Assessing Highway Tolling and Pricing Options and Impacts: Volume 2: Travel Demand Forecasting Tools (2012) / Chapter Skim
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Pages 11-19
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From page 11... ...
11 2.1 Basics of T&R Forecasting A travel demand model predicts travel flows between origins and destination by time of day, mode, and route within each mode. In addition, these models produce the necessary information on toll facility patronage, as well as tolling and pricing impacts on all trips in the region.
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12 affect transit modes as well, such as buses in mixed traffic or using HOV lanes as well as park and ride options. 2.2 Travel Cost Representation in Demand and Network Models Highway pricings should first be properly incorporated in network assignments and skimming procedures through generalized cost functions.
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13 Equation 1 corresponds to the general expression of highway utility in its most common form. This expression constitutes a key component in all travel choice models.
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14 are shown in Figure 3 and Figure 4. These models are discussed in detail in Appendix A (Section A.1.2)
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15 t would represent the most consistent impedance measure, it is computationally very intensive to do so since it should be implemented for each origin-destination pair. The usual practical approach taken with a 4-step model (as well as adopted for some ABMs)
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16 • In most cases, only route itinerary (assignment) and binary route type choice (toll versus non-toll)
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17 Major model feature Detailed feature / sub-model Possible characteristics Spatial scale Regional Corridor / sub-area Facility Coverage of time periods Regular weekday AM peak & shoulders PM peak & shoulders Midday Other off-peak (night, early) Daily traffic Annualization factors Weekend traffic (assumptions)
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18 specifications include toll utilities that are a function of both the toll alternative travel time and travel time savings with respect to the free alternative. This type of specification may result in counter-intuitive results when the LOS attributes on either the toll or the free routes change.
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From page 19... ...
19 implemented by periods of the day and for multiple vehicle classes that typically include vehicle type and occupancy. However, trip purposes and income groups are blended together before assignment, creating strong aggregation biases with respect to VOT.
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