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Conjugate gradient cellular automaton method for simulating influence of rail transit on urban growth

A cellular automata and conjugate gradient technology, applied in design optimization/simulation, etc., to achieve the effect of robust performance and high simulation accuracy

Pending Publication Date: 2020-02-21
TONGJI UNIV
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Problems solved by technology

There are complex interactions between transportation and urban growth, but the driving factors, including urban rail transit, are not linearly related to urban growth and cannot be represented by simple linear models

Method used

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  • Conjugate gradient cellular automaton method for simulating influence of rail transit on urban growth
  • Conjugate gradient cellular automaton method for simulating influence of rail transit on urban growth
  • Conjugate gradient cellular automaton method for simulating influence of rail transit on urban growth

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specific Embodiment

[0105] The actual concrete embodiment of the present invention is as follows:

[0106] Taking the urban growth of Jinhua City from 2008 to 2018 as a case, the urban growth in this case area is as follows: figure 2 shown. To verify CA CG The effectiveness of the model in urban growth simulation, simulating the urban growth process of Jinhua City from 2008 to 2018, the results show that CA CG The model can obtain simulated urban patterns with high precision. In this case, a conjugate gradient cellular automata method for simulating the impact of rail transit on urban growth includes the following steps:

[0107] 1) First, select the satellite remote sensing image data, administrative division data, road traffic data, and rail traffic data of Jinhua City in 2008 and 2018 as the basis for training CA rule transformation and obtaining transformation probability;

[0108] 2) Classify the remote sensing images to obtain the urban pattern map ( figure 2 ), to obtain rail transi...

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Abstract

The invention relates to a conjugate gradient cellular automaton method for simulating the influence of rail transit on urban growth. The method comprises the following steps of: 1) supervising and classifying remote sensing images to obtain urban spatial pattern diagrams of initial and final years; acquiring urban rail transit and various infrastructure data; 2) acquiring a space driving factor influencing the urban pattern change; 3) sampling the city pattern graph and the space variables to obtain effective sample points; 4) training the effective sample points by using conjugate gradient to obtain a CA conversion rule; 5) obtaining the conversion probability of the land under different influences of the urban rail transit based on the conversion rule; 6) establishing a CG-based geographic CA model in combination with the cellular automaton model elements and the CG rules; 7) using a CACG model to perform land use change simulation and prediction, and performing precision evaluation; 8) outputting and storing a result. Compared with the prior art, the conjugate gradient cellular automaton method provided by the invention has the advantages that the simulation performance is better, and the prediction of urban growth under the influence of rail transit can be realized.

Description

technical field [0001] The invention relates to a method for simulating the influence of urban rail transit on urban growth, in particular to a conjugate gradient cell automata method for simulating the influence of urban rail transit on urban growth. Background technique [0002] Cellular automata (CA) is a spatially explicit model of bottom-up automatic evolution, which uses local cellular interactions and global driving variables to simulate the dynamic evolution of geographically complex systems, especially in the fields of urban expansion and land use change. Wide range of applications. In CA modeling, the state of the cell is determined by the transformation rules, so establishing the transformation rules is the core and difficult point of CA modeling, which directly determines the characteristics of the CA model and affects its simulation performance. [0003] At present, there are many methods to establish CA conversion rules, which can be divided into two categorie...

Claims

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Application Information

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IPC IPC(8): G06F30/20
Inventor 冯永玖童小华谢欢刘颂柳思聪
Owner TONGJI UNIV
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