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Open AccessArticle

A Data Model for Using OpenStreetMap to Integrate Indoor and Outdoor Route Planning

by Zhiyong Wang 1,† and Lei Niu 2,*,†
1
GIScience Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany
2
School of Surveying and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467036, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(7), 2100; https://doi.org/10.3390/s18072100
Received: 2 May 2018 / Revised: 21 June 2018 / Accepted: 27 June 2018 / Published: 30 June 2018
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor route planning, in this paper, we focus on the connections inside buildings and propose a data model, which uses OSM primitives (nodes, ways and relations) and tags to capture horizontal and vertical indoor components, as well as the connection between indoor and outdoor environments. A set of new approaches is developed to support indoor modeling and mapping. Based on the proposed data model, we present a workflow that enables automatic generation of a routing graph and provide an algorithm to calculate integrated indoor-outdoor routes. We applied our data model to a set of test cases. The application results demonstrate the capability of our data model in modeling built environments and its feasibility for the integration of indoor and outdoor navigation. View Full-Text
Keywords: route planning; indoor; outdoor; OpenStreetMap route planning; indoor; outdoor; OpenStreetMap
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Wang, Z.; Niu, L. A Data Model for Using OpenStreetMap to Integrate Indoor and Outdoor Route Planning. Sensors 2018, 18, 2100.

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