High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm

Evacuation of high rise building has become an issue nowadays as the modern development has increased tremendously with a very complex structure and design. The complexity and height of the building can affect the successfulness of the evacuation process, especially towards unfamiliar occupants in t...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd Sabri, Nor Amalina
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	Q Science (General) QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/18199/1/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18199/2/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.18199
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA76 Computer software
spellingShingle	Q Science (General) QA76 Computer software Mohd Sabri, Nor Amalina High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
description	Evacuation of high rise building has become an issue nowadays as the modern development has increased tremendously with a very complex structure and design. The complexity and height of the building can affect the successfulness of the evacuation process, especially towards unfamiliar occupants in the building. Generally, they only know the route taken while they enter the building. Moreover, the available evacuation map provided by the building is not showing the shortest and safest path. Subsequently, they are hard to find the optimal route to escape. Furthermore, the shortest path algorithm needed additional features to produce better result. This research aims to assist the evacuees to find the shortest path in a high rise building using a shortest path algorithm. The objective is to design and develop an evacuation route using shortest path algorithm based on the evacuation map of the building. The method involves in this research starts with abstracting the original floor plan of the high rise building into CAD format. The floor plan is an important data to be used in this study, which is to design the evacuation route of the building. However, the original floor plan is visualised into 2D layout to gather the information on nodes and weights. The information then is used to generate a directed graph in order to obtain the shortest path results through the implementation of shortest path algorithm. The main algorithms involve is Dijkstra’s algorithm and then an Ant Colony Optimization algorithm is used as hybrid versions of Dijkstra’s algorithm. As a result, the evacuation route model is able to gain the shortest path and safest path consistently between Dijkstra’s algorithms and hybrid version which is Dijkstra-Ant Colony Optimization (DACO). In conclusion, based on the results, the shortest path can be implemented into a computerized evacuation map of the high rise building which can assist evacuees in pre evacuation to find the shortest and safest path to evacuate.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohd Sabri, Nor Amalina
author_facet	Mohd Sabri, Nor Amalina
author_sort	Mohd Sabri, Nor Amalina
title	High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
title_short	High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
title_full	High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
title_fullStr	High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
title_full_unstemmed	High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm
title_sort	high rise building evacuation route model using dijkstra's algorithm
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/18199/1/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18199/2/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm.pdf
_version_	1747833916930129920
