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Hup-Me Inferring and Reconciling a Timeline of User Activity from Rich Smartphone Data

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Academic year: 2022

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David Montoya Serge Abiteboul Pierre Senellart

Hup-Me

Inferring and Reconciling a Timeline of User Activity from Rich Smartphone Data

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System overview

Goal

• Infer the Multimodal Itineraries traveled by a user

Input

• Smartphone Data (GPS, GSM, Wi-Fi, Accelerometer, Bluetooth)

• Transportation Network + Transit Schedules (OpenStreetMap + GTFS)

Output

• Multimodal Itinerary for modes: foot, bike, car, bus, tram, metro and train

Novelty

• Rich sensor data

• Exploits Transit Schedules

• Exploits missing data (lost GPS signal)

11/04/2015

2

Hup-Me: Inferring and Reconciling a Timeline of User Activity from Rich Smartphone Data

Transportation Network, Paris, France - CC-BY-SA Maximilian Dörrbecker, OpenStreetMap

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Algorithm and Evaluation

Transportation Network

• OpenStreetMap aligned with Transit Schedules over the Paris region

Two-Phase Algorithm

1. “Multimodal map-matching” using a Particle Filter over a Dynamic Bayesian Network 2. Transit Line Recognition from Transit

Schedules

Future Work

• Include Personal Data (e.g. calendar, mails) to predict the user’s itinerary

11/04/2015

3

Hup-Me: Inferring and Reconciling a Timeline of User Activity from Rich Smartphone Data

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