Real-time public transport service-level monitoring using passive WiFi: a spectral clustering approach for train timetable estimation

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.