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INTEGRATING SHORTEST-PATH ANALYSIS and MULTI-AGENT SIMULATION for CAMPUS ACCESSIBILITY EVALUATION

Year 2026, Volume: 27 Issue: 1 , 190 - 203 , 27.03.2026
https://doi.org/10.18038/estubtda.1854154
https://izlik.org/JA88ET74GF

Abstract

Physical accessibility constitutes a fundamental dimension of spatial justice, ensuring that all individuals can equally benefit from the built environment. This study aims to evaluate and quantify the accessibility challenges faced by wheelchair users within the Sivas Cumhuriyet University campus and to propose a model that objectively identifies spatial inequalities. For this purpose, the intra-campus pedestrian network was modelled using Python, and shortest-path analyses were conducted via Dijkstra’s algorithm for 1,000 randomly selected origin–destination pairs. These routes were further simulated within a multi-agent system (MAS) environment to estimate travel times and compare mobility performance between wheelchair users and able-bodied individuals. This study provides one of the first quantitative frameworks to integrate Dijkstra-based shortest-path computation and MAS-driven simulation for assessing wheelchair accessibility in outdoor environments. The resulting data were used to develop a numerical accessibility scoring system that expresses spatial disadvantage as an accessibility coefficient. The findings revealed that 85.8% of the routes were completely inaccessible for wheelchair users and that, where access was possible, travel distances were on average 8.5 times longer than those of non-disabled individuals. By establishing a reproducible and data-driven framework, the study connects the aim of promoting spatial equity with quantifiable outcomes, thereby providing a decision-support tool for campus redesign and urban accessibility planning. These findings provide a scalable analytical framework for promoting spatial equity, offering practical guidance for policymakers and urban planners seeking to improve accessibility in built environments.

