Data Assimilation in Discrete Event Simulations

Enabled by the increased availability of data, the data assimilation technique, which incorporates measured observations into a dynamical system model to produce a time sequence of estimated system states, gains popularity. The main reason is that it can produce more accurate estimation results than using either a simulation model or the...

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from...

A particle filter-based data assimilation framework for discrete event simulations

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event...

Data Assimilation in Discrete Event Simulations: A Rollback Based Sequential Monte Carlo Approach

Data assimilation is an analysis technique which aims to incorporate measured observations into a dynamic system model in order to produce accurate estimates of the current state variables of the system. Although data assimilation is conventionally applied in continuous system models, it is also a desired ability for its discrete event...