Optimized evacuation route based on crowd simulation
An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed, and determines the routes in a hierarchical manner. This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results, with visualizations, demonstrate the feasibility of our evacuation route optimization method.
Keywordscrowd simulation evacuation path planning optimization
The authors thank the reviewers for their constructive comments. This work was supported in part by “the Ministry of Science and Technology of Taiwan” under Grant MOST 102-2221-E-009-083-MY3, Grant MOST 103-2221-E-009-122-MY3, and Grant MOST 104-2221-E-009-051-MY3.
- Wong, S.-K.; Wang, Y.-S.; Tang, P.-K.; Tsai, T.-Y. Optimized route for crowd evacuation. In: Pacific Graphics Short Papers. Grinspun, E.; Bickel, B.; Dobashi, Y. Eds. The Eurographics Association, 2016.Google Scholar
- Hadzic, T.; Brown, N.; Sreenan, C. J. Real-time pedestrian evacuation planning during emergency. In: Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 597–604, 2011.Google Scholar
- Wang, H.-R.; Chen, Q.-G.; Yan, J.-B.; Yuan, Z.; Liang, D. Emergency guidance evacuation in fire scene based on pathfinder. In: Proceedings of the 7th International Conference on Intelligent Computation Technology and Automation, 226–230, 2014.Google Scholar
- Desmet, A.; Gelenbe, E. Capacity based evacuation with dynamic exit signs. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, 332–337, 2014.Google Scholar
- Haworth, B.; Usman, M.; Berseth, G.; Kapadia, M.; Faloutsos, P. Code: Crowd optimized design of environments. In: Proceedings of the 29th International Conference on Computer Animation and Social Agents, 2016.Google Scholar
- Rodriguez, S.; Amato, N. Behavior-based evacuation planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, 350–355, 2010.Google Scholar
- Tsai, J.; Fridman, N.; Bowring, E.; Brown, M.; Epstein, S.; Kaminka, G.; Marsella, S.; Ogden, A.; Rika, I.; Sheel, A.; Taylor, M. E.; Wang, X.; Zilka, A.; Tambe, M. ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Vol. 2, 457–464, 2011.Google Scholar
- Inoue, Y.; Sashima, A.; Ikeda, T.; Kurumatani, K. Indoor emergency evacuation service on autonomous navigation system using mobile phone. In: Proceedings of the 2nd International Symposium on Universal Communication, 79–85, 2008.Google Scholar
- Chen, C.-Y. The design of smart building evacuation system. International Journal of Control Theory and Applications Vol. 5, No. 1, 73–80, 2012.Google Scholar
- Van den Berg, J.; Lin, M.; Manocha, D. Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceeding of the IEEE International Conference on Robotics and Automation, 1928–1935, 2008.Google Scholar
- Guy, S. J.; Chhugani, J.; Curtis, S.; Dubey, P.; Lin, M.; Manocha, D. PLEdestrians: A least-effort approach to crowd simulation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 119–128, 2010.Google Scholar
- Johansson, A.; Helbing, D.; Shukla, P. K. Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Advances in Complex Systems Vol. 10, No. supp02, 271–288, 2007.Google Scholar
- Lee, K. H.; Choi, M. G.; Hong, Q.; Lee, J. Group behavior from video: A data-driven approach to crowd simulation. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 109–118, 2007.Google Scholar
- Kapadia, M.; Pelechano, N.; Allbeck, J.; Badler, N. Virtual Crowds: Steps toward Behavioral Realism. Morgan & Claypool Publishers, 2015.Google Scholar
- Mekni, M. Hierarchical path planning for situated agents in informed virtual geographic environments. In: Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques, Article No. 30, 2010.Google Scholar
- Pettré, J.; Laumond, J. P.; Thalmann, D. A navigation graph for real-time crowd animation on multilayered and uneven terrain. In: Proceedings of the 1st International Workshop on Crowd Simulation, Vol. 43, No. 44, 194, 2005.Google Scholar
- Bayazit, O. B.; Lien, J. M.; Amato, N. M. Better group behaviors in complex environments using global roadmaps. Artificial Life 8 Vol. 8, 362, 2003.Google Scholar
- Donelson, S. M.; Gordon, C. C. 1995 matched anthropometric database of U.S. marine corps personnel: Summary statistics. Technical Report. Geo-Centers INC Newton Centre MA, 1996.Google Scholar
- Aspelin, K. Establishing pedestrian walking speeds. Portland State University, 5–25, 2005.Google Scholar
- TranSafety Inc. Study compares older and younger pedestrian walking speeds. Road Management & Engineering Journal, 1997.Google Scholar
- Zhong, J.; Cai, W.; Luo, L. Crowd evacuation planning using cartesian genetic programming and agent-based crowd modeling. In: Proceedings of the Winter Simulation Conference, 127–138, 2015.Google Scholar
- Feng, T.; Yu, L.-F.; Yeung, S.-K.; Yin, K.; Zhou, K. Crowd-driven mid-scale layout design. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 132, 2016.Google Scholar
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