References

  • [1] United Nations. Convention on the Rights of Persons with Disabilities. A/RES/61/106. UN General Assembly; 2006.
  • [2] Pineda VS. Enabling Justice: Spatializing Disability in the Built Environment. Crit Plan J. 2008; 15: 111-123.
  • [3] Oliver M. The politics of disablement: new social movements. Macmillan Education; 1990.
  • [4] Sönmez Z, Aydın CC. Network Analysis for Accessibility Problems of Individuals with Physical Disabilities: Hacettepe University Beytepe Campus Case. Geomatik 2019; 4(1): 58–67.
  • [5] Ezme Gürlek AT, Ezme AD. Barrier-Free Campus Criterias and A Comparative Evaluation on Turkey. In: 11th Int. Summit Scientific Research Congress; 15–17 December 2023; Gaziantep, Türkiye: 1164–1183.
  • [6] Kesik OA, Demirci A, Karaburun A. (2014). Analysis of sidewalks for disabled pedestrians in metropolitan cities: A case study from Şişli District in Istanbul. Doğu Coğrafya Dergisi 2014; 18(30): 135-154.
  • [7] World Health Organization. World report on disability. WHO Press; 2011.
  • [8] US Department of Justice. Americans with Disabilities Act standards for accessible design. 2010.
  • [9] Turkish Standards Institution. TS 12576: urban roads—structural preventive and sign design criteria on accessibility in sidewalks and pedestrian crossings. 2012.
  • [10] Botsis D, Panagiotopoulos E. Determination of the shortest path in the university campus of Serres using the Dijkstra and Bellman-Ford algorithms. Chorografies. 2020; 6(1): 33-42.
  • [11] Le Pira M, Distefano N, Cocuzza E, Leonardi S, Inturri G, Ignaccolo M. Pedestrian mobility and university campus accessibility: an analysis of student preferences. Eur Transp. 2024; 97: 1-15.
  • [12] Fortu ZJT, Guevarra LML, Ang MRCO, Vergara KAP. Agent-based modeling of UP Diliman intracampus pedestrian mobility using NetLogo and GIS. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2023; 48: 235-241.
  • [13] Yang Q, Dubey RK, Kalantari S. PATH-U: a data-driven agent-based wayfinding model incorporating perceived path uncertainty and cognitive strategies in unfamiliar indoor environments. Build Simul. 2025; 18: 449-471. doi:10.1007/s12273-024-1217-3
  • [14] Hsu YC. SimArch: a multi-agent system for human path simulation in architecture design. arXiv. 2018; arXiv: 1807.03760.
  • [15] Kristen M. Accessibility analysis and mapping at UBC. ArcGIS StoryMaps. Published 2021. Accessed November 15, 2024. https://storymaps.arcgis.com/stories/99ab8c7237514a859c7233f333a38ec0.
  • [16] Anderson S. Wheelchair and pedestrian accessibility on WKU campus. ArcGIS StoryMaps. Published 2024. Accessed November 12, 2024. https://storymaps.arcgis.com/stories/2faf9af60d4f47f189f1d78221b388b8.
  • [17] Özkaraca N, İnceoğlu M. Accessibility assessment in university campuses: case of Duzce University campus. Duzce Univ Bilim Teknol Derg. 2021; 9(5): 1891-1908.
  • [18] Arai Y, Kusakabe T, Niwa Y, Honma K. Evaluation of wheelchair accessibility in train stations using a spatial network. Asian Transp Stud. 2022; 8: 100067. doi:10.1016/j.eastsj.2022.100067.
  • [19] Koritsoglou K, Tsoumanis G, Patras V, Fudos I. Shortest path algorithms for pedestrian navigation systems. Information. 2022; 13(6): 269. doi:10.3390/info13060269.
  • [20] Ullrich A, Hunger F, Stavroulaki I, Bilock A, Jareteg K, Tarakanov Y, et al. A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling. Front Built Environ. 2024; 10: 1447377. doi:10.3389/fbuil.2024.1447377.
  • [21] Zhou Y, Liu XC, Chen B, Grubesic T, Wei R, Wallace D. A data-driven framework for agent-based modeling of vehicular travel using publicly available data. Comput Environ Urban Syst. 2024; 110: 102095. doi:10.1016/j.compenvurbsys.2024.102095.
  • [22] Alqahtani FK, Sherif M, Ghanem A, et al. Optimizing accessibility utilizing simulation-based framework for efficient resource allocation and scheduling for disability-friendly utilities. Sci Rep. 2025; 15: 25318. doi:10.1038/s41598-025-08221-w.
  • [23] Prandi C, Barricelli BR, Mirri S, Fogli D. Accessible wayfinding and navigation: a systematic mapping study. Univ Access Inf Soc. 2023; 22(1): 185-212. doi:10.1007/s10209-021-00843-x.
  • [24] Ma L, Brandt SA, Seipel S, Ma D. Simple agents–complex emergent path systems: agent-based modelling of pedestrian movement. Environ Plan B Urban Anal City Sci. 2024; 51(2): 479-495. doi:10.1177/23998083231184884.
  • [25] Chen K, Zhao P, Qin K, Kwan MP, Wang N. Towards healthcare access equality: understanding spatial accessibility to healthcare services for wheelchair users. Comput Environ Urban Syst. 2024; 108: 102069. doi:10.1016/j.compenvurbsys.2023.102069.
  • [26] Sivas Cumhuriyet University. General overview. Cumhuriyet University Website. Accessed March 11, 2026. https://www.cumhuriyet.edu.tr/genel-tanitim.
  • [27] Tuztaşı U, Koç P. A spatial analysis of the physical properties of Sivas Cumhuriyet University campus. J Hum Sci. 2021; 18(4): 564-577.
  • [28] Medrano FA. Effects of raster terrain representation on GIS shortest path analysis. PLoS One. 2021; 16(4) :e0250106. doi:10.1371/journal.pone.0250106.
  • [29] Tang Q, Dou W. An effective method for computing the least-cost path using a multi-resolution raster cost surface model. ISPRS Int J Geo Inf. 2023; 12(7): 287. doi:10.3390/ijgi12070287.
  • [30] Seegmiller L, Shirabe T. A method for finding least-cost corridors in three-dimensional raster space. Trans GIS. 2022; 26(2): 1098-1115. doi:10.1111/tgis.12864.
  • [31] Murekatete RM, Shirabe T, Griffin CA. An experimental analysis of least-cost path models on ordinal-scaled raster surfaces. Int J Geogr Inf Sci. 2021; 35(8): 1545-1569. doi:10.1080/13658816.2020.1753204.
  • [32] Amini M, Mirbagheri B, Matkan AA, Alimohammadi A. Enhanced least-cost path analysis for infrastructure planning: achieving a comprehensive search space with civil engineering structures. Int J Geogr Inf Sci. 2024; 38(6): 1091-1108. doi:10.1080/13658816.2024.2333922.
  • [33] Fedorov D, Kontsevik G, Bashirov R, et al. Assessing the complexity of a path search optimization method based on clustering for a transport graph. EPJ Data Sci. 2025. doi:10.1140/epjds/s13688-025-00542-0.
  • [34] Pung J, D’Souza RM, Ghosal D, Zhang M. A road network simplification algorithm that preserves topological properties. Appl Netw Sci. 2022; 7 :79. doi:10.1007/s41109-022-00521-8.
  • [35] Grujic Z, Grujic B. Optimal routing in urban road networks: a graph-based approach using Dijkstra’s algorithm. Appl Sci. 2025; 15(8): 4162. doi:10.3390/app15084162.
  • [36] Tyagi N, Singh J, Singh S, Sehra SS. A 3D model-based framework for real-time emergency evacuation using GIS and IoT devices. ISPRS Int J Geo Inf. 2024; 13(12): 445. doi:10.3390/ijgi13120445.
  • [37] Strasser B, Wagner D, Zeitz T. Space-efficient, fast and exact routing in time-dependent road networks. Algorithms. 2021; 14(3): 90. doi:10.3390/a14030090.
  • [38] Alamri A. A smart spatial routing and accessibility analysis system based on GIS for emergency response. Int J Environ Res Public Health. 2023; 20(3): 1808. doi:10.3390/ijerph20031808.
  • [39] Wooldridge M. An introduction to multiagent systems. 2nd ed. John Wiley & Sons; 2009.
  • [40] MacAl CM, North MJ. Tutorial on agent-based modelling and simulation. J Simul. 2010; 4(3): 151-162.
  • [41] Mohler BJ, Thompson WB, Creem-Regehr SH, Pick HL, Warren WH. Visual flow influences gait transition speed and preferred walking speed. Exp Brain Res. 2007; 181(2): 221-228.

