Accounting for inertia in modal choices:

Size: px

Start display at page:

Download "Accounting for inertia in modal choices:"

Curtis Fields
5 years ago
Views:

1 Workshop on Discrete Choice Models EPFL, Lausanne - August 2010 Accounting for inertia in modal choices: Some new evidence using RP/SP dataset

2 Content of the presentation

3 Backgrounds Modal choice model have traditionally been estimated based on the microeconomic assumptions that each individual choice implies a new and independent maximisation process based on the trade-offs of the attributes in the current situation. Behavioural researchers postulate that past decisions remain in the memory of the consumer and are used to adapt the current consumption behaviour. Basically people construct their preferences when encountering a new domain (such as when new alternatives entered the market or some existing alternatives is completed revamped) as they are forced to rethink about their choice. Otherwise, people try to avoid the effort of constructing their preferences for each decision and tend to repeat the first decision without thinking again about the reasons ( ideal trade-offs ) why they behave in such way. Inertia measures the effect that preferences experienced in previous periods have on the current choice.

4 Objetives In this work we use a mixed dataset of revealed preference (RP)-stated preference (SP) with the objective to study the effect of inertia between RP and SP and if inertia is stable along the SP experiments. In particular: 1. We firstly test and compare several different measures of inertia that have been proposed in the literature (most of these measures have been used in short and long RP panel data, but not in the RP/SP context). 2. We then explore some new measures of inertia to test for the effect of learning along the SP experiments and we disentangle this later effect from the pure inertia effect. 3. Finally we explore the relation between the utility specification (especially in the SP dataset) and the role of the inertia in explaining current choices.

5 Accounting for inertia To properly account for inertia, the current choice needs to be explicitly relates with the previous ones, and for this panel data are required. In particular, given a sequence of choices t ={1,,T} inertia effect for any situation in time t >1 is usually specified as: Where: is the random utility of the current situation is any term that measures the lagged effect on the current choice (t+1) of the evaluation in the previous time (t) is a parameter that measures the impact of the lagged effect on the current choice and, in the most general case, can vary across alternatives, individuals and choice situations.

6 Measures for inertia The influence of inertia in the choice process has been largely discussed in the literature. Different measures have been proposed for : Dummy lagged dependent variables. This is the measure of inertia most typically used in the RP/SP dataset: if the alternative j chosen in the SP situation is equal to the alternative chosen in the real situation (RP), and zero otherwise. Continuous lagged dependent variables. This measure proposed by Morikawa (1994) was only tested with continuous panel data: the number of times the alternative j chosen in the (t+1) situation has been chosen in the previous situations Lagged response to changes in some attribute. This is a measure typically used to test inertia with panel data. where X is typically travel time, or travel cost. Lagged response to changes in the utility. This measure has been proposed by Cantillo et al (2007) specifically to test inertia between RP and the SP choices. inertia is defined as the systematic utility of the alternative chosen in the RP.

7 Measures for inertia Starting form the idea of Swait et al. (2004), we propose a new measure that is based on the assumption that individuals store in the memory every choice made and evaluate the current situation weighting all the previous experiences. The following two measures were tested: Lagged weighted response to changes in attributes it is the weighted value of the lagged attribute over the previous situations. Lagged weighted response to changes in the utility it is the weighted value of the lagged utility over the previous situations. Different from Cantillo et al. (2007), we considered the random utility (excluded the EV1 terms).

8 Modelling inertia in the RP/SP data Given two sources of data, one coming from a RP survey and the other from a SP one, the RP/SP model with inertia can be specified as: Where: It might include different attributes and different random components. It accounts for systematic and random heterogeneity in preferences and responses, for correlation among alternatives and for panel effect. is the effect of the RP experience on the sequence of SP choices In the RP/SP dataset, inertia has been studied only with respect to the choice made in the RP real world.

9 Modelling inertia in the RP/SP data Since in the SP dataset the sequence of choices is observed from the beginning, inertia can be measured respect to the SP situations, other than the RP situation. To account for both effects separately, we use the following formulation: Where it is: For both the RP and SP parameters

10 RP/SP data used Data were collected in 1998 on a modal choice context among car, bus and train Focus Groups (qualitative survey to obtain a better knowledge of the phenomenon: income, RP questionnaire form, etc.) Revealed Preferences (RP) survey (300 home interviews (families), 24-hours self completion travel diary, 903 people interviewed, 1,840 trips, SE characteristics) Stated Preferences (SP) survey (300 individuals RP, choice context, 4 variables at 3 levels, two-pair interactions, 9 cases to each individual) RP/SP observations used to estimate models

11 LOS attributes Random heterogeneity Interactions between LOS attributes Systematic heterogeneity Attributes Travel time (PT) Travel time (Car) Walking time (RP) Walking time (SP) Cost/g (i.e. expenditure rate) Frequency Transfer Comfort 1 (mean) Comfort 2 (mean) Comfort 1 (st.dev) Comfort 2 (st.dev.) Travel time* Cost/g (SP) Travel time * freq (SP) Comfort1* Student Comfort2* Student (Cost/g)* Gender (SP) Walk. time* Gender (SP) Travel time* Car/Lic (SP) ASC_bus ASC_car PT correlation SP scale Specific for RP and SP

12 The lagged response to changes in travel time and in the utility are both significant And the two models have almost the same overall fit (same LL) The effect of inertia is sensitive to the specification adopted. The lagged response in the utility shows the highest sensitivity. When LOS interactions are specified, the inertia parameters (both the standard deviation and even more the mean) are more significant Not considering the random heterogeneity around the two comfort variables makes the inertia (both the mean and the deviation) not significant.

13 systematic and random heterogeneity around the mean inertia the significance of the random heterogeneity of the comfort variables is due to the panel correlation. The inclusion of the systematic heterogeneity reduces significantly the effect of the inertia (both its mean and standard deviation). Only the car license is significant. Car ownership is not significant.

14 The RP and SP inertia parameters are (often) significant and with different signs: - negative for the RP (seeking variety), - positive for the SP (reinforce the previous choice). The real experience (RP alternative) is always important in explaining the SP choices, although the heterogeneity around the RP inertia term is much more important than its mean effect. On the other hand, the experience made during the SP experiment has only an effect as a cumulative experience. BUT this result is not stable. The car licence is the only significant variable, although it is clear that it is only relevant to explain the inertia due to the RP experience.

Inertia has a strong effect in explaining mode choice models: Different measures have been proposed and it seems important to test and compare which is the best for each specific set of data.

15 Inertia has a strong effect in explaining mode choice models: Different measures have been proposed and it seems important to test and compare which is the best for each specific set of data. Other measures of inertia need to be tested, to account for the more complex behaviours. Using RP/SP data, we found that: There is inertia effect inside the SP choices and this is different from the inertia of the RP choice Among the SP choices, the effect of inertia is given by the weighted utilities of the previous choices It is crucial to adopt a correct utility specification to properly highlight inertia effect.

16 Thanks

Choice Theory. Matthieu de Lapparent

Choice Theory. Matthieu de Lapparent Choice Theory Matthieu de Lapparent matthieu.delapparent@epfl.ch Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne