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• A large amount of time commuting, it is important we understand how that time is valued.
• We explore values in three contexts: individual WTA, individual when partner affect and.
• Hierarchical Bayes analysis is used to provide parsimonious decomposition of WTA values.
• Rich insights into how people value commuting time are revealed allowing a better understanding.
This paper reports on an analysis aiming to understand differences across individual people in their willingness to accept increased commuting time in return for higher salary, using Hierarchical Bayes (HB) analysis of a dataset collected in Sweden. We find that socio-demographic and attitudinal differences are significant in explaining the variations in values of time for individuals, in particular income, who drives when carpooling and hours worked per week. Additionally we also examine the values of individuals when their choices also impact on the salary and commute of their partner, finding that incomes, income differentials, driving behaviour when carpooling, division of housework and car user decisions significantly explain the values assigned to others and variations in an individual’s own values once their partner is affected. The overall richness of the results reflect the benefits that posterior analysis can bring, and highlight the computational efficiency of Bayesian methods in producing such conditionals at an individual level.
Journal: Transportation Research Part A: Policy and Practice - Volume 91, September 2016, Pages 1–16