INTEGRATING SHORTEST-PATH ANALYSIS and MULTI-AGENT SIMULATION for CAMPUS ACCESSIBILITY EVALUATION

Year 2026, Volume: 27 Issue: 1 , 190 - 203 , 27.03.2026
https://doi.org/10.18038/estubtda.1854154
https://izlik.org/JA88ET74GF

Abstract

Physical accessibility constitutes a fundamental dimension of spatial justice, ensuring that all individuals can equally benefit from the built environment. This study aims to evaluate and quantify the accessibility challenges faced by wheelchair users within the Sivas Cumhuriyet University campus and to propose a model that objectively identifies spatial inequalities. For this purpose, the intra-campus pedestrian network was modelled using Python, and shortest-path analyses were conducted via Dijkstra’s algorithm for 1,000 randomly selected origin–destination pairs. These routes were further simulated within a multi-agent system (MAS) environment to estimate travel times and compare mobility performance between wheelchair users and able-bodied individuals. This study provides one of the first quantitative frameworks to integrate Dijkstra-based shortest-path computation and MAS-driven simulation for assessing wheelchair accessibility in outdoor environments. The resulting data were used to develop a numerical accessibility scoring system that expresses spatial disadvantage as an accessibility coefficient. The findings revealed that 85.8% of the routes were completely inaccessible for wheelchair users and that, where access was possible, travel distances were on average 8.5 times longer than those of non-disabled individuals. By establishing a reproducible and data-driven framework, the study connects the aim of promoting spatial equity with quantifiable outcomes, thereby providing a decision-support tool for campus redesign and urban accessibility planning. These findings provide a scalable analytical framework for promoting spatial equity, offering practical guidance for policymakers and urban planners seeking to improve accessibility in built environments.

Thanks

This study was conducted as part of the master's thesis by Onur Ferdi GÜZEL, one of the authors, within the Department of Geomatics Engineering at Sivas Cumhuriyet University, Institute of Science.

