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  Evaluating the Reliability based Park-Crowd for Real- Time Parking Space Information  
  Authors : K Radha; Suram Kavya; V Karthik; K Manish
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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.

Suram Kavya : IV-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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