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A matrix estimation method using the semi dynamic assignment model STAQ is developed exploiting its methodological advantages over full DTA models. The matrix estimation problem is formulated as a bi-level problem and is solved on the node level taking flow metering into account. In the lower level the method uses marginal simulation of the node mo...
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... w=0.5 should result in components 1 f and 2 f contributing equally to the objective function; i.e.: the ratio between 1 f and 2 f should converge towards 0.5. Fig. 5 shows values of objective function components 1 f and 2 f and the objective function total F for a run without constraints on ∆D over iterations. From this figure, a repetitive cycle can be seen where the components converge towards a ratio of 0.5 during three subsequent iterations, but shoots out of convergence every fourth iteration ...
Citations
... The link state constraints in this paper effectively stabilize the solution method by maintaining the state of potential bottleneck links. However, constraints in the node model actually operate on the turn level, and any constraint state switch causes a discontinuity in α n (T n ) (Brederode et al., 2014). This means that the link state constraints from 2.3.3 are too simplistic as they do not specify the normative turning movement. ...
... Further note that the results in this example seem trivial, as they can be deduced by simply analysing the network, but this example is given as an introduction to the solution scheme described in Section 3.2. A more complex case based on the numerical example described in (Tampère et al., 2011) is given in (Brederode et al., 2014). ...
This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters.
The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function.
Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice.
... Most importantly, the development of a STAQ based matrix estimation method that takes flow metering and spillback effects on observed data into account. A first attempt for such a method is described and applied in (Brederode, Pel, and Hoogendoorn 2014;Brederode, Hofman, and van Grol 2017) respectively. When in place, model systems can properly be calibrated using STAQ which enables more thorough validation of the assignment model comparing its outcomes with observed flows, congestion patterns and travel times for a large urban region. ...
This paper describes the road traffic assignment model STAQ that was developed for situations where both static (STA) and dynamic (DTA) traffic assignment models are insufficient: strategic applications on large-scale congested networks. The paper demonstrates how the model overcomes shortcomings in STA and DTA modeling approaches in the strategic context by describing its concept, methodology and solution algorithm as well as by presenting model applications on (small) theoretical and (large) real-life networks. The STAQ model captures flow metering and spillback effects of bottlenecks like in DTA models, while its input and computational requirements are only slightly higher than those of STA models. It does so in a very tractable fashion, and acquires high-precision user equilibria (relative gap < 1E-04) on large scale networks. In light of its accuracy, robustness and accountability, the STAQ model is discussed as viable alternative to STA and DTA modeling approaches.