| Summary: | 博士 === 國立交通大學 === 交通運輸研究所 === 87 === When a planner do not know how to start to allocate the physical items and look at the planned area map helplessly, it is wished that an analysis tool can be generated to assist planner to proceed the sketch layout task. With this tool, the planner can sincerely face to and systematically deal with the preference of citizen to generate nondominated alternatives, rather than decide the layout environment pretentiously in the office and generate few alternatives in a forced manner. Thus the next tasks, evaluation and precise design will be more meaningful.
The objective of this study is to construct an analysis tool, which is needed by the planners as mentioned above. The tool consists of two elements: a mathematical programming model and a genetic algorithm. The model is constituted from the key factors of layout problem in an increasingly complex process. The algorithm is used to find the approximating nondominated solutions for the model, and its searching ability is identified through many tests. The input data for this tool must be investigated in order to follow the preference of citizen. The alternatives generated by this tool are approximating to the Pareto optimum. We think that this tool is nearly matched with the planners'' need.
Two case studies, Sheh-Tzyy-Dao and Tan-Hai new town, are described to illustrate the application process and outputs. There are some findings in the case studies. First, the fewer types of land uses the case is, the more possible to generate only one solution. Second, the analysis area should be the appropriate enlargement of planned area. Third, the integrated strategies of land use and transportation have better performance on increasing development efficiency than individual strategies of land use or transportation. Fourth, the smaller the cell is, the more detail and clear the layout will be. Fifth, the function relationships among calculating time, string length and number of cells can be used to estimate operation time from the problem scale. Sixth, the outputs of model do reflect the planning principles and expectation, which are implied in the input parameters, and the algorithm is very stable on the solutions seeking.
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