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AccessVOR: A Semantically Enriched Voronoï-Based Approach for Navigation Assistance of Wheelchair Users in Indoor Environments

Published online by Cambridge University Press:  18 June 2019

Reda Yaagoubi ,
Yehia Miky Open the ORCID record for Yehia Miky [Opens in a new window]  and
Ahmed El Shouny
Reda Yaagoubi*
Affiliation:
(College of Geomatics and Surveying Engineering, IAV Hassan II, Rabat Morocco)
Yehia Miky
Affiliation:
(Department of Geomatics, Faculty of Environmental Design, King Abdulaziz University, Jeddah, Saudi Arabia) (Faculty of Engineering, Aswan University, Aswan, Egypt)
Ahmed El Shouny
Affiliation:
(Department of Geomatics, Faculty of Environmental Design, King Abdulaziz University, Jeddah, Saudi Arabia) (Survey Research Institute, National Water Research Center, Giza, Egypt)
*
(E-mail: [email protected])
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Abstract

People with physical disabilities often face many challenges due to the non-compliance of public buildings to accessibility standards. Hence, it is necessary to provide them with relevant information about the quality of access associated with the environment they plan to visit. In this paper, we propose ‘AccessVOR’ (Accessibility assessment based on VORonoï Diagram), a novel approach that aims to automatically generate an indoor navigation network and to assess its accessibility for people moving with wheelchairs based on the American with Disabilities Act Accessibility Guidelines (ADAAG). A semantically enriched spatial database is developed based on ADAAG and the Indoor Geography Markup Language (IndoorGML) standard. A Three-Dimensional (3D) navigation-graph is then generated from the various components of an indoor environment using a Voronoï Diagram. The semantics of ADAAG allow assessing the accessibility of each segment of this navigation graph. Next, a navigation cost is allocated to this graph based on the accessibility of each segment of the network graph for navigation purposes.

