Towards an extensive map-oriented trace basis for human mobility modeling
Autor(en): | Schwamborn, M. Aschenbruck, N. |
Stichwörter: | Extraction, Human mobility; Human Mobility; Map matching; Map-Matching; Mobility Analysis; Opportunistic Networks; Point extraction, Trace analysis; Stay Point Extraction; Trace Processing | Erscheinungsdatum: | 2017 | Herausgeber: | Institute of Electrical and Electronics Engineers Inc. | Enthalten in: | 2016 IEEE 35th International Performance Computing and Communications Conference, IPCCC 2016 | Zusammenfassung: | Human mobility analysis and modeling is a very interdisciplinary field of research. Mobility models play an important role particularly in assessing the simulative performance of mobile and opportunistic networks. The mobility traces, which most of these models are based on, however, mostly suffer from several shortcomings. In this paper, we use the extensive Lausanne Data Collection Campaign (LDCC) mobility trace as basis for further map-oriented processing. Map-matching and sensible addition of optimal routes between points mitigate problems like GPS spatial noise, anonymization, and data gaps. Moreover, stay point extraction is performed as a preparation for the analysis of elemental statistical mobility characteristics. An exemplary impact evaluation of contact statistics shows that the resulting map-oriented trace basis is indeed suited for large-scale mobility analysis and simulation. © 2016 IEEE. |
Beschreibung: | Conference of 35th IEEE International Performance Computing and Communications Conference, IPCCC 2016 ; Conference Date: 9 December 2016 Through 11 December 2016; Conference Code:126011 |
ISBN: | 9781509052523 | DOI: | 10.1109/PCCC.2016.7820643 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013395138&doi=10.1109%2fPCCC.2016.7820643&partnerID=40&md5=93f2ea4ec820a41b1bf015c90d83b9e7 |
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geprüft am 09.06.2024