided by n; s in the matrix r (W) the sum of all elements divided by n is the average total effect; the difference between the average total effect and the average direct effect is recorded as the average indirect effect; as follows:
in the above formula, r is 1,2,3, r, k, i is the number of the built-up environmental elements in the city n Representing an n × 1 order matrix, M (k) Direct Representing the direct effect of environmental elements built into a city on traffic production or attraction within the local traffic cell, M (k) Indirect Representing the indirect effect of environmental elements built into a city on traffic production or attraction in neighboring traffic cells, M (k) total Representing the total effect of the urban built-up environment elements on the traffic generation or attraction in the traffic cell.
The invention has the beneficial effects that: the O-D space-time distribution prediction method based on the space Dubin model adds the built-up environment as the explanatory variable of traffic generation and attraction of the traffic cell, and proves the explanatory of the built-up environment on the O-D space-time distribution; and on the basis, the overflow effect of the urban built environment on the O-D distribution is proved and quantified, the overflow effect is decomposed into a direct effect, an indirect effect and a total effect, the detailed information of spatial correlation is perfected, and reference is provided for urban land planning development of urban management and planning departments.
Detailed Description
The following describes the embodiments of the present invention in detail with reference to the technical solutions, and simulates the implementation effects of the present invention.
(1) Study object
Selecting Shenzhen market as a research scope, wherein the Shenzhen market is not only an economic center of the whole nation, the infrastructure construction is perfect, and the urban construction environment elements are rich and the distribution scope is wide; the population gathering center in the pearl triangle area has huge population flow amount both inside and outside, and is convenient for developing research.
(2) Basic data
The division of the traffic cells is completed by applying ArcGIS software, and grids with the size of 1.5km x 1.5km are finally selected as the dimension of the unit traffic cell in consideration of the richness of built-up environment inside the traffic cells and the operability of grid data, so that 1031 traffic cells are obtained. And selecting the origin-destination points of the floating vehicle track in 6:00-8:30 days 6: 9-14 days 6.9.2014 by Oracle programming, scattering the matched GPS points of the floating vehicle into a traffic cell, and extracting the traffic generation amount and the attraction amount of each cell in ArcGIS, namely the dependent variable. Selecting 14 construction environment elements in total, namely hotel density, restaurant density, supermarket density, pharmacy density, mansion density, school density, hospital density, bank density, government unit density, bus station density, intersection density, subway station density, land utilization diversity and distance from a transportation hub in a research community as construction environment independent variables. The statistical values of each built-up environment attribute are given in table 1.
TABLE 1 statistics of the main independent variables of each traffic cell
(3) Global regression determines significant independent variables
For most spatial empirical analysis, spatial metric modeling generally starts with a non-spatial linear regression model first, and then further discusses whether the model needs to be extended to account for spatial interaction effects to build a spatial metric model, so this study first builds a global regression model as a benchmark reference for spatial metric analysis.
In the global regression model, 1031 traffic cell early peak traffic generation amount and attraction amount (O-D) are used as dependent variables, the city built environment attribute is used as an independent variable, and the model calibration is completed in SPSS software. The estimation results are shown in table 2, and when the absolute value of the value of t is greater than 1.96, it is shown that the influence of the independent variable on the traffic attraction amount or the occurrence amount is significant.
TABLE 2 Global regression model results
Note: significance levels were 99%, 95% and 90%, respectively.
R when the interpreted variable in the model result is the traffic generation amount a 2 0.610, indicating that the independent variables in the model can account for 61.0% change in traffic production; when the interpreted variable is the traffic attraction, R a 2 And 0.640, illustrates that the independent variables in the model can account for the 64.0% change in traffic attraction.
As can be seen from Table 2, there is a significant positive correlation between the number of hotels, the number of buildings and the number of bus stops and traffic generation; while the number of quotients, diversity and distance to the traffic hub are significantly negatively correlated with traffic generation. The number of mansions, subway stations, bus stations and governments has obvious positive correlation with traffic attraction, and the diversity has obvious negative correlation with traffic attraction.
(4) Substituting significant independent variables into spatial Dubin model
The results of the spatial Durbin model based on Matlab programming are shown in tables 3 and 4 below.
TABLE 3 space Durbin model parameter estimation results (traffic generation)
Note: significance levels were 99%, 95% and 90%, respectively.
TABLE 4 space Durbin model parameter estimation results (traffic attraction)
Note: significance levels were 99%, 95% and 90%, respectively.
As can be seen from tables 3 and 4, hotel density, building density and bus station density have significant positive correlation with traffic generation, and supermarket density has significant negative correlation with traffic generation; the mansion density, the subway station density, the bus station density and the government density have obvious positive correlation with traffic attraction and are consistent with the result of the global regression model. However, the effect of diversity on traffic attraction and traffic generation is not significant, because of the existence of the spatial weight matrix, so that the effect of diversity on the interpreted variables is decomposed, and the effect of diversity on self traffic generation or attraction is not significant.
The variable with W in tables 3 and 4 represents a spatial lag term representing that traffic generation or traffic attraction of the traffic cell is affected by the ambient traffic cell creation environment variable. For traffic generation, the spatial lag variables are all significant except that the spatial lag variables of supermarket density and bus stops are not significant. Specifically, the hotel density and building density of the surrounding cells have a significant positive effect on the traffic generation of the cell, and the diversity of the surrounding cells has a significant negative effect on the traffic generation of the cell. For the traffic attraction, the spatial lag variables of the other variables are obvious except that the spatial lag variable of the bus stop is not obvious. The density of buildings and subway stations in peripheral cells has obvious positive influence on the traffic attraction of the cell, and the density and diversity of governments in the peripheral cells have obvious negative influence on the traffic attraction of the cell.
