چكيده به لاتين
Abstract:
Todays, with the development of cities, the increase of the population, car ownership and the transportation problems especially in (central business districts) CBD’s, the necessity of appropriate locating of land uses has increased. In addition, land uses optimization in urban design has been ignored. This can cause the transportation problems and the social costs increase. Thus, urban planners thought to change cities structure from homocentric to polycentric. So, this structure changing can decrease the number of trips toward CBD and also can improve the level of transportation facility services especially in CBD’s. As it’s been mentioned before, optimization of location and number of land uses according to transportation facilities have been ignored that can make lots of problems in urban planning system.
The main objective of this study is the decentralization of the important land uses especially in central districts of cities and transforming these kinds of land uses to other non-condensing parts of cities. To gain this objective, we should answer these questions: First, where are the suitable locations for these land uses in a city? Second, what are the optimum number of land uses in each location? In addition, in this paper, we want to find the optimum number and location for different kinds of land uses so that the transportation costs are minimized. In other words, according to the Wardrop second principle, the main objective is to minimize the total transportation costs. In this paper, for optimization of the location and the number of land uses along with reduction in the transportation costs, we develop a bi-level optimization model. The upper level of proposed model is to optimize the total transportation costs by optimizing of the location and the number of land uses. The lower level of proposed model is to simulate the users’ behavior according to changing in the number and the location of land uses. Because of constructing new land uses, new supply will produce that can be effective in the increase of supply. For solving the proposed bi-level model, the genetic algorithm has been used. Then, the bi-level optimization model was implemented in Matlab software. After that, we applied the developed model to the Sioux Falls city network data as a numerical example. Finally, the results show that the proposed model can be used as a useful approach for the optimization of the location and the number of land uses. A comparison of the total transportation costs in two cases (without and with considering of the transportation system) shows that the total transportation costs decrease in the proposed model significantly. This result confirms the better performance of the proposed model compared with other approaches. In addition, by increasing the number of land uses, differences between the value of total transportation costs has been reduced significantly compared with the homocentric and the polycentric model. The results of the proposed model for land use location and the number of supply isn’t unique and fixed. Finally, some recommendations have been presented to help experts for urban land use location design and optimization.
Keywords:Locating, land use, multicore city, bi-level model, genetic algorithm