spelling	my-utem-ep.181992021-10-10T15:16:42Z High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm 2015 Mohd Sabri, Nor Amalina Q Science (General) QA76 Computer software Evacuation of high rise building has become an issue nowadays as the modern development has increased tremendously with a very complex structure and design. The complexity and height of the building can affect the successfulness of the evacuation process, especially towards unfamiliar occupants in the building. Generally, they only know the route taken while they enter the building. Moreover, the available evacuation map provided by the building is not showing the shortest and safest path. Subsequently, they are hard to find the optimal route to escape. Furthermore, the shortest path algorithm needed additional features to produce better result. This research aims to assist the evacuees to find the shortest path in a high rise building using a shortest path algorithm. The objective is to design and develop an evacuation route using shortest path algorithm based on the evacuation map of the building. The method involves in this research starts with abstracting the original floor plan of the high rise building into CAD format. The floor plan is an important data to be used in this study, which is to design the evacuation route of the building. However, the original floor plan is visualised into 2D layout to gather the information on nodes and weights. The information then is used to generate a directed graph in order to obtain the shortest path results through the implementation of shortest path algorithm. The main algorithms involve is Dijkstra’s algorithm and then an Ant Colony Optimization algorithm is used as hybrid versions of Dijkstra’s algorithm. As a result, the evacuation route model is able to gain the shortest path and safest path consistently between Dijkstra’s algorithms and hybrid version which is Dijkstra-Ant Colony Optimization (DACO). In conclusion, based on the results, the shortest path can be implemented into a computerized evacuation map of the high rise building which can assist evacuees in pre evacuation to find the shortest and safest path to evacuate. 2015 Thesis http://eprints.utem.edu.my/id/eprint/18199/ http://eprints.utem.edu.my/id/eprint/18199/1/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18199/2/High%20Rise%20Building%20Evacuation%20Route%20Model%20Using%20DIJKSTRA%27S%20Algorithm.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100060 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology 1. Aedo, I., Yu, S., Díaz, P., Acuña, P., and Onorati, T., 2012. Personalized Alert Notifications and Evacuation Routes in Indoor Environments. Sensors (Basel, Switzerland), 12 (6), pp.7804–27. 2. Agrawal, K. and Bagoria, R., 2014. Ant Colony Optimization : Efficient Way To Find Shortest Path. International Journal of Advanced Technology & Engineering Research (IJATER), 4 (3), pp.18–21. 3. Anon, 2011. Getting Started Literature Reviews : Getting Started, (00233), pp.1–3. 4. Arora, T. and Gigras, Y., 2013. A Survey of Comparison Between Various Meta -Heuristic Techniques for Path Planning Problem, 3 (2), pp.62–66. 5. Asim, M., Gopalia, R., and Swar, S., 2014. Travelling Salesman Problem Using Genetic Algorithm. 6. Bhagade, A.S. and Puranik, P. V, 2012. Artificial Bee Colony ( ABC ) Algorithm for Vehicle Routing Optimization Problem. International Journal of Soft Computing and Engineering, (2), pp.329–333. 7. Bi, H. and Gelenbe, E., 2013. Emergency and Normal Navigation in Confined Spaces, pp.1–35. 8. Bi, H. and Gelenbe, E., 2014. Routing Diverse Evacuees with Cognitive Packets. In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014. 9. Biswas, S.S., Alam, B., and Doja, M.N., 2013. Generalization of Dijkstra’s Algorithm for Extraction of Shortest Paths in Directed Multigraphs. Journal of Computer Science, 9 (3), pp.377–382. 10. Biswas, S.S., Alam, B., and Doja, M.N., 2014. A Refinement of Dijkstra’s Algorithm for Extraction of Shortest Paths in Generalized Real Time-Multigraphs. Journal of Computer Science, 10 (4), pp.593–603. 