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

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Suggested Citation:"T57054 txt_116.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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sis. For the DTA approximator, this period was broken into eighteen 10-min intervals, with three additional 10- min intervals provided for network clearance. A total of 2.56 million vehicle trips were assigned. Because Trans - CAD’s approximator does not recognize tolls, delay- based tolls were added to the free- flow travel time for each link, using an assumed travel time value of $10 per vehicle hour. Tables 1 and 2 summarize the results for comparing the approximator and VISTA with STA. The most notable differences between the approxima- tor and STA are in links that are predicted to be con- gested under the static analysis. For these, DTA predicts an even higher level of congestion, often significantly so. While static analysis predicts a TSTT of 1.27 million vehicle hours, the DTA approximator predicts 2.53 mil- lion vehicle hours. Unfortunately, the approximator’s use of Bureau of Public Roads–type functions, which allow arbitrarily high volumes, precludes taking advan- tage of the queue spillback features available in other DTA implementations (such as VISTA) that can provide added realism. Additionally, it should be noted that much of this increased congestion can be found on free- ways, which carry far more traffic than other functional classes. As with the DTA approximator, TSTT is much higher under VISTA than under static assignment: VISTA pre- dicts a TSTT of 3.09 million vehicle hours for the same 3-h application. Another, perhaps more significant, result is that static assignment tends to designate consid- erably more vehicles to freeways, whereas VISTA’s assignment relies more on arterials and collectors. This is in contrast to the DTA approximator, in which the dis- tribution of traffic among the roadway classes was more comparable. This arises from fundamental distinctions between the link performance function- based approach and the strictly capacity- constrained CTM. The shift from freeways to arterials and collectors is felt to be more consistent with CTM’s more detailed traffic flow model. However, actual traffic counts and speed checks would be needed to determine which model’s predictions are more accurate. CONCLUSIONS While congestion- reduction policies and DTA each have attracted considerable interest in recent years, efforts at using the latter to evaluate the former on large- scale net- works are relatively few. Several issues arise when trying to do this. A comparison of static and dynamic traffic assignment is nontrivial due to fundamental differences between the models; however, the increase in capacity induced by clearance intervals in the DTA approximator can be accounted for by an appropriate increase in the capacities used in static assignment. With models such as the CTM, which are vastly different from static assign- ment, it is much more difficult to compare the results on a link- by- link basis, and in this work only global mea- sures of system performance were compared. The CTM may produce results that are considerably different than traditional assignment, because it models traffic flow at a more detailed level. This is particularly apparent in congested networks because many assump- tions in static traffic assignment about steady- state con- ditions and link performance functions are less realistic. In this investigation, the effects of these assumptions were amplified with the DTA approximator, which also uses link performance functions. However, the additional computation time required to find a DTA solution, particularly with the CTM, can- 116 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 TABLE 1 Comparison Between Static Assignment and DTA Approximator Vehicle- miles Traveled (millions) Average v/c (VMT- weighted) Category N Static Dynamic Static Dynamic Freeway 6,292 25.88 29.86 1.02 1.64 Principal Arterial 4,936 6.46 5.39 0.72 0.86 Minor Arterial 10,434 7.65 6.25 0.43 0.52 Collector 14,596 2.97 2.74 0.77 1.39 Frontage Road 2,783 1.56 1.44 0.60 0.96 Congested 2,995 18.35 22.61 1.26 2.08 Uncongested 25,997 28.61 25.11 0.62 0.79 TABLE 2 Comparison Between Static Assignment and VISTA Total travel time (h x 103) Proportion (%) Functional class Static Dynamic Static Dynamic Freeway 505 325 40.6 10.5 Principal Arterial 174 543 14.0 17.6 Minor Arterial 237 715 19.1 23.2 Collector 227 738 18.2 23.9 Frontage Road 45 390 3.7 12.6 Total System 1,266 3,086 100 100

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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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