In this article, we discuss the system-optimum dynamic traffic assignment (SO-DTA) problem in the presence of time-dependent uncertainties on both traffic demands and road link capacities. Building on an earlier formulation of the problem based on the cell transmission model, the SO-DTA problem is robustly solved, in a probabilistic sense, within the framework of random convex programs (RCPs). Different from traditional robust optimization schemes, which find a solution that is valid for all the values of the uncertain parameters, in the RCP approach we use a fixed number of random realizations of the uncertainty, and we are able to guarantee a priori a desired upper bound on the probability that a new, unseen realization of the uncertainty would make the computed solution unfeasible. The particular problem structure and the introduction of an effective domination criterion for discarding a large number of generated samples enables the computation of a robust solution for medium- to large-scale networks, with low desired violation probability, with a moderate computational effort. The proposed approach is quite general and applicable to any problem that can be formulated through a linear programing model, where the stochastic parameters appear in the constraint constant terms only. Simulation results corroborate the effectiveness of our approach.
Robust dynamic traffic assignment for single destination networks under demand and capacity uncertainty
Journal of Intelligent Transportation Systems ; 24 , 4 ; 331-351
2020-07-03
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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