Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand (2012) / Chapter Skim
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Pages 114-131
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114 C h a p t e r 5 This chapter provides a concise guide to how the methodological issues and results presented in the previous chapters can best be incorporated into practical planning tools. Specific models and tables presented in earlier chapters are referred to as needed.
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115 ownership models were not estimated in this study, they are included in most advanced-practice trip-based models, and examples can be found in the literature. The team includes this recommendation here to underline its importance, because the most significant variables in mode choice models are those related to auto ownership, and auto ownership is responsive to changes in household size and income distributions, as well as changes in pricing and congestion.
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116 effects of a number of those characteristics on willingness to pay and VOT. A prototypical VOT function would be one such as in Chapter 3, including the effects of income, occupancy, mode, TOD, gender, age, tour purpose, and distance, plus residual random variation.
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117 in the application of these methods (see beginning of this chapter) include the main recommendation of segmentation of the demand matrices into classes with different VOT and other behavioral parameters.
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118 reliability measures in the form of generalized cost. Most existing tools readily allow incorporating a link-level cost attribute in the path search procedures used to generate paths in the assignment process.
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119 some of the above methods and recommendations in static assignment tools. In particular, if path-based assignment approaches are used, it is possible to improve the route choice basis by implementing the recommended options.
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120 DTA at the disaggregate level. In fact, the list of trips for which the individual trajectories can be produced is a very small portion of the all possible trips to consider.
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121 demand models or the static trip-based network simulation models because they operate with unconnected trips and do not control for activity durations at all. Also, in four-step models, the inherently crude level of temporal resolution does not allow for incorporating this constraint.
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122 obey utility-maximization rules over the entire schedule and cannot be effectively modeled by simplified procedures that adjust departure times for each trip separately. None of the existing operational ABMs explicitly controls for activity durations, although some of them control for entire-tour durations (such as the MTC ABM)
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123 times based on expected travel times (from the DTA) and TOD choice utilities.
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124 are different from planned travel times. This action helps the DTA to reach convergence (inner loop)
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125 the scale of all departure and arrival times. In this way, the problem is formulated in the space of activity durations, and the trip departure and arrival times are derived from the activity durations and given travel times.
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126 reliability. It can be used as a surrogate when more advanced methods are not available, but it is less appealing behaviorally and it is not the main focus of the current research.
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127 Travel Purpose Examples of Population and Travel Characteristics Model Coefficients and Derived Measures Household Income ($/year) Car Occupancy Distance (mi)
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128 Travel Purpose Examples of Population and Travel Characteristics Model Coefficients and Derived Measures Household Income ($/year) Car Occupancy Distance (mi)
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129 each trip based on the expected travel times (and known variations if used in the scheduling procedure and departure time optimization)
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130 approach. Some approaches to endogenously calculated PAT within the scheduling model as a latent variable were suggested by Ben-Akiva and Abou-Zeid (2007)
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131 this method on the network simulation side proved to be more complicated than its incorporation in a demand model. Any demand model, whether four-step or ABM, inherently operates with entire-trip O-D LOS measures.
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