National Academies Press: OpenBook

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

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Suggested Citation:"T57054 txt_115.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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This function represents a time rate of demand, so integrating over an interval gives the number of vehicles assigned in that interval. Because the demand function is piecewise linear, these integrals can be calculated using basic geometric formulas to compute the area under the curve. The curve itself is generated using these formulas: starting from a seed value, successive linear segments are determined to ensure that the correct number of vehicles is assigned for the major periods given as input to the algorithm, and such that the curve is everywhere non- negative. Several curves are generated using different seed values, and these are averaged to minimize the impact of artifacts unique to particular seeds. THE VISTA MODEL AND TRANSCAD’S DTA APPROXIMATOR This work compares the results of an STA model to two DTA implementations: VISTA and an add- in to Trans - CAD software. VISTA is a network- enabled software that integrates temporal network data and models for a wide range of transport applications. In particular, VISTA can perform using a cell transmission model (CTM), a traffic flow model developed by Daganzo (1994) as a discrete ver- sion of the hydrodynamic traffic flow model. The CTM divides links into smaller cells, which can then be mod- eled individually at a fine resolution, on the order of 5 to 10 s. A unique feature of the CTM is that flows cannot exceed capacity; queues form to maintain flow. As vol- ume increases, travel time in a cell is constant until criti- cal density is exceeded, after which point travel times increase rapidly, corresponding to free- flow and con- gested conditions, respectively. The DTA approximator is an add- in to the TransCAD software package, and is based on an iterative algorithm developed by Janson and Robles (1995). Much like STA and unlike VISTA, it uses link performance functions to calculate vehicle delay. Although such functions are less computationally intensive, and the approximator runs more quickly than VISTA, they cannot model traffic flow as closely as the CTM: for instance, interaction between links (such as queues that spill into upstream links) is not modeled in the approximator, and flow on a link always increases with volume, even beyond the nominal capacity. Parameters used by the two models are quite differ- ent, however. The link performance functions used by STA and the DTA approximator require capacity and free- flow time to be specified for each link, along with two calibration parameters. The CTM, on the other hand, requires specification of jam density and length for each cell, as well as two parameters indicating the slopes of the flow–density curve when flow is increasing or decreasing with volume, corresponding to the cases when density is either less than or greater than the criti- cal density, respectively. COMPARING STATIC AND DYNAMIC TRAFFIC ASSIGNMENT It is difficult to compare STA with DTA because typical measures of comparison, such as volumes on individual links or total system travel time (TSTT), cannot be read- ily applied due to fundamental differences between the modeling approaches. Moreover, the behavioral assump- tions are so different that parameter assumptions are not particularly comparable, either. Clearance intervals in the DTA approximator show shorter travel times than static assignment. Clearance intervals account for vehicles departing near the end of the model period, and thus arriving at their destinations beyond the model period: during these intervals, no addi- tional vehicles are assigned, but vehicles remaining on the network are allowed to complete their trips. This results in some links experiencing flows for a longer time than in STA, and an effective increase in link capacities. This does not occur in STA because static methods are unable to determine when vehicles depart, and thus assume steady- state conditions. Thus, to enable comparison, link capacities were increased commensurate with the additional clearance time needed for DTA. In essence, this extends the period of analysis in STA to correspond to the added time pro- vided for queue clearance in the approximator, eliminat- ing the bias that exists in a direct comparison of the two. Comparing STA and VISTA results is even more diffi- cult because the CTM used by VISTA is distinct. There- fore, global measures of comparison were used. Individual link flows are not comparable because of the vast differences between the assignment procedures, and measures such as volume/capacity (v/c) ratios have dif- ferent meanings: in VISTA, v/c is the ratio of actual flow to capacity and cannot exceed 1; in static assignment, v/c is the ratio of link demand to capacity (which can exceed 1). This distinction makes v/c comparisons meaningless. Thus, the total travel time for each of five functional classes of roadways (freeways, arterials, and so forth) was compared, as was the total system travel time for the entire network. RESULTS The DFW network contains 919 zones, 15,987 nodes, and 56,574 links (92 of which are tolled in this application). A 3-h peak period (6:00 to 9:00 a.m.) was chosen for analy- 115A 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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