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