research-article

Route classification using cellular handoff patterns

Authors:

Richard A. Becker,

Alexander Varshavsky,

Chris VolinskyAuthors Info & Claims

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

Pages 123 - 132

https://doi.org/10.1145/2030112.2030130

Published: 17 September 2011 Publication History

Abstract

Understanding utilization of city roads is important for urban planners. In this paper, we show how to use handoff patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Mover's Distance. Second, we present two accurate classification algorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for measuring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statistics published by a state transportation authority.

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Cited By

Perazzini SMetulini RCarpita M(2023)Statistical indicators based on mobile phone and street maps data for risk management in small urban areasStatistical Methods & Applications10.1007/s10260-023-00719-933:4(1051-1078)Online publication date: 30-Aug-2023
https://doi.org/10.1007/s10260-023-00719-9
Guan BTakbiri NGoeckel DHoumansadr APishro-Nik H(2022)Superstring-Based Sequence Obfuscation to Thwart Pattern Matching AttacksIEEE Internet of Things Journal10.1109/JIOT.2022.32039959:23(23348-23365)Online publication date: 1-Dec-2022
https://doi.org/10.1109/JIOT.2022.3203995
Phithakkitnukoon SPhithakkitnukoon S(2022)Inferring Passenger Travel Demand Using Mobile Phone CDR DataUrban Informatics Using Mobile Network Data10.1007/978-981-19-6714-6_2(17-43)Online publication date: 30-Nov-2022
https://doi.org/10.1007/978-981-19-6714-6_2
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Index Terms

Route classification using cellular handoff patterns
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications

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Published In

cover image ACM Conferences

UbiComp '11: Proceedings of the 13th international conference on Ubiquitous computing

September 2011

668 pages

ISBN:9781450306300

DOI:10.1145/2030112

General Chairs:
James Landay
University of Washington, USA & Microsoft Research Asia, China
,
Yuanchun Shi
Tsinghua University, China
,
Program Chairs:
Donald J. Patterson
University of California, Irvine, USA
,
Yvonne Rogers
Open University, UK
,
Xing Xie
Microsoft Research Asia, China

Copyright © 2011 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 ACM 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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University of Florida: University of Florida
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 September 2011

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Ubicomp '11

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Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing

September 17 - 21, 2011

Beijing, China

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Perazzini SMetulini RCarpita M(2023)Statistical indicators based on mobile phone and street maps data for risk management in small urban areasStatistical Methods & Applications10.1007/s10260-023-00719-933:4(1051-1078)Online publication date: 30-Aug-2023
https://doi.org/10.1007/s10260-023-00719-9
Guan BTakbiri NGoeckel DHoumansadr APishro-Nik H(2022)Superstring-Based Sequence Obfuscation to Thwart Pattern Matching AttacksIEEE Internet of Things Journal10.1109/JIOT.2022.32039959:23(23348-23365)Online publication date: 1-Dec-2022
https://doi.org/10.1109/JIOT.2022.3203995
Phithakkitnukoon SPhithakkitnukoon S(2022)Inferring Passenger Travel Demand Using Mobile Phone CDR DataUrban Informatics Using Mobile Network Data10.1007/978-981-19-6714-6_2(17-43)Online publication date: 30-Nov-2022
https://doi.org/10.1007/978-981-19-6714-6_2
Leiva-Araos AAllende-Cid H(2021)A Hierarchical Fuzzy-Based Correction Algorithm for the Neighboring Network Hit ProblemMathematics10.3390/math90403159:4(315)Online publication date: 5-Feb-2021
https://doi.org/10.3390/math9040315
Imai RIkeda DShingai HNagata TShigetaka K(2021)Origin-Destination Trips Generated from Operational Data of a Mobile Network for Urban Transportation PlanningJournal of Urban Planning and Development10.1061/(ASCE)UP.1943-5444.0000635147:1Online publication date: Mar-2021
https://doi.org/10.1061/(ASCE)UP.1943-5444.0000635
Taufique ARizwan AImran AArshad KZoha AAbbasi QImran M(2021)Big Data Analytics for 5G Networks: Utilities, Frameworks, Challenges, and OpportunitiesWiley 5G Ref10.1002/9781119471509.w5GRef230(1-38)Online publication date: 26-Jan-2021
https://doi.org/10.1002/9781119471509.w5GRef230
Balistrocchi MMetulini RCarpita MRanzi R(2020)Dynamic maps of human exposure to floods based on mobile phone dataNatural Hazards and Earth System Sciences10.5194/nhess-20-3485-202020:12(3485-3500)Online publication date: 17-Dec-2020
https://doi.org/10.5194/nhess-20-3485-2020
Derrmann TFrank RViti FEngel T(2020)How Road and Mobile Networks Correlate: Estimating Urban Traffic Using HandoversIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.290137321:2(521-530)Online publication date: Feb-2020
https://doi.org/10.1109/TITS.2019.2901373
Mohanty SPozdnukhov A(2020)Dynamic Origin-Destination Demand Estimation From Link Counts, Cellular Data And Travel Time DataTransportation Research Procedia10.1016/j.trpro.2020.08.20948(1722-1739)Online publication date: 2020
https://doi.org/10.1016/j.trpro.2020.08.209
Metulini RCarpita M(2020)A Spatio-Temporal Indicator for City Users Based on Mobile Phone Signals and Administrative DataSocial Indicators Research10.1007/s11205-020-02355-2Online publication date: 3-May-2020
https://doi.org/10.1007/s11205-020-02355-2
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