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Large‐scale zone‐based evacuation planning, Part II: Macroscopic and microscopic evaluations

dc.contributor.authorHasan, Mohd. Hafiz
dc.contributor.authorVan Hentenryck, Pascal
dc.date.accessioned2021-03-02T21:44:39Z
dc.date.available2022-04-02 16:44:34en
dc.date.available2021-03-02T21:44:39Z
dc.date.issued2021-03
dc.identifier.citationHasan, Mohd. Hafiz; Van Hentenryck, Pascal (2021). "Large‐scale zone‐based evacuation planning, Part II: Macroscopic and microscopic evaluations." Networks 77(2): 341-358.
dc.identifier.issn0028-3045
dc.identifier.issn1097-0037
dc.identifier.urihttps://hdl.handle.net/2027.42/166384
dc.description.abstractA companion paper introduces models and algorithms for large‐scale zone‐based evacuation planning in which each evacuation zone is assigned a path to safety and a departure time. It also shows how to combine zone‐based evacuations with contraflows and impose additional path‐convergence and nonpreemptive constraints. This paper evaluates these algorithms on a real, large‐scale case study, both from a macroscopic standpoint and through microscopic simulations under a variety of assumptions. The results quantify, for the first time, the benefits and limitations of contraflows, convergent plans, and nonpreemption, providing unique perspectives on how to deploy these algorithms in practice. They also highlight the approaches best suited to capture each of these design features and the computational burden they impose. The paper also suggests new directions for future research in zone‐based evacuation planning and beyond in order to address the fundamental challenges by emergency services around the world.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.othercolumn generation
dc.subject.othermathematical optimization
dc.subject.othertime‐expanded graphs
dc.subject.othernonpreemptive and convergent evacuations
dc.subject.otherBenders decomposition
dc.subject.otherevacuation planning and scheduling
dc.subject.othercontraflow
dc.titleLarge‐scale zone‐based evacuation planning, Part II: Macroscopic and microscopic evaluations
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelIndustrial and Operations Engineering
dc.subject.hlbsecondlevelManagement
dc.subject.hlbtoplevelEngineering
dc.subject.hlbtoplevelBusiness and Economics
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166384/1/net21980_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/166384/2/net21980.pdf
dc.identifier.doi10.1002/net.21980
dc.identifier.sourceNetworks
dc.identifier.citedreferenceA. Andreas and J.C. Smith, Decomposition algorithms for the design of a nonsimultaneous capacitated evacuation tree network, Networks 53 ( 2009 ), 91 – 103.
dc.identifier.citedreferenceD. Bish, Planning for a bus‐based evacuation, O.R. Spectrum 33 ( 2011 ), 629 – 654.
dc.identifier.citedreferenceC. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Trans. Res Part B Methodol. 28 ( 1994 ), 269 – 287.
dc.identifier.citedreferenceT. Dhamala and I. Adhikari, On evacuation planning optimization problems from transit‐based perspective, Int. J. Oper. Res. 15 ( 2018 ), 29 – 47.
dc.identifier.citedreferenceJ. Erdmann, “ SUMO’s lane‐changing model,” 2nd SUMO Conference on Modeling Mobility with Open Data. Switzerland: Springer International Publishing, 2015, pp. 105 – 123.
dc.identifier.citedreferenceC. Even, A. Schutt, and P. Van Hentenryck, A constraint programming approach for non‐preemptive evacuation scheduling, 21st International Conference on the Principles and Practice of Constraint Programming (CP‐2015), Lecture Notes in Computer Science, vol. 9255, 2015, pp. 574–591.
dc.identifier.citedreferenceM. Goerigk, B. Gruen, and P. Hessler, Combining bus evacuation with location decisions: A branch‐and‐price approach, Trans. Res. Proc. 2 ( 2014 ), 783 – 791.
dc.identifier.citedreferenceM.H. Hasan and P. Van Hentenryck, Large‐scale zone‐based evacuation planning—Part I: Models and algorithms, Networks 77 ( 2021 ), 127 – 145.
dc.identifier.citedreferenceD. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, Recent development and applications of SUMO—simulation of urban Mobility, Int. J. Adv. Syst. Measure. 5 ( 2012 ), 128 – 138.
dc.identifier.citedreferenceS. Krauß, Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics, Ph.D. thesis, University of Cologne, 1998.
dc.identifier.citedreferenceM. Li, J. Xu, L. Wei, X. Jia, and C. Sun, Modeling a risk‐based dynamic bus schedule problem under no‐notice evacuation incorporated with dynamics of disaster, supply, and demand conditions, J. Adv. Trans. 1 ( 2019 ). https://www.hindawi.com/journals/jat/2019/9848603/
dc.identifier.citedreferenceA. Maheo, P. Kilby, and P. Van Hentenryck, Benders decomposition for the design of a hub and shuttle public transit system, Transp. Sci. 53 ( 2019 ), 77 – 88.
dc.identifier.citedreferenceV. Pillac, P. Van Hentenryck, and C. Even, A conflict‐based path‐generation heuristic for evacuation planning, Trans. Res. Part B 83 ( 2016 ), 136 – 150.
dc.identifier.citedreferenceH. Zheng, Optimization of bus routing strategies for evacuation, J Adv. Trans. 48 ( 2014 ), 734 – 749.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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