Análisis externo Las cinco fuerzas de Porter

A reference dependent approach, mainly PT and its advanced version CPT, has been applied fruitfully in finance and economics. In the domain of travel demand models, PT almost serves as the only non-EUT approach, and one that is employed by most researchers.

The original version of PT highlights two choice stages, namely an editing stage and an evaluation stage. The former applies different decision heuristics to simplify the choice context, while the latter aims at the evaluation of risky outcomes and prospects. Note that the reference point plays an essential role in the evaluation stage, with the outcome utility being measured differently according to its relative location to reference point. It is this asymmetrical measure of reference dependence that differentiates PT from other alternatives. This subsection, therefore, seeks to explore the method for modelling reference dependence and, in particular, for determining the reference point.

4.2.1.1 Model specification

In our PT specification, we define that the utility function of PT is based on the relative value of travel time rather than travel time per se (such as mean travel time). As such, it successfully addresses reference dependence by accounting for the difference of actual travel time and reference travel time. The utility function of PT should contain the following component:

∑ ∑

(4.4)

where gives the reference point for the travel time attribute. To measure outcome utilities asymmetrically, K travel time outcomes are divided into gain and loss according to their relative magnitudes with respect to . Thus, and corresponds to the travel time less and more than respectively, suggesting travel time outcomes of gain and loss. Parameters

and are expected to be positive and negative respectively, due to individuals’

asymmetrical tastes towards gain and loss. In the most general case, outcome probability should be nonlinearly transformed to decision weight by using the weighting function .

If we admit the assumption of the reference dependence of PT, the corresponding prediction generated by this model must be highly reliant on the specific value of the reference point. Indeed, it has been found that determining the reference point has been

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regarded as the main problem for the application of PT. In experimental economics, take a gambling experiment for instance, risky outcomes are simply divided into gains and losses according to the natural reference point, £0. In travel choice behaviour, on the other hand, it is never easy to adopt the reference dependent approach since it is especially difficult to determine what a traveller’s reference point is (refer to subsection A.2.1 for literature review). This problem encourages close attention to setting the reference point prior to any extensive application of PT.

4.2.1.2 Methodology for determining reference point

Based on the above discussions, we now introduce our methods for determining the reference point. The first issue is the definition of reference point. In our risky choice framework, the reference point can be applied to reference prospect, outcome and attribute. Given that, here, risk is only derived from unpredictable travel time, we assume that travellers may only account for reference travel time when they make a route choice. The second issue arises when it is attempted to determine this reference travel time. In this subsection three potential methods are presented.

The first method is the main method applied in existing transport literature associated with reference dependent choice behaviour. It assumes that a traveller’s reference point may be a common or widely accepted travel time. For instance, decision makers might consider the mean or median travel time experienced by the target population as the reference travel time. Given the fact that travellers can only form the reference point from their travelling experiences, however, this proposal is arguably implausible since travellers cannot know these travel times. One solution is to give travellers the travel time information through a website or social network, but this still requires modelling the influence of information, e.g. dynamic models. Another possible solution is to track each individual’s commuting history using advanced equipment, e.g. GPS and cell phone data, and then extract the average travel time of each respondent. In this way, the calculated travel time reflects individual respondents actual travelling experiences recorded in equipment. Despite the reliability and validity of this tracking data (e.g. GPS data), it is potentially time and resource intensive.

The second method attempts to generate the estimated reference point that best fits the data. It is computationally difficult to implement, however, since the calibration process is highly sensitive to the estimated value of the reference point. In fact, there is a kink of utility around the reference point which dramatically affects the estimation results. As such, model calibration is necessary to test different initial values of reference points in order to obtain the

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estimates under global maximization. Moreover, this endogenous estimation method potentially requires an iteration algorithm to find out the estimated reference point which satisfies the assumptions of PT.

The last method is closely related to survey design. In an RP survey exercise, respondents are required to provide extra information regarding their reference points, such as the ideal commuting time, average journey time of recent trips. It is very easy to obtain this information by explicitly presenting the questions like ‘what is your most recent trip’, ‘when do you usually leave home in the morning peak’, ‘what is the journey time that you usually spend travelling from home to your office’. Unfortunately, however, to the author’s knowledge, this kind of RP survey based method for collecting reference points has not been applied in existing transport literature.

In closing, whilst it is relatively arbitrary to assume a natural reference point, this method has been hitherto been regarded as a simple way to incorporate PT into risky route choice modelling. We should, however, pay careful attention to its validity for realism before applying it to our models. The benefit of the survey based method for valuing reference point is its simplicity and flexibility, but the drawback is that it cannot take account of respondents’ perception errors and cognitive bias when they answer questionnaires. For instance, a traveller’s perception of reference alternatives as shown in a questionnaire may not be the same as their real perception of reference alternatives in their real commuting experiences. In this case, the respondent considers the hypothetical reference alternative as being as common an alternative as others. In contrast, whilst the endogenous estimation method for valuing reference points is constrained by the sophisticated specification and complicated calibration required it does not need additional survey based information and, therefore, it avoids respondents’ potential misperceptions due to inappropriate survey design. Consequently, endogenously estimated reference points and natural reference points are the selected methods in this thesis.