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

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Suggested Citation:"T57054 txt_087.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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The variables listed above have already been exam- ined in various research and modeling frameworks and contexts. These are measures that can be quantified and added to a survey instrument. What is needed is to move these research achievements into practice for travel sur- veys and models. In particular, widening the range of explanatory variables should eventually allow for the removal of flat mode- choice constants and distribution K- factors that dominate the current models and “explain” most of the observed variability. An important but underresearched area is the exami- nation of long- term trends in travel behavior. Travel behavior obviously undergoes a significant evolution that is not captured by static travel demand models. There have been only several attempts to capture long- term trends in VOT estimates with the corresponding consequence for the choice model coefficients. CAUSAL LINKAGES In the authors’ view, focusing on causality represents a constructive intermediate stage between a fairly stan- dard outcome- based approach and the new process- based approach. The difference between outcome- based, cause- based, and process- based approaches can be illus- trated by the following example of location choice for shopping. The conventional outcome- based approach would try to explain the chosen location by means of the location characteristics (size, distance from home, accessibility by different modes) and person–household characteristics (person type, gender, age, car ownership, presence of children, etc.) in a single- choice framework in which all location, person, and household attributes would be blended in the utility function and all other locations (zones) would be considered as available alternatives. The cause- based approach would be focused on for- mation of the available choice set under the given condi- tions of the person that are considered as earlier in the causal chain and prove that these conditions indeed were fixed in the decision making at the time of making the modeled decision (available time window, car availabil- ity, usual spatial “domain” of the person) and then for- mulation of a choice model that would take maximum advantage of the causal–conditional variables and the conventional variables. The cause- based approach is ori- ented to proper sequencing and conditioning of decision- making steps in an overall static environment. The decision process–based approach would be focused on both causal and chronological aspects of the decision making associated with the modeled event. Ide- ally, this would include a historical sequence of prelimi- nary decisions about the time and location for the modeled shopping activity, probably including numerous corrections and adjustments until the final decision was made and the corresponding activity was implemented. The three approaches described here are not actually alternatives: they are sequentially inclusive. All factors, variables, and observed statistics pertinent to the con- ventional outcome- based approach are still relevant for the cause- based approach, and causality is still a part of the decision- making screening. However, in addition to what happens as a result of the combination of explana- tory variables, the cause- based approach offers insights into the why sequence of decisions and events that led to the modeled what. The decision process–based approach takes an additional step in mapping the whole how chronology of the decision making that built up around the modeled event. The modeling complexity and amount of information needed for these approaches grows exponentially from what to why and then to how. Chronological peculiarities of individual decision making are less important for large- scale models and fre- quently lead to complicated multistage procedures with numerous feedbacks that are difficult to convert into operational models. Understanding of casual linkages is a simpler task although it is a limited view of travel behavior. It may significantly improve the structure of the travel model system and sequencing of the modeled choices and associated decision- making steps. The cause- based approach to surveys pragmatically serves the existing static structure of choice models and helps in improving it. It is not a substitute for a full- fledged process- based approach; it is a simplification that is practically helpful in the short term. It may also be helpful in the longer term as well, however, because the knowledge and understanding acquired in causal analy- sis may be of great value for the subsequent process- based analysis. Introducing causality and proper sequencing in a stat - ic framework requires adding to the household surveys specific questions that would refer to the order and con- ditionality of decisions as well as to the formation of the choice set. In particular, for each visited activity location and the corresponding choice of destination, mode, and time of day (TOD), the following set of questions can be added to either RP or SP surveys: • Was this activity- preliminary scheduled or under- taken as a result of occasionally saved time in the course of the day? • Were the destination, mode, and TOD choices made simultaneously or was there a certain order to con- ditional choices? Which of these choices are usual and stable over time and which are subject to change? • If the actually chosen alternative was not available, what would be the second- best choice? • Is there any predetermined area from which the locations choice was made (like shopping on the same 87DIRECTIONS FOR COORDINATED IMPROVEMENT OF TRAVEL SURVEYS AND MODELS

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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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