Stochastic Multi-Objective Evacuation Model under Managed and Unmanaged policies

Oren E. Nahum, Yuval Hadas, Mariano Zanini, Carlo Pellegrino, Riccardo Rossi, Massimiliano Gastaldi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Natural and man-created disasters, such as hurricanes, earthquakes, tsunamis, accidents and terrorist attacks, require evacuation and assistance routes. Evacuation routes are mostly based on the capacities of the road network. However, in extreme cases, such as earthquakes, road network infrastructure may adversely be affected, and may not supply their required capacities. If for various situations, the potential damage for critical roads can be identified in advance, it is possible to develop an evacuation model, that can be used in various situations. This paper focuses on the development of a model for the design of an optimal evacuation network which simultaneously minimizes retrofit costs of critical links (bridges, tunnels, etc.) and evacuation time. The model considers infrastructures' vulnerability (as a stochastic function which is dependent on the event location and magnitude), road network, transportation demand and evacuation areas. Furthermore, the model evaluates the benefits of managed evacuation (system optimum) when compared to unmanaged evacuation (user equilibrium). The paper presents a mathematic model for the presented problem. However, since an optimal solution cannot be found within a reasonable timeframe, a heuristic model is presented as well. This heuristic model is based on evolutionary algorithms, which also provides a mechanism for solving the problem as a multi-objective stochastic problem. Using a real-world data, the algorithm is evaluated and compared to the unmanaged evacuation conditions. The results clearly demonstrate the advantages of managed evacuation, as the average travel time can be reduced by 5% to 30%.

Original languageEnglish
Pages (from-to)728-735
Number of pages8
JournalTransportation Research Procedia
StatePublished - 2017

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© 2017 The Author(s).


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