If people repeat choices in daily travel behaviour under stable conditions, over time this can form habitual travel behaviour and lead to inertia. If travellers face a new metro line, to what extent does their habitual travel behaviour affect their willingness to use this new transport alternative. This study investigate the inertia effects in a multimodal public transport network using a fully revealed preference data set. Habitual route choice behaviour is quantified prior to the opening of the new metro line. We then use the quantitative metric for habitual behaviour as an indicator in the situation after the metro line opens to reflect the inertia effects of individuals. The outcome of the route discrete choice model reveal that the inertia effects for the new metro line are significant. The interaction effect of habitual travellers with the new transport alternative show that there is an additional effect for the new alternative if travellers are classified as habitual. Furthermore, not accounting for inertia effects leads to bias in travel demand estimations of the new transport alternative. The model with the inertia effect improves model fit and is able to more accurately.

The Dutch Ministry of Infrastructure and Water Management is committed to enhancing the accessibility and sustainability of the Dutch mobility system. Working from home (WFH) has been discussed as a policy lever to decrease commute travel and, thereby, congestion for decades. During the COVID-19 pandemic, this discussion regained momentum. WFH was a key factor for the decrease in commute travel by public transport and car in the Netherlands, which decreased congestion. Against this background, the Ministry wants to stimulate WFH post-pandemic to retain some of the sustainability and accessibility benefits identified during the pandemic. WFH can enhance the accessibility of the mobility system thanks to reduced commute trips during peak hours.
Although the stimulation of WFH may be a policy lever to increase the accessibility of the mobility system, the full effect of WFH on overall personal mobility is unknown. Studying pre-, during and post-COVID-19 measures data, this study assesses the impact of a change in WFH on activity-travel patterns. Commute travel, non-work travel, and mode use together define activity-travel patterns in this research. The main research question is as follows: How did changes in WFH influence activity-travel patterns during the pandemic in the Netherlands? To answer this question, data from the Netherlands Mobility Panel (MPN), collected between 2019-2022 is used. Latent class analysis is used to identify seven distinct activity-travel patterns. Next, two latent transition models are estimated. The first model, for 2019-2021, studies the effect of an increase in WFH on transitions in activity-travel patterns. The second model, for 2021-2022, studies the effect of a decrease in WFH on transitions in activity-travel patterns.

The application of LTA, a longitudinal clustering method, sheds light on complex transition behaviour in the population during- and post-COVID-19 measures. The results show that activity-travel patterns before a change in WFH and the level of an increase in WFH matter for the impact of WFH on transitions. Depending on the initial class membership, differences in transitions emerged. To a certain extent, all effects of WFH on travel, substitution, complementarity, modification, and neutrality exist in the population, but these depend on initial class membership. The results for an increase in WFH are significant, while results for a decrease are not. For the time being, these findings do not exclude the existence of structural changes in working and travel behaviour. Significant effects on transitions appear for a small and a large increase in WFH. After an increase in WFH, most classes are more prone to take up another activity-travel pattern. In both cases, most classes have an increased probability of transitioning towards the low mobility class, which underpins more sustainable patterns. Nonetheless, more diverse results appear for a small increase in WFH, and significant transitions towards other car profiles appear. Analysing the impact of a small increase on class sizes confirms this. Overall, the car class sizes remain relatively high after a small increase in WFH. Thus, an increase by 1-3 days working from home may not lead to more sustainable activity-travel patterns in terms of trip rates per purpose and mode. Nevertheless, it needs to be kept in mind that this study only analysed trip rates per purpose and mode, a small increase in WFH may still lead to a peak-travel reduction, which benefits accessibility. Further analyses should investigate peak-travel to conclude on the accessibility benefits of small increases in WFH. At the same time, governments and employers should take measures to counter-act low mobility associated with WFH which is detrimental for physical health.

Autonomous vehicles are expected to become an inevitable reality in the coming years, as their uncovered benefits are increasingly proven to be positive, such as increased safety and traffic efficiency. However, their impacts on travel choices have often been represented with an assumed reduction in the travel time penalty, which oversimplified AVs' subsequent effects activity-travel scheduling choices. As such, models and assessment methods do not accurately represent travel behavior implications of autonomous vehicles. Thus, using survey data, this thesis identifies possible rearrangements in activity-travel schedules, classifies the respondents into classes with similar profiles of expected rearrangements, and identifies further classification on the basis of socio-economic, personal, and travel characteristics. The survey to be used is one in which respondents were asked to report a full, regular working day activity schedule using their currently preferred mode of transport, then report the schedule as they expect it to be if they could use an autonomous vehicle. The initial exploration of the data identified the occurrence of activities on-board (work, spare-time, meals, and getting ready), changes to the duration of activities outside travel, as well as travel (delay of work-bound trips, and advancement of home-bond trips). Next, we used latent class models to cluster the responses with respect to on-board activity duration changes, stationary activity duration changes, and travel departure time changes. The clustering uncovered types of classes: no change, single activity on-board (work and spare-time), multiple activities on-board. Interactions between stationary and on-board activities were identified, with some direct activity transfer to travel episodes being common (with work, meals, and getting ready), while other activities were generally not transferred (spare-time). Finally, the addition of personal characteristics and demographics highlighted the limited influence of socio-economic factors, with the exception of education, on activity changes. In contrast, the most significant factors were mostly associated with work (daily time pressure and the ability to do work in the car) and travel time duration. An important insight uncovered was that the travel changes were limited and not as dramatic as expected, highlighting that the value of time impacts alone are not as representative as expected.