  The preponderance of connected devices provides unprecedented opportunities
for fine-grained monitoring of the public infrastructure. However while
classical models expect high quality application-specific data streams, the
promise of the Internet of Things (IoT) is that of an abundance of disparate
and noisy datasets from connected devices. In this context, we consider the
problem of estimation of the level of service of a city-wide public transport
network. We first propose a robust unsupervised model for train movement
inference from wifi traces, via the application of robust clustering methods to
a one dimensional spatio-temporal setting. We then explore the extent to which
the demand-supply gap can be estimated from connected devices. We propose a
classification model of real-time commuter patterns, including both a batch
training phase and an online learning component. We describe our deployment
architecture and assess our system accuracy on a large-scale anonymized dataset
comprising more than 10 billion records.
