ABSTRACT
Evacuation plans are designed to move people to safety in case
of a disaster. It mainly consists of two components: routing and
scheduling. Joint optimization of these two components with the
goal of minimizing total evacuation time is a computationally hard
problem, specifically when the problem instance is large. Moreover,
often in disaster situations, there is uncertainty regarding the
passability of roads throughout the evacuation time period. In this
paper, we present a way to model the time-varying risk associated
with roads in disaster situations. We also design a heuristic method
based on the well known Large Neighborhood Search framework
to perform the joint optimization task. We use real-world road network
and population data from Harris County in Houston, Texas
and apply our heuristic to find evacuation routes and schedules for
the area. We show that the proposed method is able to find good
solutions within a reasonable amount of time. We also perform
agent-based simulations of the evacuation using these solutions to
evaluate their quality and efficacy.
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Who goes there? Using an agent-based simulation for tracking population movement
We present a method to apply simulations to the tracking of a live event such as an evacuation. We assume only a limited amount of information is available as the event is ongoing, through population-counting sensors such as surveillance cameras. In this context, agent-based models provide a useful ability to simulate individual behaviors and relationships among members of a population; however, agent-based models also introduce a significant data-association challenge when used with population-counting sensors that do not specifically identify agents. The main contribution of this paper is to develop an efficient method for managing the combinatorial complexity of data association. The key to our approach is to map from the state-space to an alternative correspondence-vector domain, where the measurement update can be implemented efficiently. We present a simulation study involving an evacuation over a road network and show that our method allows close tracking of the population over time.
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- Award ID(s):
- 1903972
- NSF-PAR ID:
- 10147561
- Date Published:
- Journal Name:
- Winter Simulation Conference 2019
- Page Range / eLocation ID:
- 227 to 238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WSC40007.2019.9004861
