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