Distributed Dynamic Data Driven Simulations: A General Framework and Illustration Example

The dynamic data driven simulation paradigm continuously assimilates real-time data to improve system analysis and prediction. Operating dynamic data driven simulation in a distributed manner on edge platforms can significantly reduce latency and alleviate network bandwidth constraints. This paper develops a general framework for distributed dynamic data driven simulation, specifically addressing highly nonlinear and non-Gaussian dynamics and hybrid states comprising continuous, discrete, and categorical variables. The framework operates on two interconnected levels: locally, a particle filter-based data assimilation methodology incorporates noisy measurements to estimate state distributions; globally, a decentralized, consensus-based fusion reconciles conflicting estimates across overlapping regions. To ensure communication efficiency, a hybrid belief compression and particle regeneration mechanism are designed to avoid the prohibitive exchange of raw particles. The framework is illustrated through a distributed microscopic traffic simulation case study featuring an accident-induced shockwave. The results show that the proposed framework can produce consistent hybrid state estimates while maintaining a low communication burden. In the reported traffic case, the sensitivity analysis further suggests robustness to microscopic sensor miss-counts, while also revealing a case-specific dependence on accurate spatial accumulation data.