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Introduction

Note: This material comes from the Travel Survey Manual Chapter 21. It was originally composed by Gonçalo Correia and Mark Bradley, drawing also from the FHWA’s Travel Survey Manual’s Chapter 13.

In general transportation planning, surveys are meant to capture travelers’ current travel behavior. For instance, one is interested in knowing the actual mode a traveler is using, actual travel times, destinations, and so forth. This is known as Revealed Preference (RP) data, as the traveler is currently experiencing that behavior and making a choice based on his or her knowledge of the available travel options. Another type of data is based on Stated Responses (SR), in which hypothetical situations are presented to the respondents, who are then asked to choose based on the given attributes for each alternative, without necessarily experiencing them in real situations. SP is a very popular sub-class of SR methods, focused on estimating the utility function for alternatives (Lee-Gosselin 1995). Other question types in this class are stated intentions, stated tolerance, stated adaptation, and stated prospect, as discussed later in this chapter.

SR surveys offer a great advantage for overcoming the problem of the “new option”, whereby an analyst seeks to forecast the use of a new alternative (such as high-speed rail), particularly when the new option is very different from existing alternatives with which the respondent is familiar. The use of a new alternative is not reflected in RP data collected on choices made in real markets.

Another aspect where RP data often fails is that one is not able to correctly identify the alternatives that were not chosen. The decision maker faces options while having imperfect information, and not knowing all his/her alternatives. Other times the decision maker will only have access to attribute information on the chosen alternatives, e.g. having information on the current automobile trip and no information on all public transport alternatives possible in the area.

As noted above, the most common type of SR question is the SP variety, where the respondent is asked to chose, rank or rate different alternatives based on their attributes (e.g., travel time, travel cost, and wait time), thus giving information on the way the choice is made. For a choice-based SP survey one is aiming to get a choice on the preferred alternative. In a ranking experiment the respondent has to rank the alternatives in order of preference. In a rating experiment each alternatives must be classified using a scale which measures its attractiveness to the respondent.

There are at least four other types of SR surveys: Stated Intentions, Stated Tolerance, Stated Adaptation and Stated , as described below (and as extracted from Lee-Gosselin (1995)):

Stated Intentions: This is perhaps the simplest form of SR question. Typically one or two new choice alternatives are described, and respondents are asked if they would use the new alternative or not, in the form of a binary yes or no question (or they may be asked to rate how likely they would be to use the alternative). Such questions may be useful to get a simple overall indication of the demand for a new alternative, but do not contain enough detail to model the demand for alternatives with different attribute levels or under different scenarios. Also, a very simple type of choice question may not be sufficient to get respondents to consider their likely choice behavior very carefully.

Stated Tolerance: Techniques included in this class do not ask respondents to respond to alternative behavioral outcomes represented by specific attributes and attribute levels. Instead, respondents are asked to identify the conditions under which they would take a particular action or accept a particular behavioral outcome. The basic type of information sought is responses to questions such as: “Under what circumstances could you imagine yourself doing the following?” One form of this approach that received much attention in transportation planning in the 1980’s was the “transfer price” (TP) method. The respondent would consider two choice alternatives, and then be asked to imagine that the cost of one of the alternatives changes and indicate at what level he or she would switch to the other alternative. For example, auto commuters could be asked to consider their best transit option and indicate how high fuel prices would have to rise before they would switch to commuting by transit. This method thus gives a direct quantitative measure of the difference in utility between two alternatives, but it has been questioned whether or not travelers can respond very accurately to such questions. A related method that is popular in the field of environmental economics is that of “contingent valuation” (CV), in which people are asked to directly value a “good” that is not actually available for purchase. For example, people can be asked how much they would be willing to pay in additional taxes if it could ensure that everyone in their city has access to good public transportation. This could be thought of in the context of stated tolerance—how much of such a tax would people be willing to tolerate?

Stated Adaptation: Techniques included in this class ask respondents to indicate in a relatively open-ended manner how they would respond when faced with a particular set of constraints. The basic type of information sought are responses to questions such as: “What would you do differently if you were faced with the following specific constraints?”

Stated Prospect: With these techniques, neither the list of possible behavioral outcomes nor a detailed set of constraints is predetermined. Instead, respondents are typically presented with some sort of general scenario (e.g., an energy shortage) as a way of initiating the process of eliciting behavioral outcomes and constraints. Measurement methods for these techniques involve the use of simulation gaming techniques. The basic type of information sought are responses to questions such as: “Under what circumstances would you be likely to change your travel behavior and how would you go about it?”

Of the classes of SR techniques, SP surveys are the most important source of data for developing a Discrete Choice Model (DCM) to represent traveler decisions when faced with new travel alternatives and transportation policy actions. DCMs have played an important role in transportation modeling for the last 25 years. “They are namely used to provide a detailed representation of the complex aspects of transportation demand, based on strong theoretical justifications” (Bierlaire, 1997).

Chapter 21 of the Travel Survey Manual focuses on SP experiments. The design and deployment of the experiments is explained focusing on the attributes and their levels as well as the media and the way to present the choices to the respondents. The chapter also presents the main analysis that can be conducted through the calibration of DCMs based on SP information.

References

The Online Travel Survey Manual provides a comprehensive overview of travel surveys. It is curated by Transportation Research Board’s Travel Survey Methods Committee (ABJ40).