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Suggested Citation:"T56712 Text_34." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 1: Session Summaries. Washington, DC: The National Academies Press. doi: 10.17226/13676.
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relating to work, school, child care, and eating out can be checked. Unusual time allocations can be examined. • While the structure of travel surveys has tradition- ally focused on supporting the needs of four- step travel models, more information is obtained than is typically used in the modeling process. Only about 3% of the information obtained from travel surveys is actually used in traditional four- step models. The matrix structure of the trip distribution and model split submodels of four- step models limits the model segmentation and the num- ber of explanatory variables. Aggregate dependent variables include household trip generation, trip distri - bution, and mode choice by three to four purposes and two to three time periods. Explanatory variables are typ- ically limited to household size, number of workers, number of automobiles, and household income. • Activity- based models have unlimited segmenta- tion by travel, person, and household attributes. Activity- based models address logical linkages of activi- ties and trips of the same person in time and space, as well as logical joint activities and travel linkages across members of the same household. • New variables could be added to household surveys to add significant explanatory power to mode and desti- nation choice portions of travel models. Examples of these variables include substituting out- of- home activities with in- home activities related to telecommuting, teleshopping, and telebanking; individual and joint trip substitutions; and evening trips. Addressing the use of high- occupancy vehicles, high- occupancy tolls, managed lanes, toll pay- ment methods, and the toll amount could provide a higher level of detail about automobile trips. Additional transit trip information might address the sequence of routes and transfers, the use of park- and- ride lots, and the influence of seat availability, air conditioning, and other amenities. Mode details might include eligibility for employer- subsidized parking, parking charges for all trips, the avail- ability of parking and walking distance to destination, actual automobile availability per trip, and pedestrian and bicycle conditions. Information on income by person, rather than by household would also be of benefit. • There is a change in the conceptual unit with activity- based models. The four- step model tends to focus on location. A survey would ask the question, “What was the next location and associated activity?” Activity- based models focus on activity by asking the question, “What was the next activity and associated location?” Focusing on causality represents a construc- tive intermediate stage between the standard outcome- based approach and the new process- based approach. Causality focuses on why individuals make a specific choice or decision. In practical terms, causality is simple and free of chronological details and intermediate steps of decision making. Short- term model improvements could address sequencing and conditionality of choices, alternatives considered for each choice, and factors or attributes considered. This approach would provide an enhanced understanding of individual decision making and would move toward behavioral models. • Modeling location choices is difficult because of the large number of alternatives and zones and compli- cated substitution structure. Unrealistic modeling assumptions are typically used, which include considera- tion of all zones and locations. A simple utility combines size variables and impedance measures. Using a spatial domain or cognitive map may be a logical approach. In terms of activity locations, perceptions of a location may relate to closeness to home, work, or school. Reasons for the choice of a nonmandatory activity may relate to the proximity to a routine activity or a unique attraction at the location. The role of the activity may be the primary destination in a travel tour or it may be a secondary stop. Variations from the normal work or school location or schedule could be probed in household surveys. • Mode choice causality focuses on whether alterna- tive modes are ever considered or used for the same trip, the reasons for discarding alternative modes, transit reli- ability, and automobile reliability. Possible structural variables include time pressure and the willingness to pay a toll for time savings, as well as weather- related driving conditions. • Attitudinal and stated preference extensions to conventional revealed preference surveys provides one approach for enhancing current practices. Household surveys provide a full picture of daily activity patterns. They also provide a good basis for stated preference extensions to provide a better understanding of choices. • Household surveys remain the main source of data needed for travel models. The completeness and the quality of household surveys have been improving. Tech- nical fixes have related primarily to questions relating to in- home activities and joint activities and travel. Further conceptual development associated with activity- based models focuses on causality and decision- making, wider ranges of explanatory variables, and attitudinal and stated preference extensions. USING GLOBAL POSITIONING SYSTEM DATA TO INFORM TRAVEL SURVEY METHODS Stacey Bricka and Chandra Bhat Stacey Bricka discussed the application of Global Posi- tioning System (GPS) technology with travel survey data collection activities. She provided an overview of the use of GPS with different aspects of travel surveys and high- lighted some of the key findings from these efforts. She described a recent research project examining the use of GPS with the regional household survey conducted in 34 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 1

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries summarizes the sessions of 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.

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