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A Comparison of Static and Dynamic Traffic Assignment Under Tolls in the DallasFort Worth Region Stephen Boyles, University of Texas at Austin Satish Ukkusuri, Rensselaer Polytechnic Institute S. Travis Waller, University of Texas at Austin Kara M. Kockelman, University of Texas at Austin A s the number of drivers in urban areas increases, the tains 56,574 links and 919 zonal centroids. Compar- search continues for policies to counteract conges- isons are made of three models: traditional static traffic tion and for models to reliably predict the impacts assignment (STA), the TransCAD approximator (an ana- of these policies. Techniques for predicting the impact of lytical, link performancefunctionbased approximation such policies have improved in recent years. Dynamic traf- to DTA), and VISTA's simulation-based DTA approach. fic assignment (DTA) models have attracted attention for An additional contribution is an algorithm that effi- their ability to account for time-varying properties of traffic ciently generates a time-varying demand profile from flow. aggregate demand data (static origindestination [O-D] A feature common to all DTA approaches is the ability trip tables) by interpolating a piecewise linear curve. This to model traffic flow changes over time. A variety of for- algorithm is described below, and is followed by brief mulations exists, with significant differences in how traffic descriptions of the TransCAD add-in and the VISTA flow is modeled, or in how the mathematical program is model, as well as key issues that arise when attempting described. Simulation is sometimes used to incorporate to compare these models with static assignment. A more realistic flow in traffic models while maintaining method to facilitate comparisons of the approximator's tractability. Peeta and Ziliaskopoulos (2001) provide a results with those of static assignment is also described, comprehensive survey of DTA approaches and difficulties. as well as the DFW network results and a summary of While recognizing the dynamic features of traffic is modeling contributions and limitations. more realistic, it introduces issues that are irrelevant in static assignment, such as ensuring first-in-first-out queuing disciplines. Also, significantly more input data GENERATION OF TIME-DEPENDENT are required because DTA models require time- DEMAND DATA dependent travel demand, rather than the aggregate fig- ures that suffice for static assignment. Unlike static assignment models, DTA models require Thus, it is not surprising that DTA formulations lead specification of how demand is distributed over time. to complicated solutions that require a substantial Much of the current literature focuses on estimating amount of computation time when applied to large net- these data from observed traffic counts; however, in this works. It is natural to wonder, therefore, what justifies work, an algorithm is developed to generate such time- the added computational and data requirements. To this dependent demands from existing data used for STA end, this work investigates the differences in results (such as total demand for a.m. and p.m peak hours). This obtained from applying static and dynamic assignment algorithm generates a piecewise linear demand curve, to a large network under a congestion pricing scenario. running more quickly than the quadratic optimization The DallasFort Worth (DFW) network used here con- procedure applied previously. 114