İç mekân navigasyon rotaları ve yayaların yürüme örüntüleri arasındaki geometrik benzerliklerin Coğrafi Bilgi Sistemi ve Çok Kriterli Karar Analizi aracılığıyla değerlendirilmesi

Yıl 2024, Cilt: 39 Sayı: 2, 1255 - 1270, 30.11.2023

Atakan Bilgili Alper Şen Melih Başaraner

https://doi.org/10.17341/gazimmfd.1209668

Öz

Navigasyon ağ modelleri aracılığıyla yayaları bir iç mekânda yönlendirirken, yayalara sunulan iç mekân rotaları, yön bulma eyleminde başarıya ulaşmaları için yayaların mekânsal bilişlerine uygun olmalı ve böylece yayaların bilişsel yüklerini azaltmalıdır. Bu açıdan, yayalara sunulan iç mekân rotaları ve onların iç mekân içerisindeki gerçek yürüyüş örüntüleri geometrik açıdan benzer olmalıdır. Bu çalışmada, literatürde sıklıkla kullanılan dört navigasyon ağ modeli çalışma alanı için oluşturulmuştur. Ardından, çalışma alanında bir kullanıcı deneyi yapılarak yayaların iç mekân yürüyüş örüntüleri toplanmış ve CBS ortamında yeniden oluşturulmuştur. Bu işlemi takiben, yayaların yürüdüğü altı segment için ağ modelleri kullanılarak iç mekân rotaları hesaplanmıştır. Daha sonra, hesaplanan iç mekân rotaları ve yayaların yürüyüş örüntüleri için çeşitli geometrik benzerlik ölçüleri hesaplanmıştır. Geometrik benzerlik ölçüleri Bulanık Analitik Hiyerarşi Süreci (BAHS) yöntemi ile ağırlıklandırılarak ağ modelleri aracılığıyla üretilen iç mekân rotaları ve yayaların yürüyüş örüntüleri geometrik benzerlik açısından İdeal Çözüme Benzerliğine Göre Tercih Sıralaması Tekniği (Technique for Order Preference by Similarity to Ideal Solution - TOPSIS) yöntemi ile karşılaştırılmıştır. Deneysel çalışmada elde edilen bulgulara göre, Orta Nokta İlişki Yapısı Segment Girişi (ONİYSG) ağ modeli, çalışma alanı için geometrik benzerlik açısından yayaların yürüyüş örüntülerine en benzer ağ modeli olarak bulunmuştur, ONİYSG ağ modelini, sırasıyla Orta Eksen Dönüşümü (OED) tabanlı ağ modeli ve Grid tabanlı ağ modeli izlemiştir. Literatürde rota uzunluğu ve dönüş sayısı kriteri için en uygun bulunan görünürlük bölümlendirmesi tabanlı Evrensel Dolaşım Ağı (EDA) ağ modeli ise geometrik benzerlik açısından en geride kalmıştır.

Anahtar Kelimeler

İç Mekan Navigasyonu, Ağ Modeli, Yürüme Örüntüleri, Geometrik Benzerlik, Çok Kriterli Karar Verme

Evaluating geometric similarities between indoor navigation routes and walking patterns of pedestrians through Geographic Information System and Multi-Criteria Decision Analysis

Öz

While guiding pedestrians in an indoor space through navigation network models, indoor routes conveyed to pedestrians should comply with their spatial reasoning to achieve success in wayfinding, thus reducing the cognitive load of pedestrians. In this respect, the indoor routes and actual walking patterns of pedestrians should be geometrically similar. In this study, four navigation network models frequently used in the literature were created for the study area. Then, a user experiment was conducted in the study area, and the walking patterns of the pedestrians were collected and regenerated in the GIS environment. Following this process, indoor routes were calculated using network models for the six segments where pedestrians walked. Then, various geometric similarity measures were calculated for the indoor routes and walking patterns of the pedestrians. The geometric similarity measures were weighted with the Fuzzy Analytical Hierarchy Process (FAHP) method, and the indoor routes were computed through network models, and the walking patterns of pedestrians were compared with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in terms of geometric similarity. According to the findings of the experimental study, the Middle Point Relation Structure Segment Entrance (MPRSSE) network model was found to be the most similar to pedestrian walking patterns for the study area in terms of geometric similarity. The MPRSSE network model was followed by the Medial Axis Transform (MAT) based network model and the Grid based network model, respectively. The visibility partitioning-based Universal Circulation Network (UCN) network model, which was found to be the most suitable for route length and number of turns criteria in the literature, fell behind in terms of geometric similarity.