11. BOMBA, 2013. Perangkaan Kebakaran Mengikut Jenis Kebakaran, pp.2013. 12. Bu, F. and Fang, H., 2010. Shortest Path Algorithm Within Dynamic Restricted Searching Area In City Emergency Rescue. 2010 IEEE International Conference on Emergency Management and Management Sciences, (v), pp.371–374. 13. Bu, F. and Wan, J., 2010. The Application and Research on Particle Swam Optimization in Emergency Evacuation. In: Proceedings - 2010 IEEE Youth Conference on Information, Computing and Telecommunications, YC-ICT 2010. pp.287–290. 14. Casadesús-Pursals, S. and Garriga-Garzón, F., 2013. Building Evacuation: Principles for the Analysis of Basic Structures Through Dynamic Flow Networks. Journal of Industrial Engineering and Management, 6 (4), pp.831–859. 15. Chen, C.-Y., 2009. A Fuzzy-Based Approach for Smart Building Evacuation Modeling. 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), pp.1200–1203. 16. Chen, X., Kwan, M.-P., Li, Q., and Chen, J., 2012. A Model for Evacuation Risk Assessment With Consideration of Pre- and Post-Disaster Factors. Computers, Environment and Urban Systems, 36 (3), pp.207–217. 17. Cho, H.-J. and Lan, C.-L., 2009. Hybrid Shortest Path Algorithm for Vehicle Navigation. The Journal of Supercomputing, 49 (2), pp.234–247. 18. Cui, J., An, S., and Zhao, M., 2014. A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing. Mathematical Problems in Engineering, 2014. 19. Desmet, A. and Gelenbe, E., 2013a. Reactive and Proactive Congestion Management for Emergency Building Evacuation. 38th Annual IEEE Conference on Local Computer Networks. 20. Desmet, A. and Gelenbe, E., 2013b. Graph and Analytical Models for Emergency Evacuation. Future Internet, 5 (1), pp.46–55. 21. Desmet, A. and Gelenbe, E., 2014. Capacity Based Evacuation with Dynamic Exit Signs. In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014. 22. Dressler, D., 2012. Network Flow Problems Arising From Evacuation Planning, (August 2012). 23. Duan, P., Xiong, S., and Jiang, H., 2012a. Ant Colony Optimization Based on Guidance Degree for Emergency Evacuation. International Journal of Digital Content Technology and its Applications, 6 (20), pp.607–615. 24. Duan, P., Xiong, S., and Jiang, H., 2012b. Multi-objective Optimization Model Based on Heuristic Ant Colony Algorithm for Emergency Evacuation. 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp.1258–1262. 25. Ebada, R., Mesbah, S., Kosba, E., and Mahar, K., 2012. A GIS-based DSS for Evacuation Planning. In: 2012 22nd International Conference on Computer Theory and Applications, ICCTA 2012. pp.35–40. 26. Efstathiou, D., 2011. Demo Abstract : Emergency Building Evacuation Guided by a Wireless Sensor Network, pp.203–206. 27. Ekizoğlu, B., 2009. Emergency Evacuation Simulation in Istanbul Technical University, pp.370–376. 28. El-hamied, S.S.A. and Saleh, A.A.E., 2012. Survey on Using GIS in Evacuation Planning Process, 10 (8), pp.40–43. 29. Fahy, R.F., 1994. Exit 89-an Evacuation Model for High-rise Buildings-model Description and Example Applications. Fire Safety Science, 4, pp.657–668. 30. Fang, Z., Li, Q., Li, Q., Han, L.D., and Shaw, S.-L., 2013. A Space–time Efficiency Model for Optimizing Intra-intersection Vehicle–pedestrian Evacuation Movements. Transportation Research Part C: Emerging Technologies, 31, pp.112–130. 31. Fang, Z., Zong, X., Li, Q., Li, Q., and Xiong, S., 2011. Hierarchical Multi-Objective Evacuation Routing In Stadium Using Ant Colony Optimization Approach. Journal of Transport Geography, 19 (3), pp.443–451. 32. Farhah, N., Saidin, B., Chong, C.K., Choon, Y.W., and Chai, L.E., 2013. Advances in Biomedical Infrastructure 2013, 477, pp.25–35. 33. Filippoupolitis, A. and Gelenbe, E., 2009. A Distributed Decision Support System for Building Evacuation. 2009 2nd Conference on Human System Interactions, pp.323–330. 34. Filippoupolitis, A., Gorbil, G., and Gelenbe, E., 2012. Pervasive Emergency Support Systems for Building Evacuation, (March), pp.525–527. 