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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_117.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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not be neglected. While the more realistic modeling may balance this additional time for long- term planning applications, the time needed to run a large number of models may be prohibitive. Nevertheless, to reduce the running time needed for DTA, the demand profiling algorithm described above can be used to generate the data more quickly. When static and dynamic assignment models were applied to the DFW network, TSTT was significantly higher when predicted by DTA, which indicates that stat - ic assignment models can significantly underpredict con- gestion levels due to changes in demand over the peak period. Additionally, the distribution of trips among dif- ferent classes of roadways is significantly different between the cell transmission model (used by VISTA) and the link performance function- based models (static assignment and the DTA approximator) because CTM prohibits flows from exceeding capacities. VISTA pre- dicts significantly fewer freeway trips than static assign- ment or the approximator. Further insights could be gained if the DTA approxi- mator in TransCAD provided additional capabilities. In particular, the ability to extract path flows for each O- D pair and departure time would greatly enhance model- ers’ ability to predict policies’ impacts at a more disag- gregate level. Moreover, the use of link performance functions limits its ability to model queues or spillover effects between links due to congestion, which can be captured using other formulations, such as the CTM. With such improvements, investigations like this one could be extended to account for traffic dynamics under congestion management policies in greater depth. REFERENCES Daganzo, C. 1994. The Cell Transmission Model: A Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory. Transportation Research, Vol. 28, No. 4, pp. 269–287. Janson, B. N., and J. Robles. 1995. Quasi- Continuous Dynamic Traffic Assignment Model. In Transportation Research Record 1493, TRB, National Research Council, Washington, D.C., pp. 199–206. Peeta, S., and A. K. Ziliaskopoulos. 2001. Dynamic Traffic Assignment: The Past, the Present, and the Future. Networks and Spatial Economics, Vol. 1, No. 3, pp. 233–265. 117A COMPARISON OF STATIC AND DYNAMIC TRAFFIC ASSIGNMENT UNDER TOLLS

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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