Anahtar Kelimeler

Indoor Navigation, Network Model, Walking Patterns, Geometric Similarity, Multi-Criteria Decision Making

Kaynakça

• 1. Huang, H., Gartner, G., Krisp, J. M., Raubal, M., Van de Weghe, N., Location based services: Ongoing evolution and research agenda, Journal of Location Based Services, 12 (2), 63-93, 2018.
• 2. Gunduz, M., Isikdag, U., Basaraner, M., Trending technologies for indoor fm: Looking for "geo" in information, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2/W1, 277-283, 2016.
• 3. Raper, J., Gartner, G., Karimi, H., Rizos, C., Applications of location-based services: A selected review, Journal of Location Based Services, 1 (2), 89-111, 2007.
• 4. Vanclooster, A., Vanhaeren, N., Viaene, P., Ooms, K., De Cock, L., Fack, V., Van de Weghe, N., De Maeyer, P., Turn calculations for the indoor application of the fewest turns path algorithm, International Journal of Geographical Information Science, 33 (11), 2284–2304, 2019.
• 5. Rüetschi, U. J., Timpf, S., Modelling wayfinding in public transport: Network space and scene space, Spatial Cognition IV, Reasoning, Action, Interaction, Spatial Cognition 2004, Lecture Notes in Computer Science, Editörler: Barkowsky, T., Freksa, C., Knauff, M., Krieg-Brückner, B., Nebel, B., Springer, Berlin, Heidelberg, 3343, 24-41, 2005.
• 6. Fellner, I., Huang, H., Gartner, G., “Turn left after the WC, and use the lift to go to the 2nd floor”—Generation of landmark-based route instructions for indoor navigation, ISPRS International Journal of Geo-Information, 6 (6), 183, 2017.
• 7. Giudice, N. A., Walton, L. A., Worboys, M., The informatics of indoor and outdoor space: A research agenda, Proceedings of the Second ACM SIGSPATIAL International Workshop On Indoor Spatial Awareness, San Jose, CA, New York, 47-53, 2 November, 2010.
• 8. Arthur, P., Passini, R., Wayfinding: People, signs and architecture, McGraw Hill, New York, 1992.
• 9. Vanclooster, A., Van de Weghe, N., De Maeyer, P., Integrating indoor and outdoor spaces for pedestrian navigation guidance: A review, Transactions in GIS, 20 (4), 491-525, 2016.
• 10. Jamshidi, S., Ensafi, M., Pati, D., Wayfinding in interior environments: An integrative review. Frontiers in Psychology, 11, 549628, 1-24, 2020.
• 11. De Cock, L., Ooms K., Van de Weghe, N., Vanhaeren, N., Pauwels, P., De Maeyer, P., Identifying what constitutes complexity perception of decision points during indoor route guidance, International Journal of Geographical Information Science, 35 (6), 1232–1250, 2021.
• 12. Ohm, C., Müller, M., Ludwig, B., Displaying landmarks and the user’s surroundings in indoor pedestrian navigation systems, Journal of Ambient Intelligence And Smart Environments, 7 (5), 635-657, 2015.
• 13. Park, J., Goldberg, D. W., Hammond, T., A comparison of network model creation algorithms based on the quality of wayfinding results, Transactions in GIS, 24 (3), 602–622, 2020.
• 14. Afyouni, I., Ray, C., Claramunt, C., Spatial models for context-aware indoor navigation systems: A survey, Journal of Spatial Information Science, 4 (4), 85-123, 2012.
• 15. Zhou, Z., Weibel, R., Ritcher, KF., Huang, H., HIVG: A hierarchical indoor visibility-based graph for navigation guidance in multi-storey buildings, Computers, Environment And Urban Systems, 93, 101751, 2022.
• 16. Bilgili, A., Şen, A., Başaraner, M., İç mekân navigasyonu ağ modelleri: Karşılaştırmalı bir inceleme, Jeodezi ve Jeoinformasyon Dergisi, 9 (2), 108-126, 2022.
• 17. Karas, I. R., Batuk, F., Akay, A. E., Baz, I., Automatically extracting 3D models and network analysis for indoors, Innovations in 3D Geo Information Systems, Lecture Notes in Geoinformation and Cartography, Editörler: Abdul-Rahman, A., Zlatanova, S., Coors, V., Springer, Berlin, Heidelberg, 2006.
• 18. Vanclooster, A., Van de Weghe, N., Fack, V., De Maeyer, P., Comparing indoor and outdoor network models for automatically calculating turns, Journal of Location Based Services, 8 (3: 11th International Symposium on Location-Based Services), 148–165, 2014.
