DTA simulation with reduced number of iterations

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An iterated (day-to-day) dynamic traffic assignment (DTA) is considered. In each iteration, a certain fraction of the totaltravel demand adjusts its travel pattern before the entire demand is loaded onto the (congested) network. It is assumed that thedemand is composed of at least two commodities that exhibit non-identical travel behaviors. The demand changes from oneDTA iteration to the next can then be decomposed into two components: (i)demand exchangesbetween commodities, whichdo not affect the total (aggregate over all commodities) demand pattern, and (ii)demand shifts, which do affect the total demandpattern. Given that there are many (day-to-day) sequences of demand and congestion patterns that lead to the same stationaryassignment, the approach taken here is to minimize congestion variability along this path by favoring, to the extent possible,demand exchangesoverdemand shifts. The underlying intuition is that the resulting reduction in congestion variability andconsequently better predictability of travel times leads to a faster and/or more stable convergence of the assignment.

The selection of replanning rates in general-purpose DTA simulation packages has, due to the limited tractability of suchsystems, received limited attention in the literature. In deterministic settings, flow averaging schemes according to (variantsof) the method of successive averages are often considered (Liu et al., 2007). In stochastic process assignment, constantreplanning rates may be preferred because these can lead to unique stationary process distributions (Watling and Hazelton,2003). The possibly interesting twist of the approach described here is that it (i) leaves the choice of a global replanningrate (being identical for all commodities) to any available algorithm or expert and then (ii) accelerates the convergence of thesimulation process by deriving commodity-specific replanning rates. Lu and Mahmassani (2007) and later Lu et al. (2009)pursue the same line of thinking but make more specific modeling assumptions (a route swapping assignment that aims atattaining a deterministic user equilibrium) than what is assumed in the subsequently described method.

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