613 / 2019-04-25 21:07:43

Modeling research on urban population and post distribution based on land use and spatial form

incremental allocation,Population and employment,Building area

全体主题 > Land-use & Transportation modeling

With the rapid development of China's economy and the continuous promotion of urbanization, urban residents'travel volume is also increasing. The development of such a rapid city not only brings high travel volume, also brought to the city by the problem of unbalanced traffic and land resources. To solve these problems, you need to rely on a model of city traffic planning, traffic planning and basic data model is the population and distribution of post city traffic zone, so how to get the exact position of city traffic residential population distribution is very important. Based on the data of land use and building area, this study analyzed the relationship between land use, accessibility, building area and population post distribution. Based on the theory of spatial consumption coefficient of building area and population post and location advantage, a population and post distribution model of urban traffic district was constructed.The model assumes that there is a strong correlation between the increment of residential employment building area and the increment of population posts. Considering the impact of accessibility on the distribution of population posts, the concept of the incremental distribution weight of population posts is proposed. A spatial increment-based model of population Posts distribution is constructed. The parameters of the model are calculated by using genetic algorithm and annual data of traffic district planning in Jiangan area. After calibration, a complete population post distribution model based on spatial increment is obtained. In this study, the model was tested with test data. The results show that the relative error between the predicted and observed population positions in 70% of traffic districts is less than 30%. At the same time, the fitting degree between the predicted and observed population positions in the model is very high, and the determinant coefficients are 0.964 and 0.983, respectively. It shows that the predicted results of the model are reliable and can provide basic data support for urban traffic planning model.