Abstract: Technology is disclosed for systems and methods for an Evacuation Simulation module generates vehicle trip time and time-series road congestion metrics for vehicles in hypothetical evacuation scenarios. The outputs of the module can be used to understand total clearance time for a neighborhood or region, choke points and areas of worst congestion, individual vehicle routes and trip times, and effects of scenario constraints and mitigation efforts such as road closures, notification urgency, evacuation sequencing, and contraflow. The module contains a large number of input parameters and utilizes high granularity data to model a wide range of specific evacuation scenarios. The model utilizes a novel approach to calculating traffic congestion and a novel application of AI methodology to incorporate human input into the workings of the model.

Abstract: Embodiment method and associated apparatus relate to altering the expected value of a system modeled by a random process simplified to produce a binary outcome. Various embodiments modify a genetic algorithm to optimize such settings as population size, number of iterations to convergence, mutation chance, and sample space. Some embodiment ARON implementations correctly predict game outcome relative to the spread, based on transforming unrelated raw data, applying the transformed raw data to a modified genetic algorithm, generating multiple expected outcomes determined by the modified genetic algorithm as a function of the transformed raw data, and filtering the outcomes as a function of predefined metrics to produce a single end result that can be utilized effectively by an evolutionary-style algorithm.