Inferring Accurate Bus Trajectories from Noisy Estimated Arrival Time Records

Abstract

Urban commuting data has long been a vital source of understanding population mobility behaviour and has been widely adopted for various applications such as transport infrastructure planning and urban anomaly detection. While individual-specific transaction records (such as smart card (tap-in, tap-out) data or taxi trip records) hold a wealth of information, these are often private data available only to the service provider (e.g., taxicab operator). In this work, we explore the utility in harnessing publicly available, albeit noisy, transportation datasets, such as noisy "Estimated Time of Arrival" (ETA) records (commonly available to commuters through transit Apps or electronic signages). We first propose a framework to extract accurate individual bus trajectories from such ETA records, and present results from both a primary city (Singapore) and a secondary city (London) to validate the techniques. Finally, we quantify the upper bound on the spatiotemporal resolution, of the reconstructed trajectory outputs, achieved by our proposed technique.

Protecting Mobile Food Diaries from Getting too Personal

“…As researchers, we usually strive to enhance utility of applications and algorithms, and often use personalisation as a tool to increase utility. While this is important, an increasing body of work has also emphasized the importance of privacy preservation and the use of less sensitive data [16,20,33,55,58,59]. Personalization and privacy preservation are at the two opposite ends of the spectrum because personalisation has typically required more personal data to provide high utility, while privacy preservation aims at providing reasonable utility from the application, while preserving privacy of users from known risks.…”