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In the setting of a recently developed cellular stochastic traffic flowmodel, it has shown that the joint per-cell vehicle densities, as a function oftime, can be accurately approximated by a Gaussian process, which has theattractive feature that its means and (spatial and temporal) covariances can beefficiently evaluated. The present article demonstrates the rich potential ofthis methodology in the context of road traffic control and transportationnetwork design. To solidly provide empirical backing for the use of amultivariate Gaussian approximation, we rely on a detailed historical datasetthat contains traffic flow data. Then, in the remainder of the paper, weprovide a sequence of design and control related example questions that can beanalyzed using the Gaussian methodology. These cover the following topics: (i)evaluation of stationary performance measures, (ii) route selection, (iii)control of traffic flows, and (iv) performance of traffic networks witharbitrary topology. In discussing the setup, results, and applications of theseexamples, we stress the appropriateness of our {stochastic} traffic model overa deterministic counterpart.

A Gaussian segment-based traffic flow model for the design and control of transport networks