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

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shopping street in the town or visiting the closest movie theater) or was the choice of location based on some unique properties of the location not associated with any nearby area (like visiting Madison Square Garden or Carnegie Hall in New York)? Introducing casualty into the modeling framework should naturally reduce the tendency for using simplified models of compensatory utility maximization and work in favor of more elaborate decision- making chains with partially noncompensatory rules (eliminations). ATTITUDINAL AND SP EXTENSIONS TO CONVENTIONAL RP SURVEYS For the foreseeable future, the standard RP household survey will remain the major source of information for travel demand model estimation. The most satisfactory surveys are those that essentially form travel diaries with a full accounting of all daily activity- travel patterns for all household members. This type of survey constitutes an ideal basis for additional attitudinal and SP- type ques- tions to reflect each traveler’s actual situation and is much better than a standalone SP survey in which nor- mally one of the trips or activities is taken out of the daily pattern context and then different questions about hypothetical alternatives are pivoted off the observed choice. However, the addition of attitudinal and SP questions to the household survey represents a practical problem because existing household surveys are generally already at the upper limits of length and complexity that can be tolerated by interviewees. Thus, it is important to make these extensions as easy, natural, and short as possible. These extensions are not intended to replace SP surveys with extensive SP games; they are mostly intended to bet- ter the understanding of the observed choices, their sequencing, and the way in which choice sets were formed. There are several examples of extensions of this sort that could be added to conventional household surveys: • For mode and location choices, there can be a ques- tion asking whether the mode–location was usual or occasional; • For mode and location choices, there can be guided questions on the reasons behind the choice of a specific mode or destination; • For departure and arrival time choices, there can be a prepared set of answers on questions about how the schedule was actually built, such as “planned in advance” or “occurred in the course of the day out of necessity.” A different set of questions might be asked at the end of the survey about the schedule priority of all activities and whether any schedule adjustment took place to accommodate some other activities in the schedule. While the authors recognize that not all agencies will have the budget to support such extensive surveying, it is also the case that activity- based models can make the biggest advances in the exploration of the sequencing and scheduling of activities at both the individual and household levels. Yet making these advances requires data that have not conventionally been collected in the context of travel demand surveys and may require new innovations in data collection technology. It might well be worth treating these SP extensions as a pilot study or only collecting the additional data on a subset of the households. 88 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2