Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data

Daphne Van Leeuwen; Joost Bosman; Elenna Dugundji

doi:10.1016/j.procs.2018.04.053

Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data

Daphne Van Leeuwen^*, Joost Bosman, Elenna Dugundji

^*Corresponding author for this work

Mathematics

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

In this study we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. This OD data consists of electronic traces collected from smart phone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighborhood level. In this study we cluster the pairs by space and time to gain insight in both aspects regarding travel characteristics. We show that spatially connected clusters appear when we use a performance metric called modularity on the OD data when directionality is incorporated.

Original language	English
Pages (from-to)	359-367
Number of pages	9
Journal	Procedia Computer Science
Volume	130
Issue number	Special Issue
DOIs	https://doi.org/10.1016/j.procs.2018.04.053
Publication status	Published - Jun 2018
Event	9th International Conference on Ambient Systems, Networks and Technologies, ANT 2018 - Porto, Indonesia Duration: 8 May 2018 → 11 May 2018

Bibliographical note

Part of special issue:
The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops. Edited by Elhadi Shakshuki, Ansar Yasar

Keywords

Clustering
GPS traces
Modularity optimization
OD-matrices
Screen-lines
Travel behavior

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10.1016/j.procs.2018.04.053

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title = "Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data",

abstract = "In this study we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. This OD data consists of electronic traces collected from smart phone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighborhood level. In this study we cluster the pairs by space and time to gain insight in both aspects regarding travel characteristics. We show that spatially connected clusters appear when we use a performance metric called modularity on the OD data when directionality is incorporated.",

keywords = "Clustering, GPS traces, Modularity optimization, OD-matrices, Screen-lines, Travel behavior",

author = "{Van Leeuwen}, Daphne and Joost Bosman and Elenna Dugundji",

note = "Part of special issue: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops. Edited by Elhadi Shakshuki, Ansar Yasar; 9th International Conference on Ambient Systems, Networks and Technologies, ANT 2018 ; Conference date: 08-05-2018 Through 11-05-2018",

year = "2018",

month = jun,

doi = "10.1016/j.procs.2018.04.053",

language = "English",

volume = "130",

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TY - JOUR

T1 - Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data

AU - Van Leeuwen, Daphne

AU - Bosman, Joost

AU - Dugundji, Elenna

N1 - Part of special issue: The 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / The 8th International Conference on Sustainable Energy Information Technology (SEIT-2018) / Affiliated Workshops. Edited by Elhadi Shakshuki, Ansar Yasar

PY - 2018/6

Y1 - 2018/6

N2 - In this study we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. This OD data consists of electronic traces collected from smart phone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighborhood level. In this study we cluster the pairs by space and time to gain insight in both aspects regarding travel characteristics. We show that spatially connected clusters appear when we use a performance metric called modularity on the OD data when directionality is incorporated.

AB - In this study we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. This OD data consists of electronic traces collected from smart phone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighborhood level. In this study we cluster the pairs by space and time to gain insight in both aspects regarding travel characteristics. We show that spatially connected clusters appear when we use a performance metric called modularity on the OD data when directionality is incorporated.

KW - Clustering

KW - GPS traces

KW - Modularity optimization

KW - OD-matrices

KW - Screen-lines

KW - Travel behavior

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U2 - 10.1016/j.procs.2018.04.053

DO - 10.1016/j.procs.2018.04.053

M3 - Article

AN - SCOPUS:85051266736

SN - 1877-0509

VL - 130

SP - 359

EP - 367

JO - Procedia Computer Science

JF - Procedia Computer Science

IS - Special Issue

T2 - 9th International Conference on Ambient Systems, Networks and Technologies, ANT 2018

Y2 - 8 May 2018 through 11 May 2018

ER -

Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data

Abstract

Bibliographical note

Keywords

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