NDLI: The research on optimal parking space choice model in parking lots

Content Provider	IEEE Xplore Digital Library
Author	Mingkai Chen Chao Hu Tianhai Chang
Copyright Year	2011
Description	Author affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China (Chao Hu) \|\| School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China (Mingkai Chen; Tianhai Chang)
Abstract	In order to deal with the parking guidance issue of the parking guidance and information system in the parking lots, the behavior of choosing parking space was analyzed to build the optimal parking space choice model in the parking lots. Based on the specific situation of the parking lots, the habits and preferences of the drivers when parking their cars were all taken into consideration in this model. After summarizing the key factors which may influence drivers' parking space choice, a multiple attribute decision parking model is proposed to help PGIS in the parking lots choose the optimal parking space for drivers. A case is presented to verify the proposed model. The results show that the model can choose the optimal parking space and provide effective guidance for drivers. It can also take the drivers less time to find the parking space and it will take great help to the intelligent management for the parking guidance and information system, too.
Starting Page	93
Ending Page	97
File Size	114877
Page Count	5
File Format	PDF
ISBN	9781612848396
e-ISBN	9781612848402
DOI	10.1109/ICCRD.2011.5764091
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Multiple attribute decision marking Decision making The optimal parking space Cities and towns Space exploration Safety Mathematical model Parking guidance and information system (PGIS) Driver circuits
Content Type	Text
Resource Type	Article

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