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Internet of Things data analytics for parking availability prediction and guidance
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-7312-9089
National Road Transport Research Institute, Linköping, Sweden.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-8927-0968
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-6662-9034
2020 (English)In: European transactions on telecommunications, ISSN 1124-318X, E-ISSN 2161-3915Article in journal (Refereed) Epub ahead of print
Abstract [en]

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

Place, publisher, year, edition, pages
Wiley-Blackwell Publishing Inc., 2020.
Keywords [en]
smart parking, stochastic model, markov chain, internet of things, sumo, data analytics, autonomous cars
National Category
Transport Systems and Logistics Computer and Information Sciences
Research subject
Distributed Real-Time Systems
Identifiers
URN: urn:nbn:se:his:diva-18081DOI: 10.1002/ett.3862ISI: 000506093200001Scopus ID: 2-s2.0-85078033422OAI: oai:DiVA.org:his-18081DiVA, id: diva2:1384597
Projects
SmartPark
Funder
Vinnova, 2017-03028Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-31Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2021-01-09 00:01
Available from 2021-01-09 00:01

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Authority records BETA

Atif, YacineDing, JianguoAndler, Sten F.

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CiteExportLink to record
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