MADM-based smart parking guidance algorithm

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three rep...

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Bibliographic Details
Published in:PloS one 2017-12, Vol.12 (12), p.e0188283-e0188283
Main Authors: Li, Bo, Pei, Yijian, Wu, Hao, Huang, Dijiang
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Automobile parking
Availability
Biology and Life Sciences
Decision making
Decision theory
Engineering
Engineering and Technology
Game theory
Information science
International conferences
Markov chains
Markov processes
Methods
Neural networks
Parking
Parking facilities
Physical Sciences
Queues
Queuing theory
Real time
Research and Analysis Methods
Social Sciences
Studies
Time series
User satisfaction
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Summary:In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users' preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0188283