short-paper

Hup-me: inferring and reconciling a timeline of user activity from rich smartphone data

Authors:

Serge Abiteboul,

Pierre SenellartAuthors Info & Claims

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 62, Pages 1 - 4

https://doi.org/10.1145/2820783.2820852

Published: 03 November 2015 Publication History

Abstract

We designed a system to infer multimodal itineraries traveled by a user from a combination of smartphone sensor data (e.g., GPS, Wi-Fi, accelerometer) and knowledge of the transport network infrastructure (e.g., road and rail maps, public transportation timetables). The system uses a Transportation network that captures the set of possible paths of this network for the modes, e.g., foot, bicycle, road_vehicle, and rail. This Transportation network is constructed from OpenStreetMap data and public transportation routes published online by transportation agencies in GTFS format. The system infers itineraries from a sequence of smartphone observations in two phases. The first phase uses a dynamic Bayesian network that models the probabilistic relationship between paths in Transportation network and sensor data. The second phase attempts to match portions recognized as road_vehicle or rail with possible public transportation routes of type bus, train, metro, or tram extracted from the GTFS source. We evaluated the performance of our system with data from users traveling over the Paris area who were asked to record data for different trips via an Android application. Itineraries were annotated with modes and public transportation routes taken and we report on the results of the recognition.

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Cited By

Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Chen XZhong XZhou SFeng Y(2023)Recognize Locomotion and Transportation Modes from Wi-Fi Traces via Lightweight Models2023 International Conference on Future Communications and Networks (FCN)10.1109/FCN60432.2023.10544151(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/FCN60432.2023.10544151
Kanellopoulou D(2022)Introduction to the special issue Underground AtmospheresAmbiances10.4000/ambiances.4273Online publication date: 17-Jan-2022
https://doi.org/10.4000/ambiances.4273
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Index Terms

Hup-me: inferring and reconciling a timeline of user activity from rich smartphone data
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2015

646 pages

ISBN:9781450339674

DOI:10.1145/2820783

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Yan Huang
University of North Texas
,
Program Chairs:
Michael Gertz
Heidelberg University, Germany
,
Matthias Renz
LMU Munich, Germany
,
Jagan Sankaranarayanan
NEC Labs

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2015

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SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

Washington, Seattle

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SIGSPATIAL '15 Paper Acceptance Rate 38 of 212 submissions, 18%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.1145/3656470
Chen XZhong XZhou SFeng Y(2023)Recognize Locomotion and Transportation Modes from Wi-Fi Traces via Lightweight Models2023 International Conference on Future Communications and Networks (FCN)10.1109/FCN60432.2023.10544151(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/FCN60432.2023.10544151
Kanellopoulou D(2022)Introduction to the special issue Underground AtmospheresAmbiances10.4000/ambiances.4273Online publication date: 17-Jan-2022
https://doi.org/10.4000/ambiances.4273
Lin YLi QLyu DWang X(2022)A Review of Wi-Fi-Based Traffic Detection Technology in the Field of Intelligent Transportation SystemsBuildings10.3390/buildings1204042812:4(428)Online publication date: 1-Apr-2022
https://doi.org/10.3390/buildings12040428
Musleh MMokbel MAbbar SRenz MSarwat M(2022)Let's speak trajectoriesProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560972(1-4)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560972
Dabbas HFriedrich B(2022)Benchmarking machine learning algorithms by inferring transportation modes from unlabeled GPS dataTransportation Research Procedia10.1016/j.trpro.2022.02.04862(383-392)Online publication date: 2022
https://doi.org/10.1016/j.trpro.2022.02.048
Kamalian MFerreira PJul E(2022)A survey on local transport mode detection on the edge of the networkApplied Intelligence10.1007/s10489-022-03214-y52:14(16021-16050)Online publication date: 19-Mar-2022
https://doi.org/10.1007/s10489-022-03214-y
Paipuri MXu YGonzález MLeclercq L(2020)Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone dataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2020.102709118(102709)Online publication date: Sep-2020
https://doi.org/10.1016/j.trc.2020.102709
Coroamă VTürk CMattern FHarle RFarrahi KLane N(2019)Exploring the usefulness of bluetooth and wifi proximity for transportation mode recognitionAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3343847(37-40)Online publication date: 9-Sep-2019
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Welch TWidita A(2019)Big data in public transportation: a review of sources and methodsTransport Reviews10.1080/01441647.2019.1616849(1-24)Online publication date: 10-May-2019
https://doi.org/10.1080/01441647.2019.1616849
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