The scarcity of parking spaces in cities leads to a high demand for timely information about their availability. In this paper,
we propose a crowd sensed parking system, namely Park Crowd, to aggregate on-street and roadside parking space information reliably,
and to disseminate this information to drivers in a timely manner. Our system not only collects and disseminates basic information, such
as parking hours and price, but also provides drivers with information on the real time and future availability of parking spaces based on
aggregated crowd knowledge. To improve the reliability of the information being disseminated, we dynamically evaluate the knowledge
of crowd workers based on the veracity of their answers to a series of location-dependent point of interest control questions. We propose
a logistic regression-based method to evaluate the reliability of crowd knowledge for real-time parking space information. Moreover, to
incentivise wider participation of crowd workers, a reliability-based incentivization method is proposed to reward workers according to
their reliability and expertise levels.
Published In:IJCSN Journal Volume 8, Issue 5
Date of Publication : October 2019
Pages : 413-420
Figures :07
Tables : --
K Radha :
Asst Professor,CSE,GITAM University,Rudraram,Hyderabad,Telangana,India.
V Karthik :
Asst Professor,CSE,GITAM University,Rudraram,Hyderabad,Telangana,India.
K Manish :
IV-CSE- GITAM University,Rudraram,Hyderabad,Telangana,India
Crowd worker, veracity, park crowd
In this paper, we propose ParkCrowd, which collects and
disseminates parking space information based on crowd
knowledge. ParkCrowd offers the drivers not only the
basic information of parking spaces such as location,
hourly price and real-time vacancy status, but also makes
estimation of future availability of the parking spaces by
aggregating crowd workers' knowledge. To estimate the
reliability of the information reported by the crowd
workers, we introduce location dependent POI questions to
dynamically score the workers' expertise. Two different
probabilistic models have been proposed to infer the
collective reliability of the information for vacant parking
spaces as well as the accuracy of the estimation for the
availability level. Both real-world experiments and
simulations indicate strong performance in identifying
unreliable crowd contributed information and making
accurate estimation of parking space availability.
Moreover, ParkCrowd also implements an incentivisation
scheme to distribute rewards to crowd workers based on
their reliability and expertise level. Experiment results
indicate that the rewards are paid in alignment with
individual reliability and expertise level of the workers.
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