Uncertainty Aversion and Its Role in Travel Decision Making Under Uncertainty

Uncertainty Aversion and Its Role in Travel Decision Making Under Uncertainty

Zheng Li (School of Logistics and Trade, Xi'an Eurasia University, Xi'an, China)
Copyright: © 2017 |Pages: 12
DOI: 10.4018/IJSDS.2017010101
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Travel time variability is a random phenomenon, and within the presence of it, uncertainty is associated with decision making. When a choice is made in an uncertain situation, the probability distribution is based on the subjective judgments of a decision maker. This paper introduces a psychological perspective to the concept of travel time variability, by embedding a belief-based weighting, so as to better understand decision making under uncertainty. This research argues that a subjective probability approach accounting for degrees of belief should be addressed in order to capture the impact of travel time variability on decision making. Using a simulated choice data set, the author provides an example of modelling uncertainty aversion, and illustrate its impacts on model performance.
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Some Comments On Previous Travel Time Variability Studies

This progress of MEU has led to changes the specification of a utility function that incorporates travel time variability, and also leads to significant innovations in the way that stated choice (SC) experiments have to be designed to capture travel time variability. In recognition that travel time does vary, a series of arrival times, rather than the extent and frequency of delay, have been considered in recent SC experiments (see, e.g., Senna, 1994; Noland and Small, 1995; Small et al., 1999; Hollander, 2006; Asensio and Matas, 2008; Batley and Ibáñez, 2009). However, in stated preference studies not established on RUM, travel time variability is typically presented as the extent and frequency of delay relative to ‘normal’ travel time (see e.g., Jackson and Jucker, 1982; Small et al., 2005).

In terms of the modelling framework, the mean-variance model and the scheduling model are two dominant approaches in the transport literature; while most stated preference (SP) experiments are similar to Small et al. (1999) (see Table 1) with some slight changes (e.g., some used vertical bars to represent travel times (e.g., Batley & Ibáñez, 2009); some provided 10 travel times instead of five (see e.g., Bates et al. 2001; and some show the departure time explicitly to the respondents (e.g., Holland, 2006). The behavioural paradigm widely used in the MEU model is a mix of Random Utility Maximisation (RUM) and Expected Utility Theory (EUT) (i.e., a linear utility specification with linear probability weighting).

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