ParkCrowd: Reliable crowdsensing for aggregation and dissemination of parking space information

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Title: ParkCrowd: Reliable crowdsensing for aggregation and dissemination of parking space information
Authors: Shi, F
Wu, D
Arkhipov, D
Liu, Q
Regan, A
McCann, J
Item Type: Journal Article
Abstract: The scarcity of parking spaces in cities leads to a high demand for timely information about their availability. In this paper, we propose a crowdsensed parking system, namely ParkCrowd, 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. In addition, a joint probabilistic estimator is employed to infer the future availability of parking spaces based on crowdsensed knowledge. Moreover, to incentivise wider participation of crowd workers, a reliability-based incentivisation method is proposed to reward workers according to their reliability and expertise levels. The efficacy of ParkCrowd for aggregation and the dissemination of parking space information has been evaluated in both real-world tests and simulations. Our results show that the ParkCrowd system is able to accurately identify the reliability level of the crowdsensed information, estimate the potential availability of parking spaces with high accuracy, and be successful in encouraging the participation of more reliable crowd workers by offering them higher monetary rewards.
Issue Date: 10-Dec-2018
Date of Acceptance: 17-Oct-2018
ISSN: 1524-9050
Publisher: Institute of Electrical and Electronics Engineers
Journal / Book Title: IEEE Transactions on Intelligent Transportation Systems
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: 0905 Civil Engineering
1507 Transportation And Freight Services
0801 Artificial Intelligence And Image Processing
Logistics & Transportation
Publication Status: Published online
Online Publication Date: 2018-12-10
Appears in Collections:Faculty of Engineering

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