Robust commuter movement inference from connected mobile devices
| Autor: | Laura Wynter, Baoyang Song, Sebastien Blandin, Hasan A. Poonawala |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Public infrastructure Computer Science - Machine Learning Computer science Inference Context (language use) Machine Learning (stat.ML) 02 engineering and technology computer.software_genre Machine Learning (cs.LG) Computer Science - Computers and Society Statistics - Machine Learning 020204 information systems Component (UML) Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Cluster analysis Social and Information Networks (cs.SI) Data stream mining Level of service business.industry 020206 networking & telecommunications Computer Science - Social and Information Networks Software deployment Data mining Internet of Things business computer Mobile device |
| Zdroj: | ICDM Workshops |
| DOI: | 10.48550/arxiv.1903.01045 |
| Popis: | 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. Comment: International Conference on Data Mining 2018 |
| Databáze: | OpenAIRE |
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