ParkCrowd: Reliable Crowdsensing for Aggregation and Dissemination of Parking Space Information

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 insights of 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 (POI) control questions. We propose a logistic regression based method to evaluate the reliability of the crowd knowledge for real-time parking spaces information. Besides, a joint probabilistic estimator is employed to make inference of parking spaces' future availability 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 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 participation of the more reliable crowd workers by offering them higher monetary rewards.

A Low-Cost On-Street Parking Management System Based on Bluetooth Beacons

“…Apart from using probe vehicles to detect available on-street parking spaces, some researches such as [ 13 , 14 , 15 ] favor the mobile crowd sensing (MCS) model which leverages the power and wisdom of the crowd. In MagnoPark [ 13 ] proposed by Arab et al, the smartphones of pedestrians on the sidewalks serve as mobile sensors, which collect accelerometer, gyroscope, magnetometer, and GPS data.…”

“…To reward the participants for their contribution, the authors designed a rule that considers (1) the number of returned answers, (2) the distance of the returned spot to the point of interest (POI), and (3) the time taken to submit the answer, in the form of virtual currency that may be used to gain access to other services. In ParkCrowd [ 15 ] proposed by Shi et al, crowd workers use a smartphone app to report real-time parking availability information to a cloud server. The cloud server aggregates the collected data and employs a joint probabilistic estimator to infer the future availability of parking spaces based on crowdsensed knowledge.…”

Participant Service Quality Aware Data Collecting Mechanism With High Coverage for Mobile Crowdsensing

“…If n i has a high utility value, it means that participant n i can not only ensure the real-time effectiveness of sensing data when performing sensing tasks but also has a high data coverage. So the utility ζ s j (n i ) of participant n i can be obtained by (14).…”

CrowdParking: Crowdsourcing Based Parking Navigation in Autonomous Driving Era

“…In the past few years, plenty of works [13], [14], [15], [16] have beendevoted to the system design and implementation of crowdsourcing based parking navigation. Cben et al [13] took smart parking as a case study for mobile crowdsourcing and simulated this specific application scenario.…”