References

  • [1] United Nations. Convention on the Rights of Persons with Disabilities. A/RES/61/106. UN General Assembly; 2006.
  • [2] Pineda VS. Enabling Justice: Spatializing Disability in the Built Environment. Crit Plan J. 2008; 15: 111-123.
  • [3] Oliver M. The politics of disablement: new social movements. Macmillan Education; 1990.
  • [4] Sönmez Z, Aydın CC. Network Analysis for Accessibility Problems of Individuals with Physical Disabilities: Hacettepe University Beytepe Campus Case. Geomatik 2019; 4(1): 58–67.
  • [5] Ezme Gürlek AT, Ezme AD. Barrier-Free Campus Criterias and A Comparative Evaluation on Turkey. In: 11th Int. Summit Scientific Research Congress; 15–17 December 2023; Gaziantep, Türkiye: 1164–1183.
  • [6] Kesik OA, Demirci A, Karaburun A. (2014). Analysis of sidewalks for disabled pedestrians in metropolitan cities: A case study from Şişli District in Istanbul. Doğu Coğrafya Dergisi 2014; 18(30): 135-154.
  • [7] World Health Organization. World report on disability. WHO Press; 2011.
  • [8] US Department of Justice. Americans with Disabilities Act standards for accessible design. 2010.
  • [9] Turkish Standards Institution. TS 12576: urban roads—structural preventive and sign design criteria on accessibility in sidewalks and pedestrian crossings. 2012.
  • [10] Botsis D, Panagiotopoulos E. Determination of the shortest path in the university campus of Serres using the Dijkstra and Bellman-Ford algorithms. Chorografies. 2020; 6(1): 33-42.
  • [11] Le Pira M, Distefano N, Cocuzza E, Leonardi S, Inturri G, Ignaccolo M. Pedestrian mobility and university campus accessibility: an analysis of student preferences. Eur Transp. 2024; 97: 1-15.
  • [12] Fortu ZJT, Guevarra LML, Ang MRCO, Vergara KAP. Agent-based modeling of UP Diliman intracampus pedestrian mobility using NetLogo and GIS. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2023; 48: 235-241.
  • [13] Yang Q, Dubey RK, Kalantari S. PATH-U: a data-driven agent-based wayfinding model incorporating perceived path uncertainty and cognitive strategies in unfamiliar indoor environments. Build Simul. 2025; 18: 449-471. doi:10.1007/s12273-024-1217-3
  • [14] Hsu YC. SimArch: a multi-agent system for human path simulation in architecture design. arXiv. 2018; arXiv: 1807.03760.
  • [15] Kristen M. Accessibility analysis and mapping at UBC. ArcGIS StoryMaps. Published 2021. Accessed November 15, 2024. https://storymaps.arcgis.com/stories/99ab8c7237514a859c7233f333a38ec0.
  • [16] Anderson S. Wheelchair and pedestrian accessibility on WKU campus. ArcGIS StoryMaps. Published 2024. Accessed November 12, 2024. https://storymaps.arcgis.com/stories/2faf9af60d4f47f189f1d78221b388b8.
  • [17] Özkaraca N, İnceoğlu M. Accessibility assessment in university campuses: case of Duzce University campus. Duzce Univ Bilim Teknol Derg. 2021; 9(5): 1891-1908.
  • [18] Arai Y, Kusakabe T, Niwa Y, Honma K. Evaluation of wheelchair accessibility in train stations using a spatial network. Asian Transp Stud. 2022; 8: 100067. doi:10.1016/j.eastsj.2022.100067.
  • [19] Koritsoglou K, Tsoumanis G, Patras V, Fudos I. Shortest path algorithms for pedestrian navigation systems. Information. 2022; 13(6): 269. doi:10.3390/info13060269.
  • [20] Ullrich A, Hunger F, Stavroulaki I, Bilock A, Jareteg K, Tarakanov Y, et al. A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling. Front Built Environ. 2024; 10: 1447377. doi:10.3389/fbuil.2024.1447377.
  • [21] Zhou Y, Liu XC, Chen B, Grubesic T, Wei R, Wallace D. A data-driven framework for agent-based modeling of vehicular travel using publicly available data. Comput Environ Urban Syst. 2024; 110: 102095. doi:10.1016/j.compenvurbsys.2024.102095.
  • [22] Alqahtani FK, Sherif M, Ghanem A, et al. Optimizing accessibility utilizing simulation-based framework for efficient resource allocation and scheduling for disability-friendly utilities. Sci Rep. 2025; 15: 25318. doi:10.1038/s41598-025-08221-w.
  • [23] Prandi C, Barricelli BR, Mirri S, Fogli D. Accessible wayfinding and navigation: a systematic mapping study. Univ Access Inf Soc. 2023; 22(1): 185-212. doi:10.1007/s10209-021-00843-x.
