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Designing of Indoor Linkable Pedestrian Network Data Model for the Transportation Vulnerable

Published: 25 February 2018 Publication History

Abstract

There has been a recent increasing interest in guaranteeing the right for mobility of the transportation vulnerable and the demand for indoor routing services has been increasing as well. This paper proposes an indoor pedestrian network data model for navigation services for the transportation vulnerable. Based on the proposed network data model, it will be possible to design and construct indoor and outdoor network data in the future. In the proposed model, indoor ambulation facilities have been included and properties of the connecting facilities have been defined in detail, compared to the previous models.

References

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Becker, T., Nagel, C., and Kolbe, T.H., 2009. Supporting contexts for indoor navigation using a multilayered space model. In Proceedings of the Mobile Data Management: Systems, Services and Middleware, 2009. MDM'09. Tenth International Conference on (2009), IEEE, 680--685.
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Hashemi, M. and Karimi, H.A., 2015. Indoor spatial model and accessibility index for emergency evacuation of people with disabilities. Journal of Computing in Civil Engineering. 30, 4, 04015056.
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Kang, H.-K. and Li, K.-J., 2017. A Standard Indoor Spatial Data Model---OGC IndoorGML and Implementation Approaches. ISPRS International Journal of Geo-Information. 6, 4, 116.
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Kim, J., 2014. Automatic Generation of Pedestrian Level Network Data. Doctoral Thesis. Seoul National University.
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Lee, J., Bang, Y., Yu, K. 2015. Standard of network data model and the icon of mobility supporting facilities and the obstructions routing service for transportation vulnerable. In Proceedings of the Korea Society for GeoSpatial Information Science Spring Conference (Jeju, Korea, May 28-29, 2015).
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Liu, L. and Zlatanova, S., 2012. A semantic data model for indoor navigation. In Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness (2012), ACM, 1--8.
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Moon, M., Lee, Y., Yu, K., and Kim, J., 2016. Optimized Path Finding Algorithm for Walking Convenience of the People with Reduced Mobility. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography. 34, 3, 273--282.
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Park, S.-H. and Lee, J.-Y., 2009. Comparative Analysis of 3D Spatial Data Models. Journal of Korea Spatial Information Society. 17, 3, 277--285.
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Park, S., Bang, Y., and Yu, K., 2015. Techniques for updating pedestrian network data including facilities and obstructions information for transportation of vulnerable people. Sensors. 15, 9, 24466--24486.
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Cited By

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  • (2021)ASTRO: Reducing COVID-19 Exposure through Contact Prediction and AvoidanceACM Transactions on Spatial Algorithms and Systems10.1145/34904928:2(1-31)Online publication date: 30-Dec-2021
  • (2020)A data model for organizing relative semantics as images to support pedestrian navigation computationsTransactions in GIS10.1111/tgis.1266924:6(1655-1680)Online publication date: 3-Aug-2020
  • (2019)CAPRIO: Context-Aware Path Recommendation Exploiting Indoor and Outdoor Information2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.000-7(431-436)Online publication date: Jun-2019

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  1. Designing of Indoor Linkable Pedestrian Network Data Model for the Transportation Vulnerable

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    ICDSP '18: Proceedings of the 2nd International Conference on Digital Signal Processing
    February 2018
    198 pages
    ISBN:9781450364027
    DOI:10.1145/3193025
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 25 February 2018

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    Author Tags

    1. Indoor network data model
    2. Moving facility
    3. Transportation vulnerable

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    View all
    • (2021)ASTRO: Reducing COVID-19 Exposure through Contact Prediction and AvoidanceACM Transactions on Spatial Algorithms and Systems10.1145/34904928:2(1-31)Online publication date: 30-Dec-2021
    • (2020)A data model for organizing relative semantics as images to support pedestrian navigation computationsTransactions in GIS10.1111/tgis.1266924:6(1655-1680)Online publication date: 3-Aug-2020
    • (2019)CAPRIO: Context-Aware Path Recommendation Exploiting Indoor and Outdoor Information2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.000-7(431-436)Online publication date: Jun-2019

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