Robust commuter movement inference from connected mobile devices
This work addresses the challenge of monitoring public transport infrastructure with noisy, disparate IoT data, which is incremental in applying existing robust clustering and classification methods to a new domain.
The authors tackled the problem of estimating the level of service of a city-wide public transport network using noisy IoT data, proposing robust unsupervised models for train movement inference and commuter pattern classification, and assessed accuracy on a dataset with over 10 billion records.
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.