CrowdPark: A Crowdsourcing-based Parking Reservation System for Mobile Phones

Parking in crowded urban areas is a precious resource that impacts driver stress levels, daily productivity, and the environment. A reservation system that enables individuals to buy parking spots prior to leaving their home would significantly ease these concerns. However, designing an infrastructure for guaranteed parking requires extensive sensor deployment and manpower, which is expensive and time-consuming proposition. In this paper, we present CrowdPark, a crowdsourcing platform that enables users to “loosely reserve” parking spots. Unlike traditional reservation platforms where sellers are usually the owners of resources, CrowdPark achieves parking reservation by crowdsourcing information about when parking resources will be available, and using this availability information to help other users find parking spots. The design of such a crowdsourcing-based parking reservation system presents several challenges including incentive design, robustness to malicious users, and handling the spatial and temporal uncertainty due to real-world vagaries. We present novel solutions to address these challenges that combine protocol design, game-theoretic and cost-benefit analysis, sensor data processing techniques, and navigation-based tools. With a combination of simulation and real-world experiments, we show that CrowdPark can 1) effectively incentivize user participation and detect malicious users with accuracy of over 95%, and 2) handle over 95% of spatial uncertainty and achieve over 90% successful parking reservation with a few minute long waiting time.