A DDDAS framework for managing online transportation systems

Dynamic Data-Driven Application Systems (DDDAS) integrate an executing simulation with data instrumentation in a feedback control loop. Additional data can be assimilated into the application computations and the application may control the data acquisition process. This thesis discusses a DDDAS for monitoring transportation systems by integrating trajectory prediction and accelerated microscopic traffic simulation. We first discuss a set of trajectory prediction methods from which likely trajectories can be sampled from efficiently. Afterwards, we show how microscopic traffic simulations can be driven by these estimates and computationally accelerated with a lazy evaluation and speculative execution scheme, termed Superimposed Execution. This technique can be used to simulate objects that travel in a spatial network and where collected output statistics are limited to a single entity. Under mild assumptions, this novel scheme yields the same results as explicitly enumerated runs, while - in some cases - approaching a speedup equal to the number of runs. Lastly, a general computational method - termed Granular Cloning - is proposed to accelerate ensemble studies without accuracy loss. Granular Cloning allows the sharing of state and computations at the scale of simulation objects as small as individual variables, offering savings in computation and memory, increased parallelism and improved tractability of sample path patterns across multiple runs.Ph.D