35. Fu, H., Pel, A.J., and Hoogendoorn, S.P., 2013. Optimal Traffic Management To Ensure Emergency Evacuation Compliance. 2013 10th Ieee International Conference On Networking, Sensing And Control (ICNSC), pp.532–537. 36. Gao, C., 2013. Study of Fire Evacuation in Big Stadium Base on Performance. Research Journal of Applied Sciences, Engineering and Technology, 5 (15), pp.3946–3950. 37. Garg, P., 2011. Modeling of Evacuation Planning of Buildings Using Dynamic Exits, (June). 38. Gelenbe, E. and Gorbil, G., 2012. Wireless Networks in Emergency Management. Proceedings of the first ACM international workshop on Practical issues and applications in next generation wireless networks - PINGEN ’12, pp.1. 39. Gelenbe, E. and Wu, F.-J., 2012. Large Scale Simulation for Human Evacuation and Rescue. Computers & Mathematics with Applications, 64 (12), pp.3869–3880. 40. Goetz, M. and Zipf, A., 2012. Using Crowdsourced Geodata for Agent-Based Indoor Evacuation Simulations. ISPRS International Journal of Geo-Information, 1 (3), pp.186–208. 41. Goodwin, M., Granmo, O.C., Radianti, J., Sarshar, P., and Glimsdal, S., 2013. Ant Colony Optimisation for Planning Safe Escape Routes. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp.53–62. 42. Gottumukkala, R., Zachary, J., Kearfott, B., and Kolluru, R., 2012. Real-time Information Driven Decision Support System for Evacuation Planning. 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, pp.206–209. 43. Granmo, O., Radianti, J., Goodwin, M., and Dugdale, J., 2013. A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning, pp.63–72. 44. Gupta, M., Dua, M., and Kumar, N., 2011. CNS Using Restricted Space Algorithms for Finding A Shortest Path., pp.48–54. 45. Hadzic, T., Brown, K.N., and Sreenan, C.J., 2011. Real-Time Pedestrian Evacuation Planning During Emergency. 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pp.597–604. 46. Hamacher, H.W. and Tjandra, S.A., 2001. Mathematical Modelling of Evacuation Problems : A State of Art. Berichte des Fraunhofer ITWM, 24 (24). 47. Han, Q., 2013. Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm. Managing Emergencies Optimally Using a Random Neural Network-Based Algorithm, 5 (4), pp.515–534. 48. Haron, F., Alginahi, Y.M., Kabir, M.N., and Mohamed, A.I., 2012. Software Evaluation for Crowd Evacuation – Case Study : Al- Al - Masjid An- An - Nabawi, 9 (6), pp.128–134. 49. Höcker, M., Berkhahn, V., Kneidl, A., Borrmann, A., and Klein, W., 2010. Graph-Based Approaches for Simulating Pedestrian Dynamics In Building Models. In: ECPPM 2010 - eWork and eBusiness in Architecture, Engineering and Construction. 50. Hong, I. and Murray, A.T., 2013. Efficient Wayfinding in Complex Environments : Derivation of A Continuous Space Shortest Path. In: Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science. pp.61–66. 51. Hu, Y., Chang, Z., Sun, L., and Wang, Y., 2009. Analysis of the Shortest Repaired Path of Distribution Network Based on Dijkstra Algorithm. 2009 International Conference on Energy and Environment Technology, pp.73–76. 52. Jabbarpour, M.R., Malakooti, H., Noor, R.M., Anuar, N.B., and Khamis, N., 2014. Ant Colony Optimisation for Vehicle Traffic Systems : Applications and Challenges. International Journal Bio Inspired Computation, 6 (1), pp.32–56. 53. Jaiswal, N. and Chakrawarti, R.K., 2013. Increasing No . Of Nodes For Dijkstra Algorithm Without Degrading The Performance., 2 (3), pp.569–573. 54. Jirasingha, W. and Patvichaichod, S., 2011. Modeling Fire Evacuation of a Library Building based on the Numerical Simulation, 8 (5), pp.452–458. 55. Kabir, H., 2013. Priority based Evacuation Planning of Large Buildings. 56. Kaitao, C., Quanbo, G., and Shenan, D., 2012. Research And Design For Emergency Evacuation Instructions System Based On Wireless Sensor Network. 2012 24th Chinese Control and Decision Conference (CCDC), pp.3849–3854. 