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• 2014 and 2016 waves of Netherlands Mobility Panel examined. • Latent transition model estimated and latent transition probabilities used. • Latent classes can be distinguished within car-user groups. • Travellers' attitudes towards car use and ownership are very stable over time. • In most cases, changing car attitudes do not directly affect car use. This paper examines how changes in travellers' attitudes towards car use and ownership change over time and how these changes influence car use, based on the 2014 and 2016 waves of the Netherlands Mobility Panel (N = 1640). A latent transition model was estimated to investigate transitions between different segments based on travellers' attitudes towards car use and ownership, and the latent transition probabilities were used to predict changes in mode use. Four latent classes were found to reflect the participants' attitudes: cost-sensitive, car-minded, environmentally aware and social-conscious travellers. Most of the participants remained in the same class between 2014 and 2016, which suggests that attitudes towards car use and ownership are stable over time. Also, the results indicate that car use and car ownership may be less widespread among younger adults. Only when younger adults face life events, such as moving, starting a job or become parents, transitioning to more car-oriented profiles appears more likely. Changes in attitudes towards car use and car ownership do not significantly affect car use (number of trips per day), except for the social-conscious travellers who switched to the car-minded class. This suggests that, in most cases, a more positive or negative attitude towards car use and ownership does not directly affect the frequency of car use. [ABSTRACT FROM AUTHOR]

While most studies on mode choice behaviour and households are typically based on individual travel behaviour decisions, less is known about how relations inside households affect mode choice. This paper addresses this topic by examining intra- and inter-household variation in car use. The decision to use the car is modelled for home-based tours, based on data from the 2013 and 2014 waves of the Netherlands Mobility Panel. A multilevel framework is used to investigate mode choice behaviour at tour, individual and household level to account for the impact of individual and household characteristics on travel mode choice, interdependencies of individuals within their households and variation in individual travel mode choice and other characteristics over time. The results show that variability between households and individuals accounts for more than one third of the total variation in the mode choice of home-based tours. In dual-income households, intra-household interactions have a larger effect on car use than inter-household interactions. Although only two panel waves are used, the model results show significant time effects on mode choice: if the same tour was also conducted in the previous year and one person changed working hours or work location, car use is less likely. [ABSTRACT FROM AUTHOR]

This paper examines the influence of panel attrition on the intrapersonal dynamics in self-reported trip rates, using the data from the 2013, 2014 and 2015 waves of the Netherlands Mobility Panel, a large scale household panel. A hybrid choice model (HCM) was developed to simultaneously model the effect of socioeconomic, infrastructure and land use variables, life events and non-random attrition on trip rates, whereby the latent variable (LV) model is composed of panel attrition and survey completeness. The discrete choice model (DCM) includes four trip rate categories, including zero trips. The probability of each trip rate category was estimated for both the HCM and the DCM models; with and without the LV model. The first main conclusion from this paper is that the largest bias due to panel attrition occurs in the probability of reporting no trips per day, and 1–2 trips per day. Also, the HCM models show a correlation between the probability of reporting no trips per day and the tendency to drop out altogether. The second main conclusion is that the results show that the latent variables (attrition and completeness) are statistically significant in estimating mobility. Also, socioeconomic variables (gender, driving license, household type and size), mode preferences, spatial infrastructure and life events determine mobility rates and remain significant after adding attrition/completeness variables. Thirdly, the results proved that attrition effects significantly vary across waves. [ABSTRACT FROM AUTHOR]