Engaging the Crowd in Sensing for Smart Mobility: A Discrete Choice Experiment

With rising numbers of people living in cities leading to increasing congestion and pollution, mobile crowdsensing applications form a potential solution to make transport systems smarter and more efficient. However, sharing data comes with the risk of private information being disclosed. Therefore, a clear incentive is necessary to motivate smart device users to share data about their activities and their environment. Taking a choice modelling approach, this study aims to identify factors related to incentives and privacy that explain choice behavior of users in crowdsensing applications. We find that the effort required by users is a main factor influencing the willingness to share data. 47% of respondents (
$\text{n}=$
125) indicated to be highly concerned about their privacy. However, the risk of re-identification was found to be the least important factor to respondents, a finding which could be explained by the Privacy Paradox. Our findings imply that a trade-off has to be made by developers of crowdsensing applications between the richness of information on one hand, and the privacy risks and participation rate of users on the other hand. We propose three practical principles for designing effective and value-sensitive crowdsensing applications for smart mobility, which are 1) Tailor-made applications, 2) Transparency by design, and 3) Ensuring attractiveness of applications. Furthermore, our study provides a basis for further research on user preferences in smart mobility applications, which will become increasingly important in the light of current challenges in the field of mobility.