57. Kamayudi, A., 2006. Studi dan Implementasi Algoritma Dijkstra, Bellman-Ford dan Floyd-Warshall dalam Menangani Masalah Lintasan Terpendek dalam Graf. 58. Kambayashi, Y., 2013. A Review of Routing Protocols Based on Ant-Like Mobile Agents. Algorithms, 6 (3), pp.442–456. 59. Kamkarian, P. and Hexmoor, H., 2012. Crowd Evacuation for Indoor Public Spaces Using Coulomb’s Law. Advances in Artificial Intelligence, 2012, pp.1–16. 60. Kang, W., Zhu, F., Lv, Y., Xiong, G., Xie, L., and Xi, B., 2013. A Heuristic Implementation of Emergency Traffic Evacuation in Urban Areas. In: Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2013. pp.40–44. 61. Khyrina, A.F.A.S., Burairah, H., and Abd Samad, H.B., 2013. A Systems Thinking in Natural Disaster Management : Evacuation Preparedness, pp.384–389. 62. Khyrina, A.F.A.S., Hussin, B., and Basari, A.S.H., 2015. Modification of Dijkstra ’ s Algorithm for Safest and Shortest Path during Emergency Evacuation. Applied Mathematical Sciences, 9 (31), pp.1531–1541. 63. Kim, E., Xu, Y., Lee, K., Ki, J., and Lee, B., 2013. The Optimal Path Search Algorithm for Spread of Flame and Smoke in Fire Simulation, 29 (Ca), pp.193–197. 64. Kneidl, A. and Borrmann, A., 2010. How Do Pedestrians Find Their Way ? Results Of An Experimental Study With Students Compared To Simulation Results. 65. Koh, W.L. and Zhou, S., 2011. Modeling And Simulation Of Pedestrian Behaviors In Crowded Places. ACM Transactions on Modeling and Computer Simulation, 21 (3), pp.1–23. 66. Kou, J., Xiong, S., Fang, Z., Zong, X., and Chen, Z., 2013a. Multiobjective optimization of evacuation routes in stadium using superposed potential field network based ACO. Computational intelligence and neuroscience, 2013, pp.369016. 67. Kou, J., Xiong, S., Fang, Z., Zong, X., and Chen, Z., 2013b. Multiobjective Optimization of Evacuation Routes in Stadium using Superposed Potential Field Network based ACO. Computational intelligence and neuroscience, 2013, pp.369016. 68. Kruminaite, M. and Zlatanova, S., 2014. Indoor Space Subdivision for Indoor Navigation. ISA’14, pp.25–31. 69. Lagaros, N.D. and Karlaftis, M.G., 2011. A Critical Assessment Of Metaheuristics For Scheduling Emergency Infrastructure Inspections. Swarm and Evolutionary Computation, 1 (3), pp.147–163. 70. Lämmel, G., Grether, D., and Nagel, K., 2010. The Representation And Implementation Of Time-Dependent Inundation In Large-Scale Microscopic Evacuation Simulations. Transportation Research Part C: Emerging Technologies, 18 (1), pp.84–98. 71. Lee, S., Bae, J.W., Lee, J., Hong, J.H., and Moon, I.C., 2014. Simulation Experiment of Routing Strategy for Evacuees and Disaster Responders. In: Spring Simulation Multiconference. 72. Li, C. and Lin, S., 2012. EvaPlanner: An Evacuation Planner with Social-based Flocking Kinetics. Proceedings of the 18th ACM SIGKDD international …, pp.1568–1571. 73. Li, G., Zhang, L., and Wang, Z., 2014. Optimization and Planning of Emergency Evacuation Routes Considering Traffic Control. TheScientificWorldJournal, 2014, pp.164031. 74. Li, Q., Fang, Z., Li, Q., and Zong, X., 2010. Multiobjective Evacuation Route Assignment Model Based on Genetic Algorithm. 2010 18th International Conference on Geoinformatics, pp.1–5. 75. Li, W., 2014. A New Risk Assessment Method of Escape Route Based on Monte Carlo Method and Event Tree to Emergency Evacuation. Journal of Information and Computational Science, 11 (1), pp.219–227. 76. Lin, Z., Yan, Z., and Huang, C., 2012. Risk Analysis of City Emergency Evacuation based on Multi-Agent S imulation From EquatIOn ( BLD. 77. Luo, F., Cao, G., and Li, X., 2014. An Interactive Approach For Deriving Geometric Network Models In 3D Indoor Environments. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness - ISA ’14, pp.9–16. 78. Min, M. and Neupane, B.C., 2011. An Evacuation Planner Algorithm in Flat Time Graphs. Proceedings of the 5th International Confernece on Ubiquitous Information Management and Communication - ICUIMC ’11, pp.1. 