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Generation of a tailored routing network for disabled people based on collaboratively collected geodata

The generation of a routing network for disabled people inherits a number of prerequisites that need special consideration. Widespread routing applications that rely on commercial or governmental geodata sources are not feasible for this specific task, due to the lack of detailed information about f...

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Bibliographic Details
Main Authors: Neis, Pascal (Author) , Zielstra, Dennis (Author)
Format: Article (Journal)
Language:English
Published: 2014
In: Applied geography
Year: 2013, Volume: 47, Pages: 70-77
ISSN:0143-6228
DOI:10.1016/j.apgeog.2013.12.004
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.apgeog.2013.12.004
Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0143622813002804
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Author Notes:Pascal Neis, Dennis Zielstra

MARC

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520 |a The generation of a routing network for disabled people inherits a number of prerequisites that need special consideration. Widespread routing applications that rely on commercial or governmental geodata sources are not feasible for this specific task, due to the lack of detailed information about features such as sidewalks, surface conditions or road incline. In recent years the research community has experienced a strong increase in studies related to routing applications tailored to disabled people in which the lack of a sophisticated dataset played a major role. This study proposes an algorithm for the generation of a disabled people friendly routing network, based on collaboratively collected geodata provided by the OpenStreetMap (OSM) project. This new representation of a routing graph can be used in numerous applications and maps dedicated to people with disabilities. The algorithm is tested and evaluated for selected areas in Europe, resulting in newly generated extended networks that include sidewalk information. The results have shown that the success of the final implementation of the introduced algorithm depends highly on the attribute quality of the OSM dataset. 
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