Keywords

Voronoï diagramData modelsMobilityNetwork analysis
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 172 - 191
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Afyouni, I., Ray, C.and Claramunt, C. (2012). Spatial models for context-aware indoor navigation systems: A survey. Journal of Spatial Information Science, 4, 85–123.Google Scholar
Beale, L., Field, K., Briggs, D., Picton, P.and Matthews, H. (2006). Mapping for Wheelchair Users: Route Navigation in Urban Spaces. The Cartographic journal, 43(1), 68–81.Google Scholar
Boguslawski, P., Mahdjoubi, L., Zverovich, V. and Fadli, F. (2016). Automated construction of variable density navigable networks in a 3D indoor environment for emergency response. Automation in Construction, 72, 115–128.Google Scholar
Da Silva, G and De Almeida, P E. M. (2007). On the shortest path problem: a new approach with fuzzy inference systems and conventional Geographic Information Systems. Proceedings of the 7th International Conference on Intelligent Systems Design and Applications, 427–432.Google Scholar
Demyen, D. and Buro, M. (2006). Efficient triangulation-based pathfinding. American Association for Artificial Intelligence, 6, 942–947.Google Scholar
Dijsktra, E. W. (1959). A note on two problems in connexion with graphs, Numerische Mathematik, 1, 269–271.Google Scholar
Doi, K., Kii, M. and Nakanishi, H. (2008). An integrated evaluation method of accessibility, quality of life, and social interaction. Environment and Planning B: Planning and Design, 35, 1098–1116.Google Scholar
Fliess-Douer, O., Vanlandewijck, Y. C., Manor, G. L.and Van, Der Woude, L. H.-V. (2010). A systematic review of wheelchair skills tests for manual wheelchair users with a spinal cord injury: towards a standardized outcome measure. Clinical Rehabilitation; 24: 867–886.Google Scholar
Goetz, M. and Zipf, A. (2011). Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments. Geo-Spatial Information Science, 14(2), 119–128.Google Scholar
Gröger, G., Kolbe, T. H., Nagel, C.and Häfele, K. H. (2012). Open Geospatial Consortium OGC City Geography Markup Language (CityGML) En-coding Standard. OGC 12-019, Version 2., 344 p.Google Scholar
Han, C. S., Law, K. H., Latombe, J. C. and Kunz, J. C. (2002). A performance-based approach to wheelchair accessible route analysis. Advanced Engineering Informatics, 16(1): 53–71.Google Scholar
International Organization for Standardization (ISO). (2011). Building construction – accessibility and usability of the built environment. ISO 21542:2011. Retrieved from: https://www.iso.org/standard/50498.htmlGoogle Scholar
Huang, D. –B.and Liu, J. -C. (2010). Study on the Optional Route Algorithm of the ArcGIS Network-Based Improved Road Traffic. Proceedings of the Second International Conference on Networks Security, Wireless Communications and Trusted Computing, Wuhan, Hubei, China, 517–520.Google Scholar
Iwarsson, S. and Stahl, A. (2003). Accessibility, usability and universal design - positioning and definition of concepts describing person-environment relationships. Disability and Rehabilitation, 25(2), 57–66.Google Scholar
Izumi, S., Kobayashi, G.and Yoshida, Y. (2008) Route Navigation Method for Disabled Access GIS in Consideration of Abilities and Psychologies. Special Issue of the Journal of Digital Information Management, 6(4), 348–354.Google Scholar
Karimi, H. A. and Ghafourian, M. (2010). Indoor routing for individuals with special needs and preferences. Transactions in GIS, 14(3), 299–329.Google Scholar
Kostic, N. and Scheider, S. (2015). Automated Generation of Indoor Accessibility Information for Mobility-Impaired Individuals. In AGILE 2015 (235–252). Springer International Publishing.Google Scholar
Krūminaitė, M. and Zlatanova, S. (2014). Indoor space subdivision for indoor navigation. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, Dallas/Fort Worth, TX, USA, 25–31.Google Scholar
Laakso, M. and Kiviniemi, A. O. (2012). The IFC standard: A review of history, development, and standardization, information technology. Journal of Information Technology in Construction, 17(9), 134–161.Google Scholar
Lee, J., Li, K. J., Zlatanova, S., Kolbe, T. H., Nagel, C.and Becker, T. (2014). OGC IndoorGML. Open Geospatial Consortium standard. Retrieved from: http://docs.opengeospatial.org/is/14-005r5/14-005r5.htmlGoogle Scholar
Liu, L. and Zlatanova, S. (2015). An Approach for Indoor Path Computation among Obstacles that Considers User Dimension. ISPRS International Journal of Geo-Information, 4(4), 2821–2841.Google Scholar
Meyers, A. R., Anderson, J. J., Miller, D. R., Shipp, K.and Hoenig, H. (2002). Barriers, facilitators, and access for wheelchair users: substantive and methodologic lessons from a pilot study of environmental effects. Social Science & Medicine, 55, 1435–1446.Google Scholar
Moussaoui, A., Pruski, A.and Maaoui, C. (2012). Virtual reality for accessibility assessment of a built environment for a wheelchair user. Technology and Disability, 24, 129–137.Google Scholar
Nasir, M., Lim, C. P., Nahavandi, S.and Creighton, D. (2014). Prediction of pedestrians’ routes within a built environment in normal conditions. Expert Systems with Applications, 41(10), 4975–4988.Google Scholar
Okabe, A., Boots, B., Sugihara, K.and Chiu, S.N. (2000). Spatial Tessellations: Concepts and Applications of Voronoï Diagrams, 2nd ed.; Wiley: Chichester, UK, 671.Google Scholar
Preston, J. and Rajé, F. (2007). Accessibility, mobility and transport-related social exclusion. Journal of Transport Geography, 15(3), 151–160.Google Scholar
Schaap, J., Zlatanova, S.and Van Oosterom, P J. M. (2011). Towards a 3D geo-data model to support pedestrian routing in multimodal public transport travel advices. Urban and Regional Data Management, UDMS Annual, 63–78.Google Scholar
Slingsby, A. and Raper, J. (2007). Navigable space in 3D city models for pedestrians. Advances in 3D Geoinformation Systems, (Peter van Oosterom, Sisi Zlatanova, Friso Penninga and Elfriede M. Fendel, Eds.), pp. 49–64, Springer Berlin Heidelberg, 2007.Google Scholar
Turner, A., Doxa, M., O'sullivan, D.and Penn, A. (2001). From isovists to visibility graphs: a methodology for the analysis of architectural space. Environmental and Planning B, 28(1), 103–121.Google Scholar
US ATBCB. (1991). Americans with Disabilities Act (ADA) Accessibility Guidelines for Buildings and Facilities. United States. Architectural and Transportation Barriers Compliance Board (US ATBCB), Federal Register, 56(173).Google Scholar
US DHUD (1994). Fair Housing Accessibility Guidelines; Questions and answers; Supplement to notice. In US Department of Housing and Urban Development (US DHD) Federal Register, June 28, 1994. (vol. 59, pp. 33362–33368). Washington, DC: U.S. Government Printing Office.Google Scholar
Welage, N. and Liu, K. P. (2011). Wheelchair accessibility of public buildings: a review of the literature. Disability and Rehabilitation: Assistive Technology, 6(1), 1–9.Google Scholar
Yaagoubi, R. and Edwards, G. (2008). Cognitive design in action: developing assistive technology for situational awareness for persons who are blind. Disability and Rehabilitation: Assistive Technology, 3(5), 241–252.Google Scholar
Yaagoubi, R., Edwards, G.and Badard, T. (2018). Élaboration d'un modèle de données spatiales à inspiration cognitive pour assister la navigation des piétons non voyants (Development of a Cognitively Enriched Spatial Data Model for Blind Pedestrians’ Navigation Assistance). Revue Internationale de Géomatique, 28(3), 339–377.Google Scholar
Yang, L. and Worboys, M. (2015). Generation of navigation graphs for indoor space. International Journal of Geographical Information Science, 29(10), 1737–1756.Google Scholar