| Summary: | 碩士 === 國立交通大學 === 運輸與物流管理學系 === 102 === Abstract
To facilitate the efficiency of parking searching guidance, numerous studies and systems have been conducted to guide drivers to the parking vacancy at the nearest parking lot. However, to guide drivers to search an available parking space in a large-scale parking lot, the related studies are barely proposed and applied. Especially, to meet the excessive parking demand, many large-scale parking lots have been built. How to search available parking spaces at a highly crowded parking lot at an efficient manner is essential. With advanced technologies (e.g. e-Tag), it is feasible that the system can efficiently guide users to nearest parking space to reduce their driving and walking distance.
Based on this, this study aims to propose a dynamic parking guidance control model which can optimally trade-off the system installation cost and users driving and walking distance cost. To do so, this study employs genetic algorithms to optimally determine the zoning areas of total parking space along with the corresponding number of detection devices so as to sequentially open for parking users according to the parking demandand to effectively curtail the target searching area. Based on the optimal zoning, a fuzzy logic controller is developed to dynamically determine the open timing of each parking zone based the percentage of remaining vacant parking spaces and arrival drivers. A logit model is then used to simulate the driver behaviors in choosing among opened parking zones so as to compute the driving distance and walking distance.
To demonstrate the applicability of the proposed model, two case studies of parking lots, a private and a public parking lot, are conducted. The users of the private parking lot is usually limited to employees who have long parking duration (e.g. 8 hours) and the same entrance and exit times. However, the public parking lot is open to the public those who have comparatively shorter parking duration and rather different entrance and exit times. The results confirm that the proposed model can optimally determine the zoning and provide useful guidance control to reduce drivers driving and walking distance. Additionally, the proposed model can flexibly determine the optimal zoning area according to the subjective weight settings of installation cost and user cost.
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