79. Nabaa, M., Bertelle, C., Dutot, A., Olivier, D., and Mallet, P., 2010. Communities Detection Algorithm to Minimize Risk during an Evacuation. In: 2010 IEEE International Systems Conference Proceedings, SysCon 2010. 80. Pizzileo, B., Lino, P., Maione, B., and Maione, G., 2011. A New Algorithm for Controlling Building Evacuation by Feedback on Hazard Level and Crowd Distribution. IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, pp.434–439. 81. Qing-quan, C. and Kun, Y.A.O., 2012. Research On The Model Of Pedestrian Evacuation In Large Public Building Based On Ant Colony Algorithm. International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), pp.1682–1685. 82. Rapant, L., 2011. Application of Dynamic Graphs for the Fastest Route in a Transport Network. 83. Ribeiro, J. and Almeida, J., 2012. Using Serious Games to Train Evacuation Behaviour. 2012 7th Iberian Conference on Information Systems and Technologies (CISTI). 84. Sabet, S., Farokhi, F., and Shokouhifar, M., 2013. A Hybrid Mutation-based Artificial Bee Colony for Traveling Salesman Problem. Lecture Notes on Information Theory, 1 (3), pp.99–103. 85. Sacharidis, D. and Bouros, P., 2013. Routing Directions : Keeping it Fast and Simple. In: SIGSPATIAL ’13:Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp.164–173. 86. Sachithraj, T., Piruthiviraj, P., and Sharan, P., 2015. Comparison of Dijkstra Algorithm with Ant Colony Optimization Algorithm using Random Topology in All-Optical Network using RWA. International Journal of Research in Engineering and Technology (IJRET), pp.2319–2322. 87. Saelao, T. and Patvichaichod, S., 2012. The Computational Fluid Dynamic Simulation of Fire Evacuation from the Student Dormitory. American Journal of Applied Sciences, 9 (3), pp.429–435. 88. Shinn, T., 2013. Combining the Shortest Paths and the Bottleneck Paths Problems. Proceeding of the Thirthy-Seventh Australasian Computer Science Conference (ACSC 2014), Auckland,, New Zealand, (Acsc), pp.13–18. 89. Shweta, K. and Singh, A., 2013. An Effect and Analysis of Parameter on Ant Colony Optimization for Solving Travelling Salesman Problem. International Journal of Computer Science and Mobile Computing IJCSMC, 2 (11), pp.222–229. 90. Song, X., Zhang, Q., Sekimoto, Y., Horanont, T., Ueyama, S., and Shibasaki, R., 2013. Modeling and Probabilistic Reasoning of Population Evacuation during Large-scale Disaster. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’13, pp.1231. 91. Sukrisno, A.T. and Rahman, A., 2010. Perancangan Prototype Dynamic Exit Sign Dengan Mengembangkan Metode Floyd-Warshall Algorithm Pada Perancangan Proses Evakuasi Gedung Bertingkat, pp.1–12. 92. Theo, A., Hassane, M.S., and Xuan, Z., 2012. Global Optimization algorithms. Polytech Nice Sophia, (June), pp.1–26. 93. Thippeswamy, Hanumanthappa, and Manjaiah, 2010. A Study on Contrast and Comparison between Bellman-Ford algorithm and Dijkstra ’ s Algorithm. National Conference on Wireless Networks-09 (NCOWN), (August). 94. Vanclooster, A., Viaene, P., Van de Weghe, N., Fack, V., and De Maeyer, P., 2013. Analyzing the Applicability of the Least Risk Path Algorithm in Indoor Space. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-4/W1 (December), pp.19–26. 95. Verma, M. and Thakur, M.K., 2010. Architectural Space Planning using Genetic Algorithms. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), pp.268–275. 96. Wagoum, a. U.K., Seyfried, A., and Holl, S., 2011. Modelling Dynamic Route Choice of Pedestrians to Assess the Criticality of Building Evacuation. Safety Science, pp.15. 97. Wang, J.W., Wang, H.F., Zhang, W.J., Ip, W.H., and Furuta, K., 2013. Evacuation Planning Based on the Contraflow Technique With Consideration of Evacuation Priorities and Traffic Setup Time. IEEE Transactions on Intelligent Transportation System, 14 (1), pp.480–485. 