  • [24] Ma L, Brandt SA, Seipel S, Ma D. Simple agents–complex emergent path systems: agent-based modelling of pedestrian movement. Environ Plan B Urban Anal City Sci. 2024; 51(2): 479-495. doi:10.1177/23998083231184884.
  • [25] Chen K, Zhao P, Qin K, Kwan MP, Wang N. Towards healthcare access equality: understanding spatial accessibility to healthcare services for wheelchair users. Comput Environ Urban Syst. 2024; 108: 102069. doi:10.1016/j.compenvurbsys.2023.102069.
  • [26] Sivas Cumhuriyet University. General overview. Cumhuriyet University Website. Accessed March 11, 2026. https://www.cumhuriyet.edu.tr/genel-tanitim.
  • [27] Tuztaşı U, Koç P. A spatial analysis of the physical properties of Sivas Cumhuriyet University campus. J Hum Sci. 2021; 18(4): 564-577.
  • [28] Medrano FA. Effects of raster terrain representation on GIS shortest path analysis. PLoS One. 2021; 16(4) :e0250106. doi:10.1371/journal.pone.0250106.
  • [29] Tang Q, Dou W. An effective method for computing the least-cost path using a multi-resolution raster cost surface model. ISPRS Int J Geo Inf. 2023; 12(7): 287. doi:10.3390/ijgi12070287.
  • [30] Seegmiller L, Shirabe T. A method for finding least-cost corridors in three-dimensional raster space. Trans GIS. 2022; 26(2): 1098-1115. doi:10.1111/tgis.12864.
  • [31] Murekatete RM, Shirabe T, Griffin CA. An experimental analysis of least-cost path models on ordinal-scaled raster surfaces. Int J Geogr Inf Sci. 2021; 35(8): 1545-1569. doi:10.1080/13658816.2020.1753204.
  • [32] Amini M, Mirbagheri B, Matkan AA, Alimohammadi A. Enhanced least-cost path analysis for infrastructure planning: achieving a comprehensive search space with civil engineering structures. Int J Geogr Inf Sci. 2024; 38(6): 1091-1108. doi:10.1080/13658816.2024.2333922.
  • [33] Fedorov D, Kontsevik G, Bashirov R, et al. Assessing the complexity of a path search optimization method based on clustering for a transport graph. EPJ Data Sci. 2025. doi:10.1140/epjds/s13688-025-00542-0.
  • [34] Pung J, D’Souza RM, Ghosal D, Zhang M. A road network simplification algorithm that preserves topological properties. Appl Netw Sci. 2022; 7 :79. doi:10.1007/s41109-022-00521-8.
  • [35] Grujic Z, Grujic B. Optimal routing in urban road networks: a graph-based approach using Dijkstra’s algorithm. Appl Sci. 2025; 15(8): 4162. doi:10.3390/app15084162.
  • [36] Tyagi N, Singh J, Singh S, Sehra SS. A 3D model-based framework for real-time emergency evacuation using GIS and IoT devices. ISPRS Int J Geo Inf. 2024; 13(12): 445. doi:10.3390/ijgi13120445.
  • [37] Strasser B, Wagner D, Zeitz T. Space-efficient, fast and exact routing in time-dependent road networks. Algorithms. 2021; 14(3): 90. doi:10.3390/a14030090.
  • [38] Alamri A. A smart spatial routing and accessibility analysis system based on GIS for emergency response. Int J Environ Res Public Health. 2023; 20(3): 1808. doi:10.3390/ijerph20031808.
  • [39] Wooldridge M. An introduction to multiagent systems. 2nd ed. John Wiley & Sons; 2009.
  • [40] MacAl CM, North MJ. Tutorial on agent-based modelling and simulation. J Simul. 2010; 4(3): 151-162.
  • [41] Mohler BJ, Thompson WB, Creem-Regehr SH, Pick HL, Warren WH. Visual flow influences gait transition speed and preferred walking speed. Exp Brain Res. 2007; 181(2): 221-228.
There are 41 citations in total.

Details

Primary Language English
Subjects Geographical Information Systems (GIS) in Planning
Journal Section Research Article
Authors

Onur Ferdi Güzel 0009-0000-9156-6138

İsmail Ercüment Ayazlı 0000-0003-0782-5366

Hüseyin Duman 0000-0002-7340-7800

Submission Date January 2, 2026
Acceptance Date March 12, 2026
Publication Date March 27, 2026
DOI https://doi.org/10.18038/estubtda.1854154
IZ https://izlik.org/JA88ET74GF
Published in Issue Year 2026 Volume: 27 Issue: 1

Cite

AMA 1.Güzel OF, Ayazlı İE, Duman H. INTEGRATING SHORTEST-PATH ANALYSIS and MULTI-AGENT SIMULATION for CAMPUS ACCESSIBILITY EVALUATION. Estuscience - Se. 2026;27(1):190-203. doi:10.18038/estubtda.1854154