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This paper presents a discrete choice model of activity-travel behaviour that incorporates the effects of task complexity and time pressure on the scale of the utility. The model is subsequently estimated on data from a novel activity-travel simulator experiment that was specifically designed for the purpose of testing our model. Our main results are as follows: firstly, high levels of time pressure and task complexity lead to a smaller scale of utility and hence to more random choice behaviour. Secondly, very short decision times also lead to more random behaviour, although in that case there is no evidence of time pressure. We interpret this phenomenon in terms of a lack of engagement with the choice task among those who make a choice within a matter of one or 2 s after being presented with the choice task. Thirdly, contrary to expectations, we find no evidence for an interaction effect between task complexity and time pressure. In other words, the impact of task complexity on choice behaviour in the context of our data does not become more pronounced when there is a high level of time pressure (and neither vice versa).

This latter dimension (i.e., a potentially substantial improvement in forecasts) implies that capturing the impacts of time pressure and task complexity in discrete choice models of activity-travel behaviour is also important from a practical or policy-viewpoint; this holds even more in light of the fact that in real life, many activity-travel choices are made under conditions of considerable task complexity and time pressure. Our results suggest that by ignoring in choice models the effects of task complexity and time pressure on activity-travel behaviour, policy makers are likely to overestimate traveller sensitivity to changes in the attributes of travel options, when some choices are made under conditions of high levels of task complexity and time pressure, and others are not. Our heteroscedastic models suggest that under high levels of task complexity and time pressure, choice behaviour is governed to a large extent by randomness, implying a limited sensitivity to changes in the availability and characteristics of travel options.

Of course, before our results can be generalized, it is important that they are verified based on other datasets. Although the impact of task complexity has by now been well established, this is not the case for the impact of time pressure (nor for the presence or absence of interaction effects between the two). Whereas we used Stated Preference data collected in a simulator experiment, it would be particularly interesting to see if our results also hold in the context of revealed preference data. Some readers might even argue that what we measured in our experiments is perhaps even more about the impact of takes complexity and time pressure in choice experiments, than about their impact on (real life) travel behavior. Although we went through a lot of effort to design a simulator which gives a realistic account of a travel behavior context, it goes without saying that we only partly succeed therein. As a consequence, our manipulation of task complexity and time pressure can only be considered proxies of the variation in task complexity and time pressure that travelers may experience in real life. This in turn implies that our results should be interpreted with the utmost care. In our view, they are only but a first step towards a proper understanding of real life behaviors under varying levels of task complexity and time pressure. Before any stronger conclusions and generalizations can be drawn, revealed preference data clearly are a necessity.

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Curtis Strite

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Q: What are the effect of consistency and complexity on leisure travel?
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