Emergency evacuation planning has drawn significant interest and attention over the past few years. The increasing rate of man-made disasters and natural catastrophes affecting major urban areas require comprehensive analysis and planning for emergency evacuation scenarios while harnessing the potential of Intelligent Transportation Systems (ITS) to expedite the evacuation process. Numerous studies, formulations, and control approaches have been presented in the literature with the common goal of improving the evacuation process to save precious time and lives. These studies are important contributions to the state of the art. However, the need still existsfor integrating the various demand management and supply control strategies to synergize their potential benefits to emergency evacuation. The focus of this paper is to address the demand side of the problem and integratedemand scheduling and destination choice optimization. Towards integrating demand scheduling and destination choice, we attempt to dynamically route traffic during evacuation, capture the dynamics of both the loading and the evacuation profiles with time, utilize genetic algorithms as an optimization tool to fulfill the evacuation goal, and provide evacuees with optimal spatio-temporal guidance throughout the emergency evacuation process. The result is an optimal spatio-temporal evacuation (OSTE) model and tool that helps evacuees to decide where to go, when to go, and how to get there, i.e. the output of this model is the optimal departure times, destinations and paths for each evacuee. A case study applying the model to a portion of Downtown Toronto in a simulated environment is also presented.
Samenvatting