# Robust commuter movement inference from connected mobile devices

## TL;DR

This paper presents a robust, unsupervised approach to infer train movements and estimate commuter demand in a city-wide public transport network using noisy WiFi data from connected devices, combining clustering and classification models.

## Contribution

It introduces a novel robust clustering method for train inference and a real-time commuter pattern classification model from noisy IoT data.

## Key findings

- Achieved high accuracy on large-scale anonymized dataset
- Demonstrated effective real-time demand estimation
- Validated robustness against noisy data

## Abstract

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.