98. Wang, L. and Liu, W., 2011. Navigability and Reachability Index for Emergency Navigation Systems Using Wireless Sensor Networks. Tsinghua Science & Technology, 16 (6), pp.657–668. 99. Wang, T., Huang, R., Li, L., Xu, W., and Nie, J., 2011. The Application of the Shortest Path Algorithm in the Evacuation System. The Application of the Shortest Path Algorithm in the Evacuation System, pp.250–253. 100. Wang, X. and Liu, H., 2012. An Evacuation Algorithm for Large Buildings *, (10), pp.2497–2502. 101. Wenxue, C. and Zihui, Z., 2010. Path Selection Evaluation for Emergency Transport Vehicles. 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM), pp.1050–1054. 102. Winter, S., Gan, H., Shi, M., and Richter, K.-F., 2011. Get Me Out of Here : Collaborative Evacuation Based on Local Knowledge. In: Third International Workshop on Indoor Spatial Awareness. pp.35–42. 103. Wu, C.-H. and Chen, L.-C., 2012. 3D Spatial Information For Fire-Fighting Search And Rescue Route Analysis Within Buildings. Fire Safety Journal, 48, pp.21–29. 104. Wu, Z., Jiang, G., Zhang, C., and Tang, Y., 2010. Traffic Organization Method for Emergency Evacuation based on Information Centrality. 2010 2nd International Conference on Advanced Computer Control, pp.92–96. 105. Xiao, J. and Lu, F., 2010. An Improvement of The Shortest Path Algorithm Based On Dijkstra Algorithm. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), pp.383–385. 106. Xu, Y., Wang, Z., Zheng, Q., and Han, Z., 2012. The Application of Dijkstra’s Algorithm in the Intelligent Fire Evacuation System. 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp.3–6. 107. Yamada, T., 1996. A Network Flow Approach to a City Emergency Evacuation Planning. International Journal of Systems Science. 108. Yang, B., Wu, Y., and Wang, C., 2009. A Multi-agent and GIS Based Simulation for Emergency Evacuation in Park and Public Square. 2009 International Conference on Computational Intelligence and Security, pp.211–216. 109. Yang, B., Wu, Y.G., and Ren, B., 2012. Application of multi-resolution modelling in emergency evacuation simulation. International Journal Simulation and Process Modelling, 7 (January), pp.9–10. 110. Yang, S. and Li, C., 2010. An Enhanced Routing Method with Dijkstra Algorithm and AHP Analysis in GIS-based Emergency Plan. In: 2010 18th International Conference on Geoinformatics, Geoinformatics 2010. 111. Yuan, Y. and Wang, D., 2009. Path Selection Model and Algorithm For Emergency Logistics Management. Computers & Industrial Engineering, 56 (3), pp.1081–1094. 112. Yussof, S., Razali, R.A., and See, O.H., 2011. An Investigation of Using Parallel Genetic Algorithm for Solving the Shortest Path Routing Problem, 7 (2), pp.206–215. 113. Zakzouk, A.A.A., Zaher, H.M., and El-Deen, R.A.Z., 2010. An Ant Colony Optimization Approach for Solving Shortest Path Problem with Fuzzy Constraints. Informatics and Systems (INFOS), 2010 The 7th International Conference on. 114. Zhang, H., Liu, H., Zhang, K., and Wang, J., 2010. Modeling of Evacuations to No-Notice Event by Public Transit System, pp.480–484. 115. Zheng, Y., Ling, H., Xue, J., Chen, S., and Member, S., 2013. Population Classification in Fire Evacuation : A Multiobjective Particle Swarm Optimization Approach. IEEE Transactions on Evolutionary Computing, (c), pp.1–12. 116. Zhou, Y., 2009. Runtime Analysis of an Ant Colony Optimization Algorithm for TSP Instances. IEEE Transactions on Evolutionary Computation, 13 (5), pp.1083–1092. 117. Zong, X., Xiong, S., and Fang, Z., 2013. A Conflict-congestion Model for Pedestrian-vehicle Mixed Evacuation based on Discrete Particle Swarm Optimization Algorithm. Computers & Operations Research, 44, pp.1–12. 118. Zong, X., Xiong, S., Fang, Z., and Li, Q., 2010. Multi-Ant Colony System For Evacuation Routing Problem With Mixed Traffic Flow. IEEE Congress on Evolutionary Computation, pp.1–6.

High Rise Building Evacuation Route Model Using DIJKSTRA'S Algorithm

Similar Items