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Community based parking: Finding and predicting available parking spaces based on the Internet of Things and crowdsensing
Li, Bo1; Hou, Fen2; Ding, Hongwei1; Wu, Hao1
2021-12-01
Source PublicationComputers and Industrial Engineering
ABS Journal Level2
ISSN0360-8352
Volume162Pages:107755
Abstract

In smart parking guidance systems, the ability to estimate the availability of vacant parking spaces is important to make effective guidance. In this paper, we propose a general architecture for building crowdsensing-based parking guiding system, in which the occupancy state of parking lots can be detected by smart vehicles equipped with sensors and wireless communication devices, or by parking meters and parking fee-paying terminals, and the state information can be used to estimate the probability of finding available spaces for incoming smart vehicles. Five representative scenarios that can be used in such a framework were investigated. The problem to estimate the availability of parking spaces in each scenario was modeled as an M/M/c/c queuing problem with closed-form analytical solutions. The scenarios were validated in a simulation platform and their performance in various parking environments was investigated. Experimental results revealed that the crowdsensing-based parking prediction method can lead to 30.91% or more relative improvement on average estimation error than steady-state prediction in typical parking environments.

KeywordCrowd Sensing Queuing Model Simulation Smart Parking Guidance System
DOI10.1016/j.cie.2021.107755
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000720348400014
PublisherPERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
Scopus ID2-s2.0-85118180637
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Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Faculty of Science and Technology
Corresponding AuthorLi, Bo
Affiliation1.School of Information Science and Engineering, Yunnan University, Kunming, 650091, China
2.Department of Electrical and Computer Engineering, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Li, Bo,Hou, Fen,Ding, Hongwei,et al. Community based parking: Finding and predicting available parking spaces based on the Internet of Things and crowdsensing[J]. Computers and Industrial Engineering, 2021, 162, 107755.
APA Li, Bo., Hou, Fen., Ding, Hongwei., & Wu, Hao (2021). Community based parking: Finding and predicting available parking spaces based on the Internet of Things and crowdsensing. Computers and Industrial Engineering, 162, 107755.
MLA Li, Bo,et al."Community based parking: Finding and predicting available parking spaces based on the Internet of Things and crowdsensing".Computers and Industrial Engineering 162(2021):107755.
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