Publication: Mobile crowdsourcing platform for intelligent car park systems
Issued Date
2016-02-08
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2-s2.0-84964381083
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Mahidol University
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SCOPUS
Bibliographic Citation
ICSEC 2015 - 19th International Computer Science and Engineering Conference: Hybrid Cloud Computing: A New Approach for Big Data Era. (2016)
Suggested Citation
Wantanee Viriyasitavat, Pattaraporn Sangaroonsilp, Jiranda Sumritkij, Natnaree Tarananopas Mobile crowdsourcing platform for intelligent car park systems. ICSEC 2015 - 19th International Computer Science and Engineering Conference: Hybrid Cloud Computing: A New Approach for Big Data Era. (2016). doi:10.1109/ICSEC.2015.7401398 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/43518
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Title
Mobile crowdsourcing platform for intelligent car park systems
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Abstract
© 2015 IEEE. Mobile crowdsourcing concept has attracted attentions of researchers, developers and businesses in the last decade. This exact concept has also been used in the area of Intelligent Transportation System (ITS) mainly for collecting and distributing information about the current traffic condition. In this paper, we extend the mobile crowdsourcing concept to be used for an intelligent car park system. Among several existing applications, the system proposed in this paper has been designed so that it is suitable for the car parking behavior exhibited in asian countries. To be specific, the Pick N Park system was proposed and implemented to gather and deliver real-time information about a number of car parks in crowded urban areas. The proposed system not only assists drivers in planning their journeys; but it also helps alleviate the traffic congestion problem especially in front of the shopping malls (where a line of cars waiting to enter the car park blocks other traffic). In addition to the system, we also propose a car park model that can be used to estimate the congestion level and the search time of each car park. This can be useful especially during the initial phase that the technology is introduced. The model has been evaluated by monte-carlo simulations and the results show that the proposed model can provide excellent search time estimates.