• 19. Lee, D. T., Medial Axis Transformation of a planar shape, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4 (4), 363–369, 1982.
• 20. Lee, J., A spatial access-oriented implementation of a 3-D GIS topological data model for urban entities, GeoInformatica, 8, 237–264, 2004.
• 21. Lee, J., A three-dimensional navigable data model to support emergency response in microspatial built-environments, Annals of the Association of American Geographers, 97 (3), 512–529, 2007.
• 22. Becker, T., Nagel, C., Kolbe, T. H., A multilayered space-event model for navigation in indoor spaces, 3D Geo-Information Sciences, Lecture Notes in Geoinformation and Cartography, Editörler: Lee, J., Zlatanova, S., Springer, Berlin, Heidelberg, 61–77, 2009.
• 23. Jamali, A., Rahman A. A., Boguslawski, P., Kumar, P., Gold, C. M., An automated 3D modeling of topological indoor navigation network, GeoJournal, 82, 157–170, 2017.
• 24. Lin, W. Y., Lin, P. H., Intelligent generation of indoor topology (i-GIT) for human indoor pathfinding based on IFC models and 3D GIS technology, Automation in Construction, 94, 340–359, 2018.
• 25. Mortari, F., Zlatanova, S., Liu, L., Clementini, E., Improved Geometric Network Model (IGNM): A novel approach for deriving connectivity graphs for indoor navigation, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II–4, 45–51, 2014.
• 26. Li, X., Claramunt, C., Ray, C., A grid graph-based model for the analysis of 2D indoor spaces, Computers, Environment and Urban Systems 34 (6), 532–540, 2010.
• 27. Wang, B., Li, H., Rezgui, Y., Bradley, A., Ong, H. N., BIM based virtual environment for fire emergency evacuation. Scientific World Journal, 2014.
• 28. Xu, M, Wei, S., Zlatanova, S., Zhang, R., BIM-based indoor path planning considering obstacles, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4, 417–423, 2017.
• 29. Xu, W., Liu, L., Zlatanova, S., Penard, W., Xiong, Q., A pedestrian tracking algorithm using grid-based indoor model, Automation in Construction, 92, 173–187, 2018.
• 30. Lewandowicz, E., Lisowski, P., Flisek, P., A modified methodology for generating indoor navigation models, ISPRS International Journal of Geo-Information, 8 (2), 60, 2019.
• 31. Turner, A., Doxa, M., O’Sullivan, D., Penn, A., From isovists to visibility graphs: A methodology for the analysis of architectural space, Environment and Planning B: Planning and Design, 28 (1), 103–121, 2001.
• 32. Pang, Y., Zhou, L., Lin, B., Lv, G., Zhang, C., Generation of navigation networks for corridor spaces based on indoor visibility map, International Journal of Geographical Information Science, 34 (1), 177–201, 2020.
• 33. Yang, L., Worboys, M., Generation of navigation graphs for indoor space, International Journal of Geographical Information Science, 29 (10), 1737–1756, 2015.
• 34. Stoffel, EP., Lorenz, B., Ohlbach, H. J., Towards a semantic spatial model for pedestrian indoor navigation, Advances in Conceptual Modeling – Foundations and Applications, ER 2007, Lecture Notes in Computer Science, Cilt 4802, Springer, Berlin, Heidelberg, 328-337, 2007.
• 35. Lee, JK., Eastman, C. M., Lee, J., Kannala, M., Jeong, YS., Computing walking distances within buildings using the Universal Circulation Network, Environment and Planning B: Planning and Design, 37 (4), 628–645, 2010.
• 36. Liu, L, Zlatanova, S., A "door-to-door" path-finding approach for indoor navigation, Proceedings of the Gi4DM 2011: GeoInformation for Disaster Management, Antalya, Turkey, 3-8 May, 2011.
• 37. Kneidl, A., Borrmann, A., Hartmann, D., Generation and use of sparse navigation graphs for microscopic pedestrian simulation models, Advanced Engineering Informatics 26 (4), 669–680, 2012.
• 38. Chehreghan, A., Abbaspour, R. A., An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps, Geocarto International, 32 (5), 471-487, 2016.
