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

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Suggested Citation:"T57054 txt_143.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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income group, one must represent income group within each model component, including trip assignment). CS has completed two studies (in Minnesota and Washing- ton) in which model improvements were implemented to address this issue. For example, if one segments the mar- ket into different trip purposes and income groups throughout the system, but the modal segments are not specifically represented in trip distribution, there will be new problems for tolls that are assessed by mode (i.e., where carpools and transit go free). The Washington State model includes value of time in the mode choice model using time and cost coefficients for each type of traveler. In addition to income, other market segments affecting value of time, such as trip distance, time of day, gender, and age were considered. However, their effects were difficult to incorporate into the model stream and had marginal impact. In Minnesota, in order to develop toll mode constants using other models, CS calculated the ratio of toll-alterna- tive-specific constants to highway travel time coefficients for different market segments based on age, gender, income level, education level, and trip purposes. Based on the parameters of the SR-91 and the Congestion Road Pricing models and assumptions about the Twin Cities dis- tribution of trip and traveler characteristics by purpose (including household income, gender, educational attain- ment, and age), the differences between the free highway mode and toll mode constants range from a 0.88-min penalty (noncommute trips by one-vehicle households) to a 2.89-min advantage (home-based work trips by two- vehicle households). The average equivalent times vary by auto availability level. The variation occurs because the equivalent time penalties of the toll mode constants are calculated by relating the market segments defined by the SR-91 analyses to the Metropolitan Council market seg- ments. However, the auto availability market segmenta- tion was intended for other model components (e.g., trip generation and distribution) and specifically for toll choice. There are, of course, a number of activity- or tour- based models that disaggregate travelers and trips during the trip generation, distribution, and mode choice stages of the process. Most of these activity-based modeling sys- tems still operate on an aggregate assignment basis, how- ever, which only allows for assignment of trips by category or class rather than by vehicle. This leap between disag- gregate and aggregate systems sacrifices the most impor- tant step of the modeling system for pricing studies—trip assignment. Dynamic pricing especially depends on disag- gregate assignment techniques as well as the assignment of trips in much smaller time slices. As the number of cate- gories needed to adequately represent the values of time within assignment is increased, it becomes clear that dis- aggregate assignments would greatly improve the capabil- ity to accurately assess pricing strategies. Nonrecurring congestion also is difficult to represent without a microsimulation or dynamic traffic assignment process. TEMPORAL DETAIL Capturing variations in travel by time of day is essential to predicting transportation system performance and air quality impacts of the transportation sector. Many studies have been conducted to study travel demand by time of day. Much of this research has been limited to observing trends in service usage, such as vehicular volumes and the number of person trips. While important to understand- ing past and present usage patterns, these types of studies are less valuable for predicting future travel by time of day given changes in transportation service availability, qual- ity, and policy. Possibly the behavior least accounted for in travel forecasting is peak spreading (e.g., persons resched- uling their travel from daily periods of high demand to the portions of the day where travel takes less time and is more reliable). Travel surveys and other monitoring activities have documented the correlation between decreasing ser- vice quality (congestion) and longer peak periods. Also, many planning agencies need to test the effectiveness of policy initiatives targeted at shifting travel demand to off- peak periods. An essential component is the time-of-day choice model that provides sensitivity to trav elers’ temporal decisions with respect to sociodemographics, travel conditions, and cost of travel. This sensitivity is needed to effectively eval- uate congestion pricing strategies and improve forecasting results. So in the time-of-day choice models, the inclusion of more temporal details or time periods will make the models more sensitive to congestion pricing. With most of the prior time-of-day choice modeling studies, the various time choices are represented by several temporally con- tiguous discrete time periods such as a.m. peak period, off-peak period, and p.m. peak period. There are draw- backs of using such an approach to model time-of-day choice (4). The use of dis crete time periods requires a pre- determined partitioning of the day into time intervals, the characteristics of which may or may not be the same in the future. This might preclude analyses of potential future congestion pricing strategies during time periods that are smaller than those used in the base year. Also, the discrete choice structure considers the time points near the bound- aries of intervals as belonging to one or the other of the aggregate time periods. In reality, however, two closely spaced time points on either side of a discrete interval boundary are likely to be perceived as being similar rather than as distinct alternatives. So either many finer discrete time intervals have to be specified to obtain a reasonable time resolution, which might not be practical as this will involve estimating many parameters, or a distinction should be made between adjacent discrete time periods. 143INNOVATIVE METHODS FOR PRICING STUDIES

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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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