Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation

The authors develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions and is based on procuring path-space (time-series) information. More generally the authors propose a mathematical methodology which quantifies traffic information loss. In particular the method undertakes sensitivity analysis on available traffic data and optimizes the traffic flow model based on two information theoretic tools which the authors develop. One of them, the relative entropy rate, can adjust and optimize model parameter values in order to reduce the information loss. More precisely, the authors use the relative entropy rate as an information metric between time-series data and parameterized stochastic dynamics describing a microscopic traffic model. On the other hand, the path-space Fisher Information Matrix, (pFIM) reduces model complexity and can even be used to control fidelity. This is achieved by eliminating unimportant model parameters or their combinations. This results in easier regression of parametric models with a smaller number of parameters. The method reconstructs the Markov Chain and emulates the traffic dynamics through Monte Carlo simulations. The authors use the microscopic interaction model from Sopasakis and Katsoulakis (2006) as a representative traffic flow model to illustrate this parameterization methodology. During the comparisons the authors use both synthetic and real, rush-hour, traffic data from highway US-101 in Los Angeles, California.


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  • Accession Number: 01598171
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 15 2016 12:18PM