Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
//

Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
//

Location

 

258ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Shi:2018:10.1109/TITS.2018.2879036,
author = {Shi, F and Wu, D and Arkhipov, D and Liu, Q and Regan, A and McCann, J},
doi = {10.1109/TITS.2018.2879036},
journal = {IEEE Transactions on Intelligent Transportation Systems},
title = {ParkCrowd: Reliable crowdsensing for aggregation and dissemination of parking space information},
url = {http://dx.doi.org/10.1109/TITS.2018.2879036},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - 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.
AU - Shi,F
AU - Wu,D
AU - Arkhipov,D
AU - Liu,Q
AU - Regan,A
AU - McCann,J
DO - 10.1109/TITS.2018.2879036
PY - 2018///
SN - 1524-9050
TI - ParkCrowd: Reliable crowdsensing for aggregation and dissemination of parking space information
T2 - IEEE Transactions on Intelligent Transportation Systems
UR - http://dx.doi.org/10.1109/TITS.2018.2879036
UR - http://hdl.handle.net/10044/1/65594
ER -