  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.
