Model routing evacuation during disaster
Abstract views: 179 / PDF downloads: 78
Keywords:
Disasters, Evacuation system, Modeling, Optimization, Generalized Reduced GradientAbstract
In recent years there are many disasters occur in the whole world and particularly in Indonesia. These disasters could be manmade or natural. Therefore, it is obvious most researchers had put more attention to develop methods disaster management optimally as an effort to mitigate the disasters. Evacuation system can be regarded as a very important factor in disaster management. So far, the existing evacuation system did not involve the potential of emerging technologies. This paper addresses an optimal evacuation system model, such that, could increase safety during evacuation time, to reduce vehicle used accidents as well as congestion. Then we develop a generalized reduced gradient approach for solving the model. We solve an evacuation problem to mitigate a disaster through evacuate people around the disaster area.
References
Agra, A., Christiansen, M., Figueiredo, R., Magnus Hvattum, L., Poss, M., & Requejo, C. (2012). Layered Formulation for the Robust Vehicle Routing Problem with Time Windows. In A. R. Mahjoub, V. Markakis, I. Milis, & V. T. Paschos (Eds.), Combinatorial Optimization (Vol. 7422, pp. 249–260). Springer Berlin Heidelberg.
Campos, V., Bandeira, R., & Bandeira, A. (2012). A Method for Evacuation Route Planning in Disaster Situations. Procedia - Social and Behavioral Sciences, 54, 503–512. https://doi.org/10.1016/j.sbspro.2012.09.768
Cova, T. J., & Johnson, J. P. (2003). A network flow model for lane-based evacuation routing. Transportation Research Part A: Policy and Practice, 37(7), 579–604.
Dhamala, T. N., & Adhikari, I. M. (2018). On evacuation planning optimization problems from transit-based perspective. International Journal of Operations Research, 15(1), 29–47.
Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57(4), 1472–1483. https://doi.org/10.1016/j.cie.2009.05.009
Feng, K., & Lin, N. (2022). Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017). Transportation Research Part D: Transport and Environment, 110, 103412.
Iliopoulou, C., Konstantinidou, M. A., Kepaptsoglou, K. L., & Stathopoulos, A. (2020). ITS technologies for decision making during evacuation operations: a review. Journal of Transportation Engineering, Part A: Systems, 146(4), 4020010.
Islam, K. A., Chen, D. Q., Marathe, M., Mortveit, H., Swarup, S., & Vullikanti, A. (2022). Incorporating Fairness in Large-scale Evacuation Planning. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 3192–3201.
Kochilakis, G., Poursanidis, D., Chrysoulakis, N., Varella, V., Kotroni, V., Eftychidis, G., Lagouvardos, K., Papathanasiou, C., Karavokyros, G., & Aivazoglou, M. (2016). FLIRE DSS: A web tool for the management of floods and wildfires in urban and periurban areas. Open Geosciences, 8(1), 711–727.
Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(3), 345–358. https://doi.org/10.1016/0377-2217(92)90192-C
Minhans, A. (2008). Traffic management strategies in cases of disasters. Technische Universität Darmstadt.
Purba, D. S. D., Kontou, E., & Vogiatzis, C. (2022). Evacuation route planning for alternative fuel vehicles. Transportation Research Part C: Emerging Technologies, 143, 103837.
Sattayhatewa, P., & Ran, B. (2000). Developing a Dynamic Traffic Management Model for Nuclear Power Plane Evacuation. Proceedings of 79th Annual Meeting of TRB, Washington, DC, 2000.
Soto, M., Sevaux, M., Rossi, A., & Reinholz, A. (2017). Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem. Computers and Industrial Engineering, 107, 211–222. https://doi.org/10.1016/j.cie.2017.03.022
Starita, S., Esposito Amideo, A., & Scaparra, M. P. (2018). Assessing urban rail transit systems vulnerability: metrics vs. interdiction models. Critical Information Infrastructures Security: 12th International Conference, CRITIS 2017, Lucca, Italy, October 8-13, 2017, Revised Selected Papers 12, 144–155.
Stephen, L. M. (2007). Evaluation of different contra-flow strategies for hurricane evacuation in Charleston, South Carolina. Clemson University.
Tanaka, S., Kuwahara, M., Yoshii, T., Horiguchi, R., & Akahane, H. (2001). Estimation of travel demand and network simulators to evaluate traffic management schemes in disaster. AVENUE (an Advanced & Visual Evaluator for Road Networks in Urban ArEas), 1–16.
Tharwat, A., Elhoseny, M., Hassanien, A. E., Gabel, T., & Kumar, A. (2019). Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm. Cluster Computing, 22, 4745–4766.
UNISDR, U. (2009). Making disaster risk reduction gender sensitive: Policy and practical guidelines.
Wollenstein-Betech, S., Paschalidis, I. C., & Cassandras, C. G. (2021). Planning strategies for lane reversals in transportation networks. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2131–2136.
Xue, D., & Dong, Z. (2000). An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization. IEEE Transactions on Control Systems Technology, 8(1), 183–191.
Yazdani, M., Mojtahedi, M., Loosemore, M., Sanderson, D., & Dixit, V. (2021). Hospital evacuation modelling: A critical literature review on current knowledge and research gaps. International Journal of Disaster Risk Reduction, 66, 102627.
Zhu, Y., Li, H., Wang, Z., Li, Q., Dou, Z., Xie, W., Zhang, Z., Wang, R., & Nie, W. (2022). Optimal Evacuation Route Planning of Urban Personnel at Different Risk Levels of Flood Disasters Based on the Improved 3D Dijkstra’s Algorithm. Sustainability, 14(16), 10250