Theses and Dissertations Collection

Evacuation planning is a fundamental part of emergency management. An

effective evacuation plan can mitigate significant loss of life.

During a disaster many factors can complicate evacuation, such as

traffic congestion, real-time damage to the transportation network,

changes in the safety of areas in the city, and noncompliance with

established evacuation plans. Previously, traffic in an evacuation

has been managed as a routing problem from origins to destinations.

This work describes an evolution-based approach to the problem of

evacuation planning using a Markov model approach. It is designed to

better adapt to unpredictable complications of disaster evacuation.

In our approach an Evolution Strategies algorithm is applied to sets of

probabilities describing assignment of traffic to streets in networks

representing urban areas. A mesoscopic traffic simulation is used to

evaluate the fitness of each set of probabilities, establishing a

traffic assignment distribution. Fitness is evaluated by measuring

the safety of all vehicles at the end of the simulation. This method allows

abstraction of individual vehicles and origin-destination pairs, allowing

a more generalized solution. Sources of danger creating the need for

evacuation are also abstracted, allowing the application of this model to

arbitrary disaster events.