| Project/Area Number |
04650474
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
交通工学・国土計画
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| Research Institution | Muroran Institute of Technology |
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Principal Investigator |
TAMURA Toru Muroran Institute of Technology, Faculty of Engineering, Associate Professor., 工学部, 助教授 (80163690)
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| Co-Investigator(Kenkyū-buntansha) |
MASUYA Yuzou Tomakomai National College of Technology Associate Professor, 助教授 (70002045)
SAITO Kazuo Muroran Institute of Technology, Faculty of Engineering, Professor, 工学部, 教授 (00001222)
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| Project Period (FY) |
1992 – 1993
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| Project Status |
Completed (Fiscal Year 1993)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1993: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1992: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | self-organizing principle / parking lot / genetic algorithms / G.A. |
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| Research Abstract |
Parking problem in urban areas is one of the most important problems now a days, and expected to be approached by some new effective ideas. In this paper, optimun location pattern of parking lot models was considered based on the self-organizing principle, and this models was applied to under-ground parking lot. In addition, desirable condition of the usage of parking lots which should be realized by a parking information system is discussed.
Normally, a linear programming model is used to derive a set of parking assignments that collectively minimizes the walking distance which is modified considering the effect of parking fee. But this model is not fit a real world.
The outline of procedure and results are as follows :
1. To apply Genetic Algorithms(GA) to the optimum location pattern of parking lots. GA include generally three genetic operators, selection, crossover and mutation. The lack of dependence on function gradients makes it more suitable to such problems, like as discrete optimization design problems and optimization design problems with non-convexities or disjointness in design space. The method is tried to apply to the optimum location pattern of parking lot in this paper. The results suggest that GA is more effective for the optimization of large size parking networks.
2. To consider a maxmum road network capacity with parking constraints. Maximum copacity of a road network is defined as the maximum number of car trips which could be loaded onto the network within given link capacity. Conventional assignment simulation type model for estimating the maxmum capacity is modified considering parking constraints as well as link capacity constraints. The model describes the mechanism that drivers park cars on roads/under-ground roads in proportion to the congestion level of off-street car parks, and roadside parking vehicles reduce link capacity.
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