Assessing Highway Tolling and Pricing Options and Impacts: Volume 2: Travel Demand Forecasting Tools (2012) / Chapter Skim
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Pages 73-96
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From page 73... ...
73 5.1 Coordination with the SHRP 2 C04 Project The second Strategic Highway Research Program (SHRP 2) includes the closely related large-scale project C04 "Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand." The principal researchers working on the NCHRP Project 08-57 are also leading the SHRP 2 C04 project and are able to closely coordinate these two projects as one coherent body of research.
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From page 74... ...
74 of operational models requires that each and every choice dimension should be allocated a proper "slot" in the hierarchy, with upward and downward linkages to related choices. Operational/computing time requirements often limit the total number of choice dimensions and alternatives, but this source of restriction is lessening with time.
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From page 75... ...
75 It is important to note that each level is not seen as independent or disconnected from the others, and we aim to establish a consistent and holistic conceptual framework, where simplified and pragmatic models can be derived from more advanced models, rather than re-invented (which is probably the current state of relationship between travel modeling theory and practice)
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From page 76... ...
76 highway pricing studies. This model should include a welldefined set of features including synthesis of the best practices (corresponds to the short-term improvements described in Chapter 4)
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77 car occupancy, and payment type/technology) , and associated choice models should be properly integrated with the other sub-models in the model system.
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78 policies presents additional complications and may exacerbate VOT inconsistency issues. The objective behind the optimal segmentation structure is to treat VOT consistently across all choices, while avoiding an excessive proliferation of travel segments and vehicle classes.
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79 are modeled in the system. The corresponding dimensions can always be applied for any model either for full segmentation or as variables in the utility function.
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80 those that occur infrequently. For example, a $1.50 for auto trip to work may be perceived as $3.00 per day (assuming a symmetric toll)
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81 example, VOT) , as well as aggregation of segments by VOT for assignment and other model components that are especially sensitive to dimensionality.
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82 where each simulated individual and trip can effectively be modeled as having its own levels of VOT (and VOR)
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83 jsl = coefficients capturing observed heterogeneity, and xn = random term capturing unobserved heterogeneity. The random coefficients are specified in the following way: β β γ ζskn k skm nm n m z= + +∑ (Equation 20)
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84 • Car occupancy choice (carpool formation mechanism) can be incorporated either as part of mode choice at the intermediate level (in the auto nest and before pre-route choice)
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85 reliability, its measurement, as well as the computation and treatment of travel time reliability in modeling tools. The suggested reliability measures have been put in the context of effectiveness related to transportation projects, policies, as well as the entire highway system performance.
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86 additional measures of distribution asymmetry and skew were explored in Bogers, et al.
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87 recommendations on their estimation and incorporation within the structure of the modeling system: • The new generation of time-of-day models is characterized by a high level of temporal resolution. These models can predict trip distribution by 30–60 min intervals (or even less if necessary)
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From page 88... ...
88 behavioral push for changing travel and activity habits toward more frequent joint travel arrangements, through the synchronization of commuter schedules, as well as other activities. There has been a very intensive research effort during recent years to better understand and explicitly model joint travel from the carpool formation stage (Vovsha, et al.
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From page 89... ...
89 when users are segmented by VOT. Certain ad hoc modifications of the link performance functions may be applied to approximate distance-based, time-based, or even congestiondependent pricing forms.
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90 in the CHART corridor network between Washington, DC, and Baltimore (Zhou, et al.
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91 The breakpoints w1, w2 can be calculated for the set of extreme efficient paths in such a way that the following condition holds: C T C T C T 1 1 1 2 2 2 3 3 < < < <ω ω (Equation 21) In the bi-criterion parametric optimum path-finding algorithm developed by Mahmassani, et al.
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From page 92... ...
92 network side will consider integration within existing frameworks that have a base of application to real networks. The keen interest that agencies have for evaluation of pricing and other intelligent management strategies provides an opportunity for complementing the set of tools available to these agencies through the use of simulation-based dynamic modeling techniques.
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93 groups with similar travel behavior based on a predetermined set of criteria (for example, trip frequency, activity duration, travel time, etc.)
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94 replace some of these activity pattern attributes with choices that are more probable for the synthetic traveler, taking into consideration the implied space-time constraints that their itinerary presents. A subcomponent of the Activity Generator is a tour-based location choice module that selects the locations of activity stops in the pattern sequence and considers both the "size" of the opportunities at alternative activity locations as well as the impedance between activity stops and the travel impedance from activity stops to the home locations of the synthetic traveler ("rubber banding" method)
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From page 95... ...
95 words, the fact that individual particles are represented in micro-assignment techniques provides a natural integrating mechanism with the demand side. Hence, particle-based, or disaggregate DTA, is a central element in developing integrated approaches.
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From page 96... ...
96 • Provide the background level of fragility of traffic flows from which the probability of breakdowns can be derived. Average demand is a function of both average travel time and reliability (through measures like buffer time)
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