Anna Brauer

Ever since location-based services and mobile applications collecting data gathered through Global Navigation Satellite System (GNSS) positioning have become popular, concerns about location privacy have been expressed. Research has shown that human trajectory repositories containing sequences of observed locations ordered in time constitute a rich source for analyzing movement patterns, but they can also reveal sensitive personal information, such as a person’s home address. In this paper, we present a mechanism that protects visits to sensitive locations by suppressing revealing parts of trajectories. Our attack model acknowledges that the course of a trajectory, combined with spatial context information, can facilitate privacy breaches even if sensitive locations have been concealed. Thus, we introduce the concept of k-site-unidentifiability, a specialization of k-anonymity, under which a sensitive location cannot be singled out from a group of at least k sites that the trajectory could have visited. In an experimental study, we show that our method is utility-preserving and protects sensitive locations reliably even in sparsely built environments. As it can process each trajectory independently, individuals may also use our mechanism to enhance their privacy before publishing their trajectories.

Mobile activity tracking data, i.e. data collected by mobile applications that enable activity tracking based on the use of the Global Navigation Satellite Systems (GNSS), contains information on cycling in urban areas at an unprecedented spatial and temporal extent and resolution. It can be a valuable source of information about the quality of bicycling in the city. Required is a notion of quality that is derivable from plain GNSS trajectories.

In this article, we quantify urban cycling quality by estimating the fluency of cycling traffic using a large set of GNSS trajectories recorded with a mobile tracking application. Earlier studies have shown that cyclists prefer to travel continuously and without halting, i.e. fluently. Our method extracts trajectory properties that describe the stopping behaviour and dynamics of cyclists. It aggregates these properties to segments of a street network and combines them in a descriptive index. The suitability of the data to describe the cyclists’ behaviour with street-level detail is evaluated by comparison with various data from independent sources.

Our approach to characterizing cycling traffic fluency offers a novel view on the cyclability of a city that could be valuable for urban planners, application providers, and cyclists alike. We find clear indications for the data’s ability to estimate characteristics of city cycling quality correctly, despite behaviour patterns of cyclists not caused by external circumstances and the data’s inherent bias. The proposed quality measure is adaptable for different applications, e.g. as an infrastructure quality measure or as a routing criterion.

The amount of movement data that people record using their mobile phones via different tracking apps is vast. In a typical case, the data can be viewed within the app but using the data for other purposes is cumbersome for third parties, or practically impossible. One way to improve the situation is to establish an open trajectory data repository, where the users could save their movement tracks as open data. However, this data is considered personal data and the users may not be willing to share full trajectories as they might reveal, for example, their home locations. Thus, the trajectory data must be processed to minimize the amount of information that can be used to identify person while keeping the utility of the data as high as possible. We launched a survey of peoples’ opinions about sharing their movement data and what kind of privacy guarantees they would expect. Based on the preliminary results, a large part of the potential users appears to be interested in sharing their tracking data, when adequate privacy-preserving pre-processing is performed.

Activity tracking data collected by mobile applications opens up a new, data-driven perspective on monitoring cycling in the city. In this work, we demonstrate how a large set of trajectories can be used to measure the cyclability of an urban infrastructure. We achieve this by defining the cycling traffic fuency index that describes the smoothness of cycling traffic on segments of a street network. Bias, uncertainty, and the divergence of infrastructure popularity presents challenges to the method, but within these limits, the index could be applied in city planning or as a routing criterion.