ParkBid: An Incentive Based Crowdsourced Bidding Service for Parking Reservation

• Main Authors: Noor, Shahid, Hasan, Ragib, Arora, Arsh
• Format: Conference Proceeding
• Language: English
• Subjects: Automobiles; Biological system modeling; Crowdsourcing; Investment; Maintenance engineering; Mobile Sensing; Monitoring; Parking; Pricing; Urban areas
• ISSN: 2474-2473
• DOI: 10.1109/SCC.2017.16

Finding parking at a desired spot and time is tough, especially in an urban area. Users may also want to pay more to get a parking spot during urgent need. Although the existing infrastructure-based approaches can solve this problem partially, they require a high initial investment and maintenance cost. As a consequence, deploying such approaches on a large scale in the real world is infeasible. A more economically feasible solution is using crowdsourcing-based approaches where a user near a free parking spot informs the interested users about the available parking spot in exchange for some forms of incentives. However, most of the crowdsourced approaches suffer due to the lack of proper models for price negotiation, information verification, and assurance. In this paper, we propose ParkBid, a crowdsourcing-based parking service for automobiles where the information of free parking is circulated among the interested users following a bidding process. ParkBid determines the incentives for providing any parking information based on several primitives, such as time, location, reputation, urgency, etc. Along with a detailed discussion about the challenges in ParkBid for both the users and information providers, we provide a set of policies as countermeasures. Also, we present an extensive simulation to evaluate the impact of ParkBid for users under different circumstances. Our experimental results show that users can save a significant amount of time and can have more success rate during searching for a free parking spot using ParkBid.