• 39. Angel S., Parent J., Civco D. L., Ten compactness properties of circles: Measuring shape in geography, The Canadian Geographer / Le Géographe Canadien, 54, 441–461, 2010.
• 40. Basaraner, M., Cetinkaya, S., Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS, International Journal of Geographical Information Science, 31 (10), 1952–1977, 2017.
• 41. Duman, H. S., Başaraner, M., Şekil göstergeleri ve topluluk öğrenmesi sınıflandırma algoritmaları ile bina detaylarının şekil karmaşıklık analizi, Geomatik, 7 (3), 197-208, 2022.
• 42. Huang, H., Kieler, B., Sester, M., Urban building usage labeling by geometric and context analyses of the footprint data. Proceedings of 26th International Cartographic Conference, Dresden, Germany, 25–30 August, 2013.
• 43. Çetinkaya S., Kartografik genelleştirmede bina dizilimlerinin karakterizasyonu ve yorumlanmasına ilişkin yeni yaklaşımlar, Doktora Tezi, Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, 2014.
• 44. Yan, H., Weibel, R., Yang, B., A multi-parameter approach to automated building grouping and generalization, Geoinformatica, 12, 73-89, 2008.
• 45. Elias, B., Extracting landmarks with data mining methods. Editörler: Kuhn, W., Worboys, M. F., Timpf, S., Spatial information Theory: Foundations of Geographic Information Science, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2825, 375–389, 2003.
• 46. Grabler, F., Agrawala, M., Sumner, R. W., Pauly, M., Automatic generation of tourist maps, ACM Transactions on Graphics, 27 (3), 1-11, 2008.
• 47. Ehrlich, D., Kemper, T., Blaes, X., Soille, P., Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability, Natural Hazards, 68, 79–95, 2013.
• 48. Ustaoglu, E., Sisman, S., Aydınoglu, A. C., Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques, Ecological Modelling, 455, 109610, 2021. 49. Saaty, T. L., The Analytic Hierarachy Process, Mcgraw Hill, New York, 1980.
• 50. Çalık A., Ergülen A., A novel fuzzy group decision making approach for buying a house in pandemic process, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (2), 821-833, 2023.
• 51. Chen, J., Clarke, K. C., Indoor cartography, Cartography and Geographic Information Science, 47 (2), 95-109, 2020.
• 52. Liu, L., Li, B., Zlatanova, S., van Oosterom, P., Indoor navigation supported by the Industry Foundation Classes (IFC): A survey, Automation in Construction, 121, 103436, 2021.
• 53. Dijkstra, E. W., A note on two problems in connexion with graphs, Numerische Mathematik, 1, 269–271, 1959.
• 54. Hölscher, C., Brösamle, M., Vrachliotis, G., Challenges in multilevel wayfinding: A case study with the space syntax technique, Environment and Planning B: Planning and Design, 39 (1), 63–82, 2012.
• 55. Li, R., Klippel, A., Wayfinding behaviors in complex buildings: The impact of environmental legibility and familiarity, Environment and Behavior, 48 (3), 482–510, 2016.
• 56. Lu, Y., Ye, Y., Can people memorize multilevel building as volumetric map? A study of multilevel atrium building, Environment and Planning B: Urban Analytics and City Science, 46 (2), 225–242, 2019.
• 57. Basaraner, M., Geometric and semantic quality assessments of building features in OpenStreetMap for some areas of Istanbul, Polish Cartographical Review, 52 (3), 94-107, 2020.
• 58. Buckley, J. J., Fuzzy hierarchical analysis, Fuzzy Sets and Systems, 17 (3), 233–247, 1985.
• 59. Forman, E., Peniwati, K., Aggregating individual judgments and priorities with the analytic hierarchy process, European Journal of Operational Research, 108 (1), 165-169, 1998.
• 60. Hwang, CL., Yoon, K., Multiple attributes decision making, Methods and Applications A State-of-the-Art Survey, Springer, Berlin, Heidelberg, 1981.
• 61. Rezaei, J. Best-worst multi-criteria decision-making method, Omega, 53, 49-57, 2015.
• 62. Vanclooster, A., Ooms, K., Viaene, P, Veerle, F., Van de Weghe, N., De Maeyer, P., Evaluating suitability of the least risk path algorithm to support cognitive wayfinding in indoor spaces: An empirical study, Applied Geography, 53, 128–140, 2014.