Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations

Abstract

Taking into consideration the overlapped influences of multiple rail transit stations upon land use characteristics, this study newly develops a multi-objective land use allocation optimization model to decide the land use type and intensity of every undeveloped land block of an urban area. The new model is solved by successively utilizing the non-dominated sorting genetic algorithm and the technique for order performance by similarity to ideal solution to obtain the least biased Pareto-optimal land development scheme. The study area is an urban region around two metro stations in Beijing of China. The influencing scopes of these two stations are overlapped in part, and many of the land blocks in the study area are not yet developed. It is shown that the newly developed land use allocation optimization model is able to rationally achieve multi-objectives in coordination to the most extents for the sustainable urban development in view of the integrated effect of multiple rail transit stations.

Urban Rail Transit Network Planning Based on Particle Swarm Optimization Algorithm

“…It can be seen from the experimental results in Figure 6 that when reference [9] method is used to plan the urban rail transit network, the method does not know the overall scale of the urban rail transit network planning in advance, which makes the utilization rate of the urban rail transit network planning low; Reference [8] method in the planning process, the utilization rate of urban rail transit planning is higher than that of the planning optimization method in reference [9], and is relatively stable. With the increase of urban rail transit network planning area, the utilization rate of urban rail transit network planning tends to be stable, but the utilization rate is still not ideal; When using the planning optimization method in this paper to plan the urban rail transit network, this method not only determines the network planning scale but also establishes the urban rail transit network planning model, which simplifies the calculation process and effectively improves the utilization rate of urban rail transit network planning.…”

“…In formula (9), Z ZD represents the total number of stations on the urban rail transit line, ln represents the rounding function, and l i represents the average length of the distance between the optimal traffic stations.…”

“…e total length of the urban rail transit line is 16 kilometers, and the airport can be reached through the railway station. e studied urban rail transit network planning method based on particle swarm optimization algorithm, reference [8] method and reference [9] method are used for comparison, and the comparison results of urban rail transit network planning utilization are obtained, as shown in Figure 6.…”

“…In order to further improve the feasibility of the research method, the research method, reference [8] method, and reference [9] method are, respectively, used to carry out the comparison experiment on the repetition rate of urban rail transit network planning. e results are shown in Figure 9.…”

“…ese results are of great significance for formulating effective capacity utilization strategies of urban rail transit networks and policies to provide passengers with a high level of service. Reference [9] is put forward based on the influence of rail transit station, sustainability overlap multi-objective optimization of land use distribution, considering the multiple rail transit site overlapping influence the characteristic of land use, this article developed a new multi-objective land use allocation optimization model, to make sure every undeveloped land area city land use type and intensity.…”