Estimating Common Pedestrian Routes through Indoor Path Networks using Position Traces

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Accurate information about how people commonly travel in a given large-scale building environment and which routes they take for given start and destination points is essential for applications such as indoor navigation, route prediction, and mobile work planning and logistics. In this paper, we propose methods for detecting commonly used routes by robust aggregation, clustering, and merging of indoor position traces. The developed methods overcome three specific challenges for detecting commonly used routes in an indoor setting based on position data: i) a high ratio between path-density and positioning-accuracy, ii) a flat path hierarchy, and iii) providing cost-effective scalability. Through an evaluation based on data collected by staff members at a hospital covering more than 10 hectare over three floors, we show that the proposed methods detect routes that are representative of the commonly used routes between locations. These methods are sufficiently efficient to provide common routes based on real-time data from thousands of devices simultaneously. Furthermore, we show that the methods operate robustly even on basis of noisy and coarse-grained position estimates as provided by large-scale deployable indoor Wi-Fi positioning systems, and with no prior information on building layout.
OriginalsprogEngelsk
Titel15th IEEE International Conference on Mobile Data Management : Proceedings
RedaktørerMohammad Gaber, Rui Zhang
Antal sider6
ForlagIEEE Press
Udgivelsesår2014
Sider43-48 (vol.1)
ISBN (trykt)9781479957057
DOI
StatusUdgivet - 2014
BegivenhedInternational Conference on Mobile Data Management - Brisbane, Australien
Varighed: 15 jul. 201418 jul. 2014

Konference

KonferenceInternational Conference on Mobile Data Management
LandAustralien
ByBrisbane
Periode15/07/201418/07/2014
SerietitelI E E E International Conference on Mobile Data Management. Proceedings
ISSN1551-6245

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