High performance optimization of integrated transportation management methods for emergency evacuation

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The increasing rate of man-made disasters and natural catastrophes affecting major urban areas requires comprehensive analysis and planning for emergency evacuation scenarios while harnessing the potential of Intelligent Transportation Systems (ITS) to expedite the evacuation process. Despite the heightened attention to researching the subject over the past few years, the need still exists for integrating various transportation demand management and supply control strategies to synergize and optimize their potential benefits to emergency evacuation. Optimality refers to maximizing an objective such as maximizing the number of evacuees reaching safety, minimizing their travel times to safety or minimizing the exposure of evacuees to certain risks (e.g. chemical fumes etc.). Demand management refers to controlling the number of evacuees moving in the transportation network from certain origins to certain destinations by controlling departure time and destination choice. Supply management refers to controlling the efficiency of the transportation network using traffic signal control, freeway control and dynamic routing. The ideal output of an integrated evacuation management strategy is information and guidance to evacuees regarding when to evacuate, where to go and how to get there, in addition to a set of infrastructure control parameters such as, for instance, real time signal timing plans, contra-flow lane assignment (reversing the usual flow direction of a given lane) etc. The objectives of this research are to: (1) formulate the integrated evacuation problem as a multi-objective optimization problem, (2) develop a set of solution algorithms based on parallel distributed Genetic Algorithms, and (3) implement the system on a High Performance Computing (HPC) grid. The novelty in this approach is rooted in the unprecedented integration of evacuation management strategies, the development of state of the art extensible solution algorithms based on artificial intelligence and the harnessing of computing power of newly emerging high performance multi-processor hardware.