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How does the extent of automobile use affect the level of satisfaction that people derive from their daily travel routine, after controlling for many other attributes including socio-economic and demographic characteristics, attitudinal factors, and lifestyle proclivities and preferences? This is the research question addressed by this paper. In this study, data collected from four automobile-dominated metropolitan regions in the United States (Phoenix, Austin, Atlanta, and Tampa) are used to assess the impact of the amount of driving that individuals undertake on the level of satisfaction that they derive from their daily travel routine. This research effort recognizes the presence of endogeneity when modeling multiple behavioral phenomena of interest and the role that latent attitudinal constructs reflecting lifestyle preferences play in shaping the association between behavioral mobility choices and degree of satisfaction. The model is estimated using the generalized heterogeneous data model (GHDM) methodology. Results show that latent attitudinal factors representing an environmentally friendly lifestyle, a proclivity toward car ownership and driving, and a desire to live close to transit and in diverse land use patterns affect the relative frequency of auto-driving mode use for non-commute trips and level of satisfaction with daily travel routine. Additionally, the amount of driving positively affects satisfaction with daily travel routine, implying that bringing about mode shifts toward more sustainable alternatives remains a formidable challenge—particularly in automobile-centric contexts.

This paper describes and models the behavioral response to the COVID-19 pandemic in Switzerland. The MOBIS-COVID GPS tracking dataset, which includes a pre-pandemic reference base, is used. Trip-level data are transformed in weekly distance proportions per mode per week, and the data are modeled using a mixed multiple discrete-continuous extreme value (MMDCEV) model. Four distinct segments are derived, from September 2019 until the end of 2020, and used to uncover natural and forced behavioral adaptations. The descriptive and model estimation results confirm the trends partly observed around the globe, that is, a large decrease in public transport usage, recovered car usage, and a cycling boom. Behavioral insights are further provided as well as policy recommendations.

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