The coefficient rho of the spatial lag variable is positive and statistically significant, which indicates that the spatial overflow effect cannot be ignored when the influence of the established environment on the O-D distribution is researched. And the travel or attraction of the surrounding traffic districts is improved by 1 percent, and the traffic generation or attraction of the district is improved by about 0.24 percent and 0.25 percent.
The model parameters in tables 3 and 4 are decomposed into direct and indirect effects, and the total effect is obtained. The results of the decomposition calculations are shown in tables 5 and 6.
TABLE 5 direct, indirect and Total Effect of the spatial Durbin model (traffic generation)
Note: levels of significance were 99%, 95% and 90%, respectively.
TABLE 6 direct, indirect and Total Effect (traffic attraction) of the spatial Durbin model
Note: significance levels were 99%, 95% and 90%, respectively.
From the quantitative point of view, the bus stop density has obvious positive correlation on traffic generation influence, the direct effect accounts for 51% of the total effect, and the indirect effect accounts for 49% of the total effect, which shows that the bus stop density has double effects of explaining variables on traffic generation increase. The method reflects that daily travel of residents is carried out by taking a bus station as a center and spreading the travel to the periphery, the spreading range is considerable, and a traffic manager is prompted to properly add taxi pick-up points near the bus station to meet the travel requirements. In addition, diversity, whether it is traffic generating or attracting, both indirect and total effects are statistically significant and negative, and the coefficient of the indirect effect is an absolute contribution to the total effect, indicating that diversity is not a direct determinant of traffic generating or attracting. Generally, the supporting facilities in a certain area are perfect, the land utilization types are various, the design of a block is scientific and reasonable, the block is considered as a mature block, the research is consistent with the previous research, the diversity degree of the mature block is higher, the diversity of the surrounding blocks can be improved, and accordingly less traffic generation and attraction are generated.
Claims (1)
1. An O-D space-time distribution prediction method based on a space durin model is characterized by comprising the following steps:
(1) traffic cell division
Firstly, dividing a research area into traffic zones, and adopting a mode of dividing administrative township streets or rasterizing;
(2) urban construction environment element extraction and statistics
Extracting indexes of environment elements built in various cities in the traffic community according to needs, wherein the indexes comprise density, land utilization diversity, block design, destination accessibility and distance from public transport facilities, and carrying out statistics of the environment elements built in the traffic community; in addition, the traffic generation amount and the attraction amount of each traffic cell in the research period are obtained through basic data processing;
(3) basic form of space durin model
y＝ρWy+Xβ+γWX+ε,ε～N(0,σ 2 I n ) (1)
In the formula, n is the number of traffic districts; y is a vector of n x 1 and represents an interpreted variable, namely the early peak traffic generation amount or the attraction amount of a certain traffic cell; x is an nxk data matrix which represents an explanatory variable, namely various built environment indexes of a certain traffic cell, and k is the number of urban built environment elements; w is a spatial weight matrix, rho is a coefficient of a spatial lag dependent variable Wy, gamma is a coefficient of a spatial lag independent variable WX, beta reflects the influence of an explanatory variable on the change of the dependent variable y, and epsilon is a random error term;
the spatial weight matrix W reveals the interaction between spatial cells, of the form:
each element in the space weight matrix is space weight, and the calculation method of the space weight adopts an inverse distance weight matrix, and the form is as follows:
(4) effect decomposition of spatial durbin model
In order to explain the overflow effect in the space duren model, the direct effect, the indirect effect and the total effect of independent variables on dependent variables are introduced, namely the change of a single built-up environment element associated with the traffic generation amount or the attraction amount in any traffic cell not only influences the traffic generation amount or the attraction amount of the cell, but also indirectly influences the traffic generation amount and the attraction amount of other adjacent areas;
to facilitate the measurement of direct and indirect effects, equation (1) is rewritten as follows:
(I n -ρW)y＝ι n α+Xβ+WXθ+ε (4)
y＝(I n -ρW) -1 ι n α+(I n -ρW) -1 X(I n β+Wθ)+(I n -ρW) -1 ε (5)
order (I) n -ρW) -1 ＝V(W)，S(W)＝V(W)(I n β + W θ), then equation (6) is obtained, the matrix form is equation (7), and the matrix expression of the parametric effect is equation (8):
y＝V(W)ι n α+S(W)X+V(W)ε (6)
V(W)ι n α+V(W)ε
the average direct effect is the sum of the main diagonal elements in the formula (8) divided by n; in matrix S r (W) the sum of all elements divided by n is the average total effect; the difference between the average total effect and the average direct effect is recorded as the average indirect effect; as follows:
in the above formula, r is 1,2,3 n Representing an n × 1 order matrix, M (k) Direct Representing the direct effect of environmental elements built into a city on traffic production or attraction within the cell, M (k) Indirect Representing the indirect effect of environmental elements built into a city on traffic production or attraction in neighbouring traffic cells, M (k) total The total effect of the built-up environment elements of the city on the traffic generation amount or the attraction amount in the traffic cell is represented.
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