  A new area in which passive WiFi analytics have promise for delivering value
is the real-time monitoring of public transport systems. One example is
determining the true (as opposed to the published) timetable of a public
transport system in real-time. In most cases, there are no other
publicly-available sources for this information. Yet, it is indispensable for
the real-time monitoring of public transport service levels. Furthermore, this
information, if accurate and temporally fine-grained, can be used for very
low-latency incident detection. In this work, we propose using spectral
clustering based on trajectories derived from passive WiFi traces of users of a
public transport system to infer the true timetable and two key performance
indicators of the transport service, namely public transport vehicle headway
and in-station dwell time. By detecting anomalous dwell times or headways, we
demonstrate that a fast and accurate real-time incident-detection procedure can
be obtained. The method we introduce makes use of the advantages of the
high-frequency WiFi data, which provides very low-latency,
universally-accessible information, while minimizing the impact of the noise in
the data.
