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Smart parking sensors, technologies and applications for open parking lots: a review
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0002-2078-3327
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-4871-833X
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0003-4812-4988
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 8, p. 735-741Article in journal (Refereed) Published
Abstract [en]

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This paper reviews the literature on the usage of smart parking sensors, technologies, applications and evaluate their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this paper suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018. Vol. 12, no 8, p. 735-741
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-27619DOI: 10.1049/iet-its.2017.0406ISI: 000444389300001Scopus ID: 2-s2.0-85053198237OAI: oai:DiVA.org:du-27619DiVA, id: diva2:1203966
Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2018-10-08Bibliographically approved

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Paidi, VijayFleyeh, HasanHåkansson, JohanNyberg, Roger G.

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