Modeling experienced accessibility for utility-maximizers and regret-minimizers

This paper argues that there is a discrepancy between what Logsum-measures of accessibility aim to measure (experienced-utility) and what they actually measure (decision-utility). The latter type of utility refers to the evaluation of an alternative with the aim of making a decision, while the former refers to the evaluation of a chosen alternative after the choice has been made. We argue that accessibility should preferably be conceptualized and operationalized in terms of experienced-utility, but that this type of utility is difficult to measure. Motivated by these observations we show, taking the Logsum as a starting point, how its building blocks (parameters estimated from choice patterns) can be used to construct closed-form and easy to compute accessibility-measures that provide an approximation of experienced-utility. We distinguish between decision-making based on utility-maximization and regret-minimization premises. Using a small-scale case-study building on departure time-choice data, we illustrate the working of the developed accessibility-measures and highlight how they differ from the Logsum-approach.

► Logsum-based measures of accessibility measure decision-utility, but should ideally measure experienced utility.
► Paper presents alternatives that approximate experienced utility by allowing for preference and evaluation-rule volatility.
► Numerical analyses suggest that the different accessibility measures